With the rapid development of urban economy and the acceleration of urbanization, the demand for urban traffic is increasing rapidly. The single traffic-oriented planning does not take into account the requirements of traffic development on resources and the impact on the environment. The traffic construction of most cities can not fully meet the standard of ecotype. In this paper, the vehicle distribution route optimization problem under multiresource constraints such as vehicle energy capacity and vehicle loading capacity is studied, and (...) the static and dynamic models of distribution route optimization under multiresource constraints are established. In the static distribution route optimization model, the distribution route optimization problem with subloops is solved by modifying the network structure and adding virtual resource points. In the dynamic model, the spatiotemporal network model is used to avoid the generation of subloops, and the description of the vehicle distribution route planning problem is more intuitive and accurate. The model enriches the vehicle distribution route selection scheme at the cost of expanding the model scale. And it can solve the time when the vehicle arrives and leaves the customer point. This paper provides a good countermeasure for solving the environmental and social problems in the transportation system under the condition of resource constraints. (shrink)

The book consists of 6 chapters. Chapter One explains the reason why SLA researchers should study the language learning environment in space: population movements associated with internal and external migration and social mobility such as the circuits of commodity production and distribution create much space, in which language learning environment become diverse and uneven. With the spatial perspective, we can fully understand the interactions between language learners and the world or environments.In Chapter Two, by introducing the brief history of (...) Critical Spatial Theory, the author wants to tell us one of the key ideas in the theory: the social production of space, which views space as the product of the social relations of its era. Based on it, the author adds that the production of languages is an integral aspect of the production of space on a global scale. It can help us to understand how social mobility shapes language learning environments. From this perspective, the local environments in which languages are learned are outcomes of the production of space under the influence of mobility. It reiterates the key view that the global mobilities of physical objects (people, goods and information) produce space.In Chapter Three, the author introduces the mainstream views held by modern linguistics: languages are conceptualized as objects in the space. They hold that the spatiality of language is given by providing correct writing systems and standardized rules for various languages. Influenced by mainstream conception of language from modern linguistics, SLA tends to regard languages as self-contained objects in space. However, this view neglects the relation between language and the learning environments. The author holds that the distribution and circulation of languages and their varieties in geographical space produce the spatiality of language, which clears the ground for an environment or object-as-space view of language in Chapter Four.In Chapter Four, the author develops an alternative "objects-as-space" view and introduces three concepts to illustrate spatiality of language: flat ontology, assemblage and mobilities. The concept of flat ontology helps explain what language is and how it comes into being in the world; From assemblage perspective, the author develops the idea that everything is an object, so are human beings and languages. Language (the non-physical object) interacts with various physical objects, forming the language-bearing assemblage. This assemblage is the form of spatiality of language. The mobilities theory explains how language-bearing assemblages develop or change on a global scale because of social mobility, emphasizing the importance of learning environment in SLA. All in all, the author outlines how language makes its way into physical space in the form of assemblages, or assemblages of assemblages.Chapter Five centers on language learning environment. First, the author explains the relation between language learners and their learning environment from an ecological view: the learner is a part of the language learning environment, and second language learning is a spatial interaction between the learners and the semiotic resources provided by environment. Then, the author discusses language environment from two different perspectives. From an 'individual' perspective, environments are assembled by individuals from the spatial resources available; from an 'areal' view, environments are shaped both by local circumstances and global circulations of language-bearing assemblages. Meanwhile, the author also discusses other language environments like the multilingual city and online resources. What the author hopes to develop in this chapter is an environment perspective that grounds the contexts of language learning in space, which helps the readers think about language learning from a spatial perspective.Chapter Six contains three parts. In the first part, it outlines the work of a number of researchers from sociolinguistics and the contribution they made to the spatial theory. In the second part, the author wants to tell us how the idea of language learning environment emerges from his own research. The last part discusses the four main researching routes (areal studies, studies of individuals learning in settings, studies of the construction of individual learning environment and design-based studies) that might influence SLA research on language learning environments and some research methods. The book is in alignment with several recent "turns" occurring in the field of SLA, such as "social turn" (Block, 2003) and "multilingual turn" (May, 2014), both of which bring to the fore the social aspects involved in language learning. While the first "turn" accentuates the role of social context in shaping and influencing learner cognition, the latter highlights how the physical multilingual world remold language learning experiences, and how the mental multilingual world helps raise learners' language awareness, and facilitates their successful construction of an identity of legitimate multi-language users. The book under review joins up the two "turns" in acknowledging that language learners are persons-in-context with organic interaction with social learning environment (Ushioda, 2021). This view has research and pedagogical implications. From the point of doing research, spatial perspectives, advocated in the book, afford huge research potentials for both language teachers and (instructed) SLA researchers to conduct situated investigations, exploring the complex and dynamic nexus between language learners and their surrounding environment (not restricted to people) that play a part in their learning experience, and thus gaining insightful understanding of how individuals and learning contexts are coupled with each other, how they evolve together, and how they constitute an ecosystem that generates affordances, facilitating or constraining language learning. Pedagogically speaking, the spatial perspectives encourage language teachers to hold an ecological view of language teaching and learning, directing their attention to the individuality of each language learner and their complex, dynamic and multifaceted relationality with other learners and the physical realities, rather than solely focusing on the abstract systems (e.g., the development of cognitive aspect). This holistic view allows language teachers to take measures to address concrete questions emerging in and out of the classrooms, and stimulates them to design and experiment with pedagogical interventions that help to create a favorable environment for language learning. This view also invites language teachers to take learner agency into account (Xu & Long, 2020) and to design pedagogical activities that enable learners to take agentive actions to adapt their environments as they actively create and seek their own language learning opportunities, making the world their own (Bronfenbrenner, 1979). In turn, teachers' agency-enabling activities will ultimately help learners to autonomously access a range of resources in this new affordance-rich era to improve their language skills, and thus become more independent in their pathway to life-long learning.Beside the above benefits, the book is also theoretically and methodologically friendly. In illustrating the role of space in SLA, the author invokes a plethora of theories from such disciplines as sociology, sociolinguistics, psychology, etc. This orientation helps readers to build a transdisciplinary framework to capture the multi-layered nature of SLA, and to acquire a nuanced understanding of how language learning is influenced by multiple scales of environmental factors. Methodologically, the author proposes several specific methods that are especially apt to investigate language learning environment. These methods, together with the theoretical foundation laid by the book, will definitely help researchers and language teaching practitioners to further delve into the complex interplay between context and language learners.Admittedly, the book is not without weaknesses. It spends most of its words on illustrating the importance of physical environment in SLA. Another essential component, the virtual environment is insufficiently referenced and discussed. This lacking is undoubtedly a regret the book leaves with us, especially in these years of pandemic, where language learning in virtual spaces is becoming a norm.All in all, the book is an invaluable contribution to the field of SLA, and is worthy of recommending to language teaching practitioners, researchers, and postgraduates in applied linguistics. (shrink)

E-commerce conceivable future trade, consumption, and service is a new digital employer mode. Therefore, in order to decorate the customary natural environment of operation, it is quintessential to get rid of the preferred desktop in the true field, create a social logistics and transportation administration computing device with commodity agents and distributors as the key features, and mix freight logistics, business enterprise approach waft and data waft advertising and marketing, and advertising and marketing organically. The notion of e-commerce logistics looks (...) alongside the enchantment vogue of neighborhood technological knowledge and social demand. It is an integral part of ending e-commerce authentic economic value. All kinds of cell networks clear up the problem of data flow communication; in the technological know-how of telephone network, digital cost solves the bother of capital flow; at present, logistics is however a long-standing problem of e-commerce. Logistics has grown to be the last and entirely bottleneck of e-commerce. If logistics cannot adapt to the enchantment of e-commerce, it may also additionally lead to the slow enchantment of e-commerce. The purpose of this paper is to supply an Internet of things e-commerce logistics cloud provider platform based totally absolutely on cell communication, which can be combined with e-commerce and beautify the transport tempo of e-commerce. This paper moreover optimizes the e-commerce logistics system, so that it can combine with e-commerce to increase out the distribution of goods, the distribution tempo of e-commerce is doubled, and e-commerce can in a similar fashion develop rapidly. (shrink)

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem. (...) Based on likelihood robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization. (shrink)

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient (...) class='Hi'>resource management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines into two pools: a hot pool and a warm pool. Each PM is configured with a resource search engine that finds an available virtual machine for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm is presented to minimize the system cost and optimize the sleep parameter. (shrink)

Power provision is coming to be the most important constraint to data center development. The efficient management of power consumption according to the loads of the data center is urgent. As the load for every application hosted in every server node of the data center and corresponding Service Level Agreement requirements can be quite different, it is hard to deploy a power strategy at application. The asynchronies and abruptness characteristics of workload fluctuation make power management policymaking using periodic (...) class='Hi'>resource scheduling method invalid. In this article, the design and implementation of the request–response distributed power management scheme is elaborated. Bound by linear time complexity, the method proposed integrates dynamic voltage/frequency scaling, power-on–power-off, and virtual machine migration mechanisms and dynamically optimizes the power consumption of a cloud data center. The significant advantage of the scheme is that it does not need synchronous scheduling between all SNs. Simulation results showed that the scheme could effectively decrease the power consumption of the data center, with a tiny reduction in performance as centralized control methods. (shrink)

The emergence of new technologies such as the Internet of things and the Cloud transforms the way we interact. Whether it be human to human interaction or human to machine interaction, the size of the networks keeps growing. As the networks get more complex nowadays with many interconnected components, it is necessary to develop distributed scalable algorithms so as to minimize the computation required in decision making in such large-scale systems. In this paper, we consider a setup where each (...) agent in the network updates its opinion by relying on its neighbors’ opinions. The information exchange between the agents is assumed to be mutual. The cluster consensus problem is investigated for networks represented by static or time-varying graphs. Joint and integral connectivity conditions are utilized to determine the number of clusters that are formed, as the interactions among the agents evolve over time. Finally, some numerical examples are given to illustrate the theoretical results. (shrink)

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals’ and collectives’ freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests (...) often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human–machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies. (shrink)

Over the last decade, multi-agent systems have come to form one of the key tech- nologies for software development. The Formal Approaches to Multi-Agent Systems (FAMAS) workshop series brings together researchers from the fields of logic, theoreti- cal computer science and multi-agent systems in order to discuss formal techniques for specifying and verifying multi-agent systems. FAMAS addresses the issues of logics for multi-agent systems, formal methods for verification, for example model (...) check- ing, and formal approaches to cooperation, multi-agent planning, communication, coordination, negotiation, games, and reasoning under uncertainty in a distributed environment. In 2007, the third FAMAS workshop, FAMAS'007, was one of the agent workshops gathered together under the umbrella of Multi-Agent Logics, Languages, and Organ- isations - Federated Workshops, MALLOW'007, taking place from 3 to 7 September 2007 in Durham. This current special issue of the Logic Journal of the IGPL gathers together the revised and updated versions of the five best FAMAS'007 contributions. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Commentary on “Towards a Design-Based Analysis of Emotional Episodes”Cristiano Castelfranchi (bio) and Maria Miceli (bio)Keywordsgrief, suffering, attachment, agent architectureThis paper is significant in many respects: its approach (the design-based analysis); its proposed architecture; its description of grief; and its self-control/perturbance theory. We would offer some remarks on each of these aspects.AI: Back to the FutureAfter some years of crisis, AI seems now to have recovered its original challenging (...) attitude and its identity. In particular all the original ambitions of AI have been renewed under the label of “autonomous agents” (see, for example, Russell and Norvig 1995; Wooldridge and Jennings 1995). This paper and its authors are good representatives of this challenging attitude. Of course, the AI appearing at century’s end is different from, say, the AI of the 1980s. AI systems and models are now conceived for reasoning and acting in open unpredictable worlds, with limited and uncertain knowledge, in real time, with bounded (both cognitive and material) resources, with hybrid architectures, interfering—either co-operatively or competitively—with other systems. The new password for the AI of the 1990s is interaction (Bobrow 1991): interaction with an evolving environment; among several, distributed and heterogeneous artificial systems in a network; with human users; and among humans through computers.This renewed AI, however different though it is in some ways, can be understood as returning to its original aim of modeling “intelligence”: not only static and isolated sub-functions of intelligence but the integrated and pragmatic functioning of autonomous agent architectures as against algorithms. Our renewed AI, based on architectures, can again have important exchanges with other (psychological and social) sciences. This paper is a good example of the fruitful cross-fertilization of ideas which may result from such exchanges. It was indeed just this which originally gave rise to cognitive science.The ArchitectureThe proposed architecture, although quite speculative, and—as the authors themselves emphasize—very much a first sketch, is directly relevant to this new AI. In fact, recent research efforts in the agent-architecture domain are focused exactly on the problem of “goal dynamics” [End Page 129] or “motive processing,” which is the core of this architecture, and on the problem of integrating reactivity and deliberation. Compared with current AI architectures (for example, BDI systems) it is important to note that this “motive-processing” architecture is not yet formalized or implemented to the same extent. On the other hand, it is more interesting from a psychological point of view, and is already able to give us some important insights into human emotions, in particular those emotions (like grief) that involve all or total loss of control of management processes.The Report-Based Analysis of GriefThe report-based description of grief provides a persuasive and rich basis for both modeling and discussing the model. The relevant features of this emotion are well characterized with reference to the self-control problem and motive-processing (though, as we shall see, to use grief as an example of those emotions that involve all or total loss of control of thought processes may provide a partially distorted view of grief itself). In particular, we would stress the relevance of the “intrusive” character of grief-related contents; the rejection of certain beliefs due to the psychic pain they cause; the coping strategies people describe; and the second order motivations related to the control of the emotional state. We believe that psychological models especially of these aspects are usually less specific and explanatory.Let us now try to synthesize some criticisms one could advance of this analysis. We will first address two core notions of the model—perturbance and self-control—and then consider more specifically the treatment of grief.PerturbanceThis notion is potentially very interesting, unless it is reduced to an information processing perspective (e.g., perturbance of attentive processes). An information processing perspective is much more limited than a motive-processing one—at least in the limitative sense of the term, as it is generally used in the psychology of cognitive processes—and is in our view insufficient for understanding human emotions and their role. This limiting interpretation of perturbance fails... (shrink)

Distributed Artificial Intelligence (DAI) deals with computational systems where several intelligent components interact in a common environment. This paper is aimed at pointing out and fostering the exchange between DAI and cognitive and social science in order to deal with the issues of interaction, and in particular with the reasons and possible strategies for social behaviour in multi-agent interaction is also described which is motivated by requirements of cognitive plausibility and grounded the notions of power, dependence and help. (...) Connections with human-computer interaction are also suggested. (shrink)

This chapter takes inspiration from Wittgenstein’s thinking to formulate a non-reductive toolbox for the study of religion associated with generative modelling, specifically as applied in complex adaptive systems theory. It converges on a communal perspective on religion as multiscale active inference that contrasts starkly with common ‘straw person’ perspectives on religion that reduce it to ‘erroneous’ theorising generated by the brain. In contrast, we argue, religious practices at the enculturated level of description involve implicit and explicit meanings, experienced both (...) individually and collectively. Firstly, we characterise these in terms of Wittgenstein’s notions of rule-following and language games. Secondly, we discuss how the collective and perspectival aspects of religion in enculturation with morals, doctrines, rituals, and expressions can be formalised in terms of deep active inference in multi-agent systems. This approach describes how religions are incommensurable with scientific methods due to the epistemic separation between the different kinds of language games of religious and scientific practices. Their touchpoints tend to give rise to perpetual confusion and unproductive discussions, requiring perspicuous presentations that can be formulated with our toolbox. In this sense, our account has implications for tackling complex societal challenges such as ethical policy-making. (shrink)

Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI’s impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter (...) this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI—namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments. (shrink)

Multiuser fair sharing of clusters is a classic problem in cluster construction. However, the cluster computing system for hybrid big data applications has the characteristics of heterogeneous requirements, which makes more and more cluster resource managers support fine-grained multidimensional learning resource management. In this context, it is oriented to multiusers of multidimensional learning resources. Shared clusters have become a new topic. A single consideration of a fair-shared cluster will result in a huge waste of resources in the context (...) of discrete and dynamic resource allocation. Fairness and efficiency of cluster resource sharing for multidimensional learning resources are equally important. This paper studies big data processing technology and representative systems and analyzes multidimensional analysis and performance optimization technology. This article discusses the importance of discrete multidimensional learning resource allocation optimization in dynamic scenarios. At the same time, in view of the fact that most of the resources of the big data application cluster system are supplied to large jobs that account for a small proportion of job submissions, while the small jobs that account for a large proportion only use the characteristics of a small part of the system’s resources, the expected residual multidimensionality of large-scale work is proposed. The server with the least learning resources is allocated first, and only fair strategies are considered for small assignments. The topic index is distributed and stored on the system to realize the parallel processing of search to improve the efficiency of search processing. The effectiveness of RDIBT is verified through experimental simulation. The results show that RDIBT has higher performance than LSII index technology in index creation speed and search response speed. In addition, RDIBT can also ensure the scalability of the index system. (shrink)

The behaviour of a multi-agent system is driven by messaging. Usually, there is no central dispatcher and each autonomous agent, though resource-bounded, can make less or more rational decisions to meet its own and collective goals. To this end, however, agents must communicate with their fellow agents and account for the signals from their environment. Moreover, in the dynamic, permanently changing world, agents’ behaviour, i.e. their activities, must also be dynamic. By communicating with other (...) fellow agents and with their environment, agents should be able to learn new concepts and enrich their knowledge base. Processes and events that happened in the past may be irrelevant in the present or have a significant impact in the future, and vice versa. Therefore, the fine-grained analysis of agents’ activities as well as events within or beyond the system is very important so that the system can run smoothly without falling into inconsistencies. Moreover, as the system should communicate with its environment, the analysis should be as close to natural language as possible. The goal of this paper is a proposal for such an analysis. To this end, I apply Transparent Intensional Logic (TIL) because TIL is particularly apt for a fine-grained analysis of processes and events specified in the present, past or future tense with reference to the time when they happened, happen or will happen. (shrink)

Narrow views of systemic sustainability risks can result in ecological concerns being neglected, as well as giving rise to unequal distribution and exploitation of natural resources, creating injustice. Given recent advancements in integrating justice with the safe space environmentally, as defined by the planetary boundaries, now is a critical moment for business ethics researchers to deepen the conversation on managing systemic sustainability risks to create a safe and just operating space. We argue that the social-ecological systems approach, that views (...) humans as embedded in and dependent on the natural environment, provides a foundation for business ethics scholars to deepen the conversation. We build on two key dimensions from the social-ecological systems approach, social-ecological connectivity and cross-scale dynamics to develop a framework that illustrates four ways for business ethics scholars to research systemic sustainability risks. We demonstrate the relevance of these dimensions for understanding systemic sustainability risks, discuss their ethical implications, and outline opportunities for future research. (shrink)

An agent organization system for multi-agent based network management, called ARTISTE, is proposed. For a multi-agent system to solve problems effectively, it is important to organize agents appropriately. Organizing agents adaptively on the Internet, however, is not easy, because the status of the Internet changes dynamically in a short time and no one can have a complete view of the whole network. The aims of ARTISTE are to form an agent organization in accordance with (...) the current Internet and its problem-solving context and to provide organizational information for target MASs. ARTISTE operates as an independent system with respect to any MAS. To organize agents, ARTISTE collects information about agents' abilities and statuses, network information such as topologies, and a problem-dependent requirement from a target MAS. ARTISTE is designed as an MAS, and it can collect the information about the network and the target MAS from multiple observation points. Furthermore, ARTISTE agents exchange their own local information in order to create a more global view of the network and the distribution of the agents. Our performance evaluation showed that ARTISTE works with sufficient performance for use in the actual Internet environment. (shrink)

A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating (...) class='Hi'>cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method. (shrink)

This paper describes the escape/intervention concept as it is used in the agent growing environment framework. The Escape and Intervention is used in many multi-disciplinary areas, including agent research, artificial intelligence, groupware and workflow, process support, software engineering, and social sciences. Based on an ontological perspective, this paper explains how an interaction-oriented agent architecture and language (used for modelling, simulation, and development) makes use of an interaction pattern that is inspired from social contexts seen as (...) class='Hi'>multi-agent systems. (shrink)

In this paper, we study the evolution of knowledge in multi-agent conformant planning over transition systems. We propose a dynamic epistemic logical framework with modalities of distributed knowledge to handle the epistemic reasoning in such scenarios, and we reduce a problem of multi-agent conformant planning to a model checking problem. We prove that multi-agent conformant planning is Pspace-complete on the size of the dynamic epistemic model.

In this paper, we investigate the use of event models for automated planning. Event models are the action defining structures used to define a semantics for dynamic epistemic logic. Using event models, two issues in planning can be addressed: Partial observability of the environment and knowledge. In planning, partial observability gives rise to an uncertainty about the world. For single-agent domains, this uncertainty can come from incomplete knowledge of the starting situation and from the nondeterminism of actions. In (...) multi-agent domains, an additional uncertainty arises from the fact that other agents can act in the world, causing changes that are not instigated by the agent itself. For an agent to successfully construct and execute plans in an uncertain environment, the most widely used formalism in the literature on automated planning is “belief states”: sets of different alternatives for the current state of the world. Epistemic logic is a significantly more expressive and theoretically better founded method for representing knowledge and ignorance about the world. Further, epistemic logic allows for planning according to the knowledge (and iterated knowledge) of other agents, allowing the specification of a more complex class of planning domains, than those simply concerned with simple facts about the world. We show how to model multi-agent planning problems using Kripke-models for representing world states, and event models for representing actions. Our mechanism makes use of slight modifications to these concepts, in order to model the internal view of agents, rather than that of an external observer. We define a type of planning domain called epistemic planning domains, a generalisation of classical planning domains, and show how epistemic planning can successfully deal with partial observability, nondeterminism, knowledge and multiple agents. Finally, we show epistemic planning to be decidable in the single-agent case, but only semi-decidable in the multi-agent case. (shrink)

This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman (...) Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem. (shrink)

In a multiagent and multi-cultural world, the fine-grained analysis of agents’ dynamic behaviour, i.e. of their activities, is essential. Dynamic activities are actions that are characterized by an agent who executes the action and by other participants of the action. Wh-questions on the participants of the actions pose a difficult particular challenge because the variability of the types of possible answers to such questions is huge. To deal with the problem, we propose the analysis and classification (...) of Wh-questions apt for agents’ communication in a multiagent system (MAS). Our proposal of such a system consists of agents who communicate with their fellow agents by messaging so that each autonomous agent, though resource-bounded, can make less or more rational decisions to meet its own and collective goals. In addition, by communicating with other fellow agents and their environment, agents can learn new concepts and enrich their ontology so that their behaviour is dynamic. We aim to make a general proposal of the system so that the ‘envelope’ of agents’ messages can be formalized in any MAS standard, be it The Foundation for Intelligent Physical Agents - Agent Communication Language (FIPA-ACL) or Knowledge Query and Manipulation Language (KQML). Yet, the content of messages is encoded in a formalized natural language. To this end, we apply Transparent Intensional Logic (TIL) with its procedural semantics which is particularly apt for a fine-grained analysis in which all the semantically salient features of natural language can be plausibly formalized. In this paper, we concentrate on analysing the content of query messages, particularly the content of those that encode Wh-questions and the answers to them. We also summarize TIL deduction system that makes it possible to answer such questions in an intelligent way. Linguists distinguish several subtypes of Wh-questions. Though the linguistic classification is helpful, it is not always suitable for agents’ communication and reasoning. We need the classification based on a logical analysis of Wh-questions so that the agents can infer possible answers to such questions rather than only looking for them by keywords. This paper aims to apply an appropriate classification of the logical types of Wh-questions and the analysis of such questions; we concentrate in particular on questions concerning the participants of activities. The application of these results to the analysis of processes and events based on verb valency frames is another novelty of the paper. (shrink)

The machines exhibit an intelligence which is artificial intelligence, and it is the design of intelligent agents. A system is represented by an intelligent agent who perceives its environment and the success rate is maximized by taking the action. The AI research is highly specialized and there are two subfields and each communication fails often. The popular AI approaches include the traditional symbolic AI and computational intelligence. In order to optimize the seismic design of the reinforced concrete pier structure, (...) the particle swarm optimization algorithm and the reaction spectrum analysis method are combined; they establish a regular bridge of the design variable with cross-sectional characteristics and reinforcement ratios, with the target function. The seismic optimization design framework of the pier is transformed into a multi-objective optimization problem. Calculations show that the method can quickly obtain the optimal design parameters that meet multi-objective requirements. The improved PSO main program and the calling push-over program run time are 4.32 and 1347.56 s, respectively; the push-over program running time is 99.68% of the run time of the total program. Optimization of the seismic performance of the rear bridge pier is significantly improved and is more in line with the design method; the design method proposed in this article is more practical. (shrink)

The new Hungarian Labour Code allows informal payments to be accepted, subject only to the prior permission of the employer. In Hungary, the area most affected is Health Care, where informal payments to medical staff are common. The article assesses the practice on ethical terms, focusing on organisational justice. It includes an analysis of distributional injustice, that is, of non-equitable payments to professionals, on the distribution of payments depending on the specialisation and status of the doctor, on his or (...) her rights to allocate Health Care resources and/or on how assertive he or she is. We show, by means of a content analysis of internet postings and of interviews with physicians, the feelings and attitudes of both patients and doctors—thus enabling us to trace interactional and procedural injustice and portray the main driving forces of informal payment with causal loops. We use a new approach (a system dynamics computer simulation) to demonstrate how informal payments influence therapy procedures. The example of treating the common skin disease of psoriasis examines the specific behaviour of doctors in their everyday practice and estimates the extent to which the prescription of unnecessary or more expensive therapy increases the costs. With this multi-method approach, we demonstrate that the legislation allowing informal payment represents bad ethics, since it enforces organisational injustice, creates mistrust between physicians and patients, decreases performance and increases the total costs paid both by society and the Health Insurance Fund. Informal payments distort the allocation of resources, enforce discrimination based solely on money, violate the concept of solidarity and limit Human Rights in terms of health. (shrink)

In this essay we collect and put together a number of ideas relevant to the under- standing of the phenomenon of creativity, confining our considerations mostly to the domain of cognitive psychology while we will, on a few occasions, hint at neuropsy- chological underpinnings as well. In this, we will mostly focus on creativity in science, since creativity in other domains of human endeavor have common links with scientific creativity while differing in numerous other specific respects. We begin by briefly (...) introducing a few basic notions relating to cognition, among which the notion of ‘concepts’ is of basic relevance. The myriads of concepts lodged in our mind constitute a ‘conceptual space’ of an enormously complex structure, where con- cepts are correlated by beliefs that are themselves made up of concepts and are as- sociated with emotions. The conceptual space, moreover, is perpetually in a state of dynamic evolution that is once again of a complex nature. A major component of the dynamic evolution is made up of incessant acts of inference, where an inference occurs essentially by means of a succession of correlations among concepts set up with beliefs and heuristics, the latter being beliefs of a special kind, namely, ones relatively free of emotional associations and possessed of a relatively greater degree of justification. Beliefs, along with heuristics, have been described as the ‘mind’s software’, and con- stitute important cognitive components of the self-linked psychological resources of an individual. The self is the psychological engine driving all our mental and physical activity, and is in a state of ceaseless dynamics resulting from one’s most intimate ex- periences of the world accumulating in the course of one’s journey through life. Many of our psychological resources are of a dual character, having both a self-linked and a shared character, the latter being held in common with larger groups of people and imbibed from cultural inputs. We focus on the privately held self-linked beliefs of an individual, since these are presumably of central relevance in making possible inductive inferences – ones in which there arises a fundamental need of adopting a choice or making a decision. Beliefs, decisions, and inferences, all have the common link to the self of an individual and, in this, are fundamentally analogous to free will, where all of these have an aspect of non-determinism inherent in them. Creativity involves a major restructuring of the conceptual space where a sustained inferential process eventually links remote conceptual domains, thereby opening up the possibility of a large number of new correlations between remote concepts by a cascading process. Since the process of inductive inference depends crucially on de- cisions at critical junctures of the inferential chain, it becomes necessary to examine the basic mechanism underlying the making of decisions. In the framework that we attempt to build up for the understanding of scientific creativity, this role of decision making in the inferential process assumes central relevance. With this background in place, we briefly sketch the affect theory of decisions. Affect is an innate system of response to perceptual inputs received either from the exter- nal world or from the internal physiological and psychological environment whereby a positive or negative valence gets associated with a perceptual input. Almost every sit- uation faced by an individual, even one experienced tacitly, i.e., without overt aware-ness, elicits an affective response from him, carrying a positive or negative valence that underlies all sorts of decision making, including ones carried out unconsciously in inferential processes. Referring to the process of inferential exploration of the conceptual space that gener- ates the possibility of correlations being established between remote conceptual do- mains, such exploration is guided and steered at every stage by the affect system, analogous to the way a complex computer program proceeds through junctures where the program ascertains whether specified conditions are met with by way of generating appropriate numerical values – for instance, the program takes different routes, depending on whether some particular numerical value turns out to be positive or negative. The valence generated by the affect system in the process of adoption of a choice plays a similar role which therefore is of crucial relevance in inferential processes, especially in the exploration of the conceptual space where remote domains need to be linked up – the affect system produces a response along a single value dimension, resembling a number with a sign and a magnitude. While the affect system plays a guiding role in the exploration of the conceptual space, the process of exploration itself consists of the establishment of correlations between concepts by means of beliefs and heuristics, the self-linked ones among the latter having a special role in making possible the inferential journey along alternative routes whenever the shared rules of inference become inadequate. A successful access to a remote conceptual domain, necessary for the creative solution of a standing problem or anomaly – one that could not be solved within the limited domain hitherto accessed – requires a phase of relatively slow cumulative search and then, at some stage, a rapid cascading process when a solution is in sight. Representing the conceptual space in the form of a complex network, the overall process can be likened to one of self-organized criticality commonly observed in the dynamical evolution of complex systems. In order that inferential access to remote domains may actually be possible, it is necessary that restrictions on the exploration process – necessary for setting the context in ordinary instances of inductive inference – be relaxed and a relatively free exploration in a larger conceptual terrain be made possible. This is achieved by the mind going into the default mode, where external constraints – ones imposed by shared beliefs and modes of exploration – are made inoperative. While explaining all these various aspects of the creative process, we underline the supremely important role that analogy plays in it. Broadly speaking, analogy is in the nature of a heuristic, establishing correlations between concepts. However, analo- gies are very special in that these are particularly effective in establishing correlations among remote concepts, since analogy works without regard to the contiguity of the concepts in the conceptual space. In establishing links between concepts, analogies have the power to light up entire terrains in the conceptual space when a rapid cas- cading of fresh correlations becomes possible. The creative process occurs within the mind of a single individual or of a few closely collaborating individuals, but is then continued by an entire epistemic community, eventually resulting in a conceptual revolution. Such conceptual revolutions make pos- sible the radical revision of scientific theories whereby the scope of an extant theory is broadened and a new theoretical framework makes its appearance. The emerging theory is characterized by a certain degree of incommensurability when compared with the earlier one – a feature that may appear strange at first sight. But incommen- surability does not mean incompatibility and the apparently contrary features of the relation between the successive theories may be traced to the multi-layered structureof the conceptual space where concepts are correlated not by means of single links but by multiple ones, thereby generating multiple layers of correlation, among which some are retained and some created afresh in a conceptual restructuring. We conclude with the observation that creativity occurs on all scales. Analogous to correlations being set up across domains in the conceptual space and new domains being generated, processes with similar features can occur within the confines of a domain where a new layer of inferential links may be generated, connecting up sub- domains. In this context, insight can be looked upon as an instance of creativity within the confines of a domain of a relatively limited extent. (shrink)

This paper develops a natural-language agent-based model of argumentation (ABMA). Its artificial deliberative agents (ADAs) are constructed with the help of so-called neural language models recently developed in AI and computational linguistics. ADAs are equipped with a minimalist belief system and may generate and submit novel contributions to a conversation. The natural-language ABMA allows us to simulate collective deliberation in English, i.e. with arguments, reasons, and claims themselves — rather than with their mathematical representations (as in symbolic models). This (...) paper uses the natural-language ABMA to test the robustness of symbolic reason-balancing models of argumentation (Mäs & Flache 2013; Singer et al. 2019): First of all, as long as ADAs remain passive, confirmation bias and homophily updating trigger polarization, which is consistent with results from symbolic models. However, once ADAs start to actively generate new contributions, the evolution of a conversation is dominated by properties of the agents as authors. This suggests that the creation of new arguments, reasons, and claims critically affects a conversation and is of pivotal importance for understanding the dynamics of collective deliberation. The paper closes by pointing out further fruitful applications of the model and challenges for future research. (shrink)

As one of the most effective medical technologies for the infertile patients, in vitro fertilization has been more and more widely developed in recent years. However, prolonged waiting for IVF procedures has become a problem of great concern, since this technology is only mastered by the large general hospitals. To deal with the insufficiency of IVF service capacity, this paper studies an IVF queuing network in an integrated cloud healthcare system, where the two key medical services, that is, egg (...) retrieval and transplantation, are assigned to accomplish in the general hospital, while the routine medical tests are assigned into the community hospital. Based on continuous-time Markov procedure, a dynamic large-scale server scheduling problem in this complicated service network is modeled with consideration of different arrival rates of multiple type of patients and different service capacities of multiple servers that can be defined as doctors of the general hospital. To solve this model, a reinforcement learning algorithm is proposed, where the reward functions are designed for four conflicting subcosts: setup cost, patient waiting cost, penalty cost for unsatisfied patient personal preferences, and medical cost of patient. The experimental results show that the optimal service rule of each server’s queue obtained by the RL method is significantly superior to the traditional service rule. (shrink)

The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates (...) the potential of AI to revolutionize sustainable energy management. Experimental results highlight improvements in grid reliability, cost reduction, and carbon footprint minimization, paving the way for resilient and eco-friendly urban energy systems. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number of (...) subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

Large language models now possess human-level linguistic abilities in many contexts. This raises the concern that they can be used to deceive and manipulate on unprecedented scales, for instance spreading political misinformation on social media. In future, agentic AI systems might also deceive and manipulate humans for their own purposes. In this paper, first, I argue that AI-generated content should be subject to stricter standards against deception and manipulation than we ordinarily apply to humans. Second, I offer new characterizations of (...) AI deception and manipulation meant to support such standards, according to which a statement is deceptive (resp. manipulative) if it leads human addressees away from the beliefs (resp. choices) they would endorse under “semi-ideal” conditions. Third, I propose two measures to guard against AI deception and manipulation, inspired by this characterization: “extreme transparency” requirements for AI-generated content and “defensive systems” that, among other things, annotate AI-generated statements with contextualizing information. Finally, I consider to what extent these measures can protect against deceptive behavior in future, agentic AI systems. (shrink)

Ecological psychology (EP) and the enactive approach (EA) may benefit from a more focused view of lived temporality and the underlying temporal multiscalar nature of human living. We propose multiscalar temporality (MT) as a framework that complements EP and EA, and moves beyond their current conceptualisation of timescales and inter-scale relationships in organism-environment dynamical systems. MT brings into focus the wide ranging and meshwork-like interdependencies at play in human living and the questions concerning how agents are intimately entangled in such (...) meshworks, utilising them as resources for skilful living. We develop a conceptual toolkit that highlights temporality: Firstly, we address lived temporality. We use a case study from psychotherapy to show how a person’s skilful engagement with the world is best described as adaptive harnessing of interdependencies of constraints residing across a wide range of timescales. We call this skill time-ranging. Secondly, the case study provides a proof of concept of the integration of an idiographic approach to human conversing and a more general theory of emergent organisation rooted in theoretical biology. We introduce the existing concept of constraint closure from theoretical biology and scale it up to human interactivity. The detailed conceptualisation of constraint interdependencies constitutes the backbone of the proposal. Thirdly, we present a heuristic mapping of what we call organising frames. The mapping guides the conceptualisation of the emergence of inter-scale relationships and serves as an epistemic tool that brings together nomothetic and idiographic approaches. Finally, we combine new ideas with re-interpretations of existing EP and EA concepts and elaborate on the need of a fresh new look at the implicit and sometimes missing conceptualisations of temporality in the EP and EA literature. (shrink)

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI (...) in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment. (shrink)

Two approaches to externally distributed individual cognition are contrasted in the paper. The first begins with making a distinction between minds and artifacts, both considered as structural components of larger-scale cognitive systems, while the second focuses on the dynamic coordination of internal and external resources within the context of human interaction with the world. Conceptual limitations of the first approach are discussed. The notion of functional organs is introduced and applied for identifying the types of abilities associated with efficient (...) integration of artifacts into the structure of human activity. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Toward Resilient DemocracyCognitive Resources and ConstraintsJohn Teehan (bio)I. Introduction: The Cognitive Science of ReligionAmerican Immanence, an important and insightful work, offers an analysis of the existential crisis facing American democracy, and a possible path through this crisis. In developing this path, Michael Hogue asks, "can the feeling and awareness of the precarious value of life …awaken us to the precious depths of immanence, to living as if this, our (...) one and only world, matters ultimately?"1 Such an awakening, he argues, is vital to developing a "resilient democracy." I believe the answer to his question is "yes," and a positive answer to this profound question is sufficient warrant for exploring the path Hogue sets out. However, there is another question, one with broader implications: can this moral vision be adopted on a wide enough scale to attain the critical mass necessary to develop a democracy sufficiently resilient to weather this crisis? I will explore this second question using the insights derived from cognitive science, and particularly from the cognitive science of religion. This approach picks up on Hogue's invitation to interpret religion "within the evolutionary contexts of natural history,"2 and concurs with his claim that in "demystifying religion" an "evolutionary account …opens the way to a religious experience."3The Cognitive Science of Religion (CSR) is grounded in an evolutionary understanding of human cognitive and emotional processes, as these processes have developed to enable the species to respond to the challenges of survival and reproduction in dynamic environments, both physical and social. These cognitive tools were not shaped to discern some pre-given reality, but rather to serve the practical goals of natural beings embedded in natural situations. CSR, then, is congruent with the immanental philosophy, grounded in pragmatism, that Hogue develops in his book. Indeed, it can be argued that the methodology of CSR implicitly entails a pragmatist epistemology4. The various [End Page 65] mental tools and resultant religious beliefs and rituals all serve the ultimate goal of successful action within natural situations. CSR, then, can provide an empirical account of at least some of the processes that would be involved in actualizing an immanental political theology, and may identify some of the cognitive obstacles to that project.It is beyond the scope of this essay to set out a detailed account of CSR, but it will suffice to introduce some general tenets that have played a seminal role in the field.5 Those elements of a cultural system that we would deem religious are derived from natural cognitive processes that evolved to allow our earliest ancestors to successfully navigate an uncertain and dangerous environment.6 As an example, let's consider the gods, understanding by the "gods" any of the various beings that might commonly be termed "supernatural" (e.g., gods, spirits, ghosts, demons, fairies, divine ancestors, etc.). Belief in gods is theorized to flow, in part, from natural cognitive mechanisms for the detection of agents (that is, beings that act with intent). The ability to detect such beings—beings that might pose a danger—is essential to surviving in an uncertain and dangerous world. This ability was so vital that natural selection favored the evolution of a cognitive process for detecting agents that is hypersensitive.7 Detecting the presence of an agent when none is actually present is a much safer error than failing to detect an actual predator, and so this cognitive strategy could regularly give rise to false positives.Detecting the presence of an agent is the beginning phase of devising effective action, but to determine how to respond to this perceived agent, we must make sense of its possible intent. Therefore, we do not merely perceive a presence, we ascribe intention and purpose to that agent. In doing so, we engage the mental process termed Theory of Mind (ToM).8 Detecting the [End Page 66] action of agents, and determining intention and purpose, are vital to successful action. In a dangerous and uncertain world—i.e. the world in which humans have lived for millennia—these skills are essential to survival, and it follows that natural selection would favor cognitive processes that erred on the side of... (shrink)

The regulation of the activity of multiple autonomous entities represented in a multi-agent system, in environments with no central design (and thus with no cooperative assumption), is gaining much attention in the research community. Approaches to this concern include the use of norms in so-called normative multi-agent systems and the development of electronic institution frameworks. In this paper we describe our approach towards the development of an electronic institution providing an enforceable normative environment. Within this environment, (...) institutional services are provided that assist agents in forming cooperative structures whose commitments are made explicit through contracts. Our normative framework borrows some concepts from contract law theory. Contracts are formalized using norms which are used by the institution while monitoring agents’ activities, thus making our normative environment dynamic. We regard the electronic institution as a means to facilitate both the creation and the enforcement of contracts between agents. A model of “institutional reality” is presented that allows for monitoring the fulfillment of norms. The paper also distinguishes our approach from other developments of the electronic institution concept. We address the application of our proposal in the B2B field, namely regarding the formation of Virtual Organizations. (shrink)

The use of computers for simulation work can be traced back to the 1950s, and the pioneering work of Stafford Beer, KD Tocher and others at Cybor House in Sheffield, UK, the research and development (R&D) department of British steelmakers, United Steel. This innovative simulation work sought to offer an abstracted, ‘total’ environment of the steelmaking process in which different operational activities could be modeled. Critical to this work was the ability of computer simulations to perform such modelling at a (...) fraction of the cost, wasting fewer material resources, and in a considerably shorter timeframe. Such work can be understood as the earliest example of the application of industrial-scale ‘automated computation’ to a real-world industrial process. Similarly indebted to the early principles of computer simulation, Waymo engineers are also engaged in the building of so-called ‘conflict typologies’ designed to encode material properties of everyday driving interactions between road users, rather than simply road users themselves. Through ‘motion planning’, coupled with the categorization of driving interactions, Waymo engineers build instrumental understanding of their own system’s purported intelligence in navigating everyday driving situations. Functioning as ‘generative mechanisms’ rather than simply evaluative devices, engineers seek to industrialize—instrumentalize, scale up, rationalize—everyday driving knowledge. Through conflict typologies, instrumental knowledge of the actual capacities of autonomous vehicles is industrialized, materialized, and realized. (shrink)

This article develops a biosemiotic ´hyper-narrative model´ for the purposes of investigating emergent motor behaviors. It proposes to understand such behaviors in terms of the following associations: the organization of information acquired from the environment, focusing on narrative; the organizational dynamics of epigenetic mechanisms that underly the neural processes facilitating the processing of information; and the evolution of emergent motor behaviors that enable the informational acquisition. The article describes and explains these associations as part of a multi-ordered and (...) class='Hi'>multi-causal generative principle of biological phenotype emergence that supersedes the theory of the arbitrary coding of life. Preceeding from narrative’s operations in a biological dimension, the article presents scientific research dealing with the associations of action-oriented organization of narrative information and underlying psychological and physiological dynamics and depicts the relations with a distributed multi-directional mapping dynamic. The article presents the explanatory implications of such a hyper-narrative dynamic model on an example of emergent motor behaviors – bruxism. Central to this discussion is the exploration of the possible mechanisms of emergence and etiopathogenesis of bruxism, based on its neurobiology. The article takes the perspective that complex systems dynamics themselves with a tendency to narrative form are found not to be underlain merely by arbitrary coded mechanisms but, rather, biological neural networks (e.g. neuro-epigenetic network) that render context-dependent bio-informational mapping analogous to that of the narrative possible. (shrink)

Most of the IoT-based smart systems require low latency and crisp response time for their applications. Achieving the demand of this high Quality of Service becomes quite challenging when computationally intensive tasks are offloaded to the cloud for execution. Edge computing therein plays an important role by introducing low network latency, quick response, and high bandwidth. However, offloading computations at a large scale overwhelms the edge server with many requests and the scalability issue originates. To address the above issues, (...) an efficient resource management technique is required to maintain the workload over the edge and ensure the reduction of response time for IoT applications. Therefore, in this paper, we introduce a metaheuristic and nature-inspired Artificial Bee Colony optimization technique that effectively manages the workload over the edge server under the strict constraints of low network latency and quick response time. The numerical results show that the proposed ABC algorithm has outperformed Particle Swarm Optimization, Ant Colony Optimization, and Round-Robin Scheduling algorithms by producing low response time and effectively managing the workload over the edge server. Furthermore, the proposed technique scales the edge server to meet the demand of high QoS for IoT applications. (shrink)

Massively multiplayer online games are not merely electronic communication systems based on computational databases, but also include artificial intelligence that possesses complex, dynamic structure. Each visible action taken by a component of the multi-agent system appears simple, but is supported by vastly more sophisticated invisible processes. A rough outline of the typical hierarchy has four levels: interaction between two individuals, each either human or artificial, conflict between teams of agents who cooperate with fellow team members, enduring social-cultural (...) groups that seek to accomplish shared goals, and large-scale cultural traditions, often separated into virtual geographic regions. In many MMOs, both magic and religion are represented, in ways that harmonize with a social-scientific theory that defines them in terms of specific versus general psychological compensators. This article draws empirical examples from five diverse MMOs: Dark Age of Camelot, Dungeons and Dragons Online, World of Warcraft, A Tale in the Desert and Gods and Heroes. (shrink)

The first aim of simulation in virtual environment is to help biologists to have a better understanding of the simulated system. The cost of such simulation is significantly reduced compared to that of in vivo simulation. However, the inherent complexity of biological system makes it hard to simulate these systems on non-parallel architectures: models might be made of sub-models and take several scales into account; the number of simulated entities may be quite large. Today, graphics cards are used for (...) general purpose computing which has been made easier thanks to frameworks like CUDA or OpenCL. Parallelization of models may however not be easy: parallel computer programing skills are often required; several hardware architectures may be used to execute models. In this paper, we present the software architecture we built in order to implement various models able to simulate multi-cellular system. This architecture is modular and it implements data structures adapted for graphics processing units architectures. It allows efficient simulation of biological mechanisms. (shrink)

Recently, agent technologies have attracted a lot of interest as an emerging programming paradigm. With such agent technologies, services are provided through collaboration among agents. At the same time, the spread of mobile technologies and communication infrastructures has made it possible to access the network anytime and from anywhere. Using agents and mobile technologies to realize ubiquitous computing systems, we propose a new framework based on KODAMA and VPC. KODAMA provides distributed management mechanisms by using the concept of (...) community and communication infrastructure to deliver messages among agents without agents being aware of the physical network. VPC provides a method of defining peer-to-peer services based on agent communication with policy packages. By merging the characteristics of both KODAMA and VPC functions, we propose a new framework for ubiquitous computing environments. It provides distributed management functions according to the concept of agent communities, agent communications which are abstracted from the physical environment, and agent collaboration with policy packages. Using our new framework, we conducted a large-scale experiment in shopping malls in Nagoya, which sent advertisement e-mails to users' cellular phones according to user location and attributes. The empirical results showed that our new framework worked effectively for sales in shopping malls. (shrink)

In the knowledge economy, the process of knowledge sharing and creation for value co-creation frequently emerge in a multi-agent and multi-level system. It's important to consider the roles, functions, and possible interactive knowledge-based activities of key actors for ecological development. Makerspace as an initial stage of incubated platform plays the central and crucial roles of resource orchestrators and platform supporter. Less literature analyses the knowledge ecosystem embedded by makerspaces and considers the interactive process of civil society (...) and natural environment. This study constructs a multi-agent and multi-level knowledge ecosystem from macro, meso, and micro perspective based on Quintuple Helix theory and designs four evolutionary stages of knowledge orchestrating processes. This study finds that the symbiosis, co-evolution, interaction, and orchestration of multiple agents in the knowledge ecosystem should be merged with each other for value co-creation, which helps to take a systematic approach for policymakers, managers, and researchers. (shrink)

This article uses a research method that combines theoretical research and empirical analysis. It first introduces the relevant theories of energy service city distribution sites in the context of e-commerce and then the types of energy service city distribution sites and the composition of energy service city distribution systems. The network layout of the service city distribution site and the location objectives, principles, and processes of the model is studied to determine the network layout plan of (...) the energy service city distribution system in this paper. This paper fully considers the characteristics of the network operation mode of the energy service city distribution site and establishes an optimization model for the location selection and vehicle routing of the distribution center with the lowest total system cost under the simultaneous delivery service mode; based on the hierarchical solution strategy, a combination of deep learning is designed. Algorithms mainly include two-stage hybrid heuristic algorithm of cluster analysis, maximum coverage and genetic algorithm; simulation analysis is conducted to verify the effectiveness of the model and algorithm by data simulation, finally get the integrated optimization plan of distribution center location and routing, and put forward the operation strategy through the result expansion analysis. This paper studies the planning model based on the network layout planning of the energy service city distribution system under the e-commerce environment, aiming to promote the breakthrough development of urban smart logistics and prove the importance of the energy service city distribution station network layout planning. The purpose and results of the research are to reduce traffic and environmental pressures, achieve joint direct distribution, improve the efficiency of urban logistics and distribution, and solve the problem of the last mile of the city. (shrink)

Multi-level institutional designs with distributed power and authority arrangements among federal, state, regional, and local government agencies could lead to the emergence of differential patterns of socioeconomic and infrastructure development pathways in complex social–ecological systems. Both exogenous drivers and endogenous processes in social–ecological systems can lead to changes in the number of “basins of attraction,” changes in the positions of the basins within the state space, and changes in the positions of the thresholds between basins. In an effort to (...) advance the theory and practice of the governance of policy systems, this study addresses a narrower empirical question: how do intergovernmental institutional rules set by federal, state, and regional government agencies generate and sustain basins of attraction in funding infrastructure projects? A pattern-oriented, agent-based model (ABM) of an intergovernmental network has been developed to simulate real-world transportation policy implementation processes across the federal, the state of Vermont, regional, and local governments for prioritizing transportation projects. The ABM simulates baseline and alternative intergovernmental institutional rule structures and assesses their impacts on financial investment flows. The ABM was calibrated with data from multiple focus groups, individual interviews, and analysis of federal, state, and regional scale transportation projects and programs. The results from experimental simulations are presented to test system-wide effects of alternative multi-level institutional designs, in particular different power and authority arrangements between state and regional governments, on the emergence of roadway project prioritization patterns and funding allocations across regions and towns. (shrink)

The enactive and ecological approaches to embodied cognitive science are on a collision course. While both draw inspiration from similar views in psychology and phenomenology, the two approaches initially held seemingly contradictory views and points of focus. Early enactivists saw value in the ecological approach but insisted that the two schools remain distinct. While ecological psychology challenged the common foes of mental representation and mind-body dualism, it seemingly did so at the cost of the autonomy of the agent. (...) This is evidence that the early enactive and ecological approaches told different stories about how agents and environments interact. Whereas the enactive approach broadly focuses on agency and the organism’s resilience to environmental perturbations, the ecological approach insists that organisms are best understood in terms of the organism–environment system and at the ecological scale. Historically, this tension created space for harsh criticisms from both sides and for some ecological psychologists to dismiss enactivism altogether. Despite their differences, both approaches use dynamic systems theory to explain the interactions between embodied agents and the environment or contextual milieu in which they are embedded. This has led some scholars to focus on the complementary elements of each approach and argue that the two schools are allies, thus rejecting the historical disagreements between the two approaches and calling for an ecological–enactive synthesis. The attempts to synthesize the approaches are noteworthy and should be considered steps in the right direction but are potentially problematic. If the two schools are merely synthesized to some form of ecological–enactivism, then something of value from both approaches could be lost. This is analogous to the hasty comparison between two seemingly similar schools of thought found in early attempts at East-West comparative philosophy. I argue that the relationship between the enactive and ecological approaches is both complementary and contrary and is thus best understood in terms of complementarity. Given the complexity of complementarity I will unpack the notion in steps. I will begin with the exploration of analogous concepts in Japanese Philosophy and gradually build a lens through which both agent environment and ecological enactive complementarities can be understood. (shrink)

Agenda Relevance is the first volume in the authors' omnibus investigation of the logic of practical reasoning, under the collective title, A Practical Logic of Cognitive Systems. In this highly original approach, practical reasoning is identified as reasoning performed with comparatively few cognitive assets, including resources such as information, time and computational capacity. Unlike what is proposed in optimization models of human cognition, a practical reasoner lacks perfect information, boundless time and unconstrained access to computational complexity. The practical reasoner is (...) therefore obliged to be a cognitive economizer and to achieve his cognitive ends with considerable efficiency. Accordingly, the practical reasoner avails himself of various scarce-resource compensation strategies. He also possesses neurocognitive traits that abet him in his reasoning tasks. Prominent among these is the practical agent's striking (though not perfect) adeptness at evading irrelevant information and staying on task. On the approach taken here, irrelevancies are impediments to the attainment of cognitive ends. Thus, in its most basic sense, relevant information is cognitively helpful information. Information can then be said to be relevant for a practical reasoner to the extent that it advances or closes some cognitive agenda of his. The book explores this idea with a conceptual detail and nuance not seen the standard semantic, probabilistic and pragmatic approaches to relevance; but wherever possible, the authors seek to integrate alternative conceptions rather than reject them outright. A further attraction of the agenda-relevance approach is the extent to which its principal conceptual findings lend themselves to technically sophisticated re-expression in formal models that marshal the resources of time and action logics and label led deductive systems. Agenda Relevance is necessary reading for researchers in logic, belief dynamics, computer science, AI, psychology and neuroscience, linguistics, argumentation theory, and legal reasoning and forensic science, and will repay study by graduate students and senior undergraduates in these same fields. Key features: relevance action and agendas practical reasoning belief dynamics non-classical logics labelled deductive systems. (shrink)

The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in (...) the network. Through the use of a multi-agent reinforcement learning algorithm, this paper proposes a novel algorithm for malware detection in IoT devices. The proposed algorithm is not strongly dependent on the size of the IoT network due to the that its training is adapted using time differences if the IoT network size is small or Monte Carlo otherwise. To validate the proposed algorithm in an environment as close to reality as possible, we proposed a scenario based on a real IoT network, where we tested different malware propagation models. Different simulations varying the number of agents and nodes in the IoT network have been developed. The result of these simulations proves the efficiency and adaptability of the proposed algorithm in detecting malware, regardless of the malware propagation model. (shrink)