The semantic fluency task is commonly used as a measure of one’s ability to retrieve semantic concepts. While performance is typically scored by counting the total number of responses, the ordering of responses can be used to estimate how individuals or groups organize semantic concepts within a category. I provide an overview of this methodology, using Alzheimer’s disease as a case study for how the approach can help advance theoretical questions about the nature of semantic representation. However, many open (...) questions surrounding the validity and reliability of this approach remain unresolved. (shrink)

It is almost universally acknowledged that first-order logic (FOL), with its clean, well-understood syntax and semantics, allows for the clear expression of philosophical arguments and ideas. Indeed, an argument or philosophical theory rendered in FOL is perhaps the cleanest example there is of “representing philosophy”. A number of prominent syntactic and semantic properties of FOL reflect metaphysical presuppositions that stem from its Fregean origins, particularly the idea of an inviolable divide between concept and object. These presuppositions, taken at face value, (...) reflect a significant metaphysical viewpoint, one that can in fact hinder or prejudice the representation of philosophical ideas and arguments. Philosophers have of course noticed this and have, accordingly, sought to alter or extend traditional FOL in novel ways to reflect a more flexible and egalitarian metaphysical standpoint. The purpose of this paper, however, is to document and discuss how similar “adaptations” to FOL—culminating in a standardized framework known as Common Logic —have evolved out of the more practical and applied encounter of FOL with the problem of representing, sharing, and reasoning upon information on the World Wide Web. (shrink)

Knowledge Representation and Reasoning (KR&R) is based on the idea that propositional content can be rigorously represented in formal languages long the province of logic, in such a way that these representations can be productively reasoned over by humans and machines; and that this reasoning can be used to produce knowledge-based systems (KBSs). As such, KR&R is a discipline conventionally regarded to range across parts of artificial intelligence (AI), computer science, and especially logic. This standard view of (...) KR&R’s participating fields is correct — but dangerously incomplete. The view is incomplete because, as we explain herein, sophisticated KR&R must rely heavily upon philosophy. Encapsulated, the reason is actually quite straightforward: Sophisticated KR&R must include the representation of not only simple properties, but also concepts that are routine in the formal sciences (theoretical computer science, mathematics, logic, game theory, etc.), and everyday socio-cognitive concepts like mendacity, deception, betrayal, and evil. Because in KR&R the representation of such concepts must be rigorous in order to enable machine reasoning (e.g., machine-generated and machine-checked proofs that a is lying to b) over them, philosophy, devoted as it is in no small part to supplying analyses of such concepts, is a crucial partner in the overall enterprise. To put the point another way: When the knowledge to be represented is such as to require lengthy formulas in expressive formal languages for that representation, philosophy must be involved in the game. In addition, insofar as the advance of KR&R must allow formalisms and processes for representing and reasoning over visual propositional content, philosophy will be a key contributor into the future. (shrink)

in Handbook of Knowledge representation, Elsevier, 2008.

We present a preliminary high-level formal theory, grounded on knowledge representation techniques and foundational ontologies, for the uniform and integrated representation of the different kinds of (quali- tative and quantitative) knowledge involved in the designing process. We discuss the conceptual nature of engineering design by individuating and analyzing the involved notions. These notions are then formally charac- terized by extending the DOLCE foundational ontology. Our ultimate purpose is twofold: (i) to contribute to foundational issues of design; (...) and (ii) to support the development of advanced modelling systems for (qualitative and quantitative) representation of design knowledge. (shrink)

Machine learning (ML) techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis, and insurance pricing. Likewise, in the physical world, ML models are critical components in autonomous agents such as robotic surgeons and self-driving cars. Among the many ethical dimensions that arise in the use of ML technology in such applications, analyzing morally permissible actions is both immediate and profound. For example, there is the (...) potential for learned algorithms to become biased against certain groups. More generally, in so much that the decisions of ML models impact society, both virtually (e.g., denying a loan) and physically (e.g., driving into a pedestrian), notions of accountability, blame and responsibility need to be carefully considered. In this article, we advocate for a two-pronged approach ethical decision-making enabled using rich models of autonomous agency: on the one hand, we need to draw on philosophical notions of such as beliefs, causes, effects and intentions, and look to formalise them, as attempted by the knowledge representation community, but on the other, from a computational perspective, such theories need to also address the problems of tractable reasoning and (probabilistic) knowledge acquisition. As a concrete instance of this tradeoff, we report on a few preliminary results that apply (propositional) tractable probabilistic models to problems in fair ML and automated reasoning of moral principles. Such models are compilation targets for certain types of knowledge representation languages, and can effectively reason in service some computational tasks. They can also be learned from data. Concretely, current evidence suggests that they are attractive structures for jointly addressing three fundamental challenges: reasoning about possible worlds + tractable computation + knowledge acquisition. Thus, these seems like a good starting point for modelling reasoning robots as part of the larger ecosystem where accountability and responsibility is understood more broadly. (shrink)

This article provides an overview of the subfield of Artificial Intelligence known as “Knowledge Representation and Reasoning.” This field uses the techniques of philosophical logic, but aims at providing a theoretical basis for the management of declarative information in automated reasoning systems. Three topics are singled out here for attention: planning and reasoning about actions, description logics, and nonmonotonic logics.

Knowledge on regulatory relations, in for example regulatory pathways in biology, is used widely in experiment design by biomedical researchers and in systems biology. The knowledge has typically either been represented through simple graphs or through very expressive differential equation simulations of smaller sections of a pathway. As an alternative, in this work we suggest a knowledge representation of the most basic relations in regulatory processes regulates, positively regulates and negatively regulates in logics based on a (...) semantic analysis. We discuss the usage of these relations in biology and in artificial intelligence for hypothesis development in drug discovery. (shrink)

Contents: Foreword. Wolfgang BALZER and C. ULISES MOULINES: Introduction. José A. DÍEZ CALZADA: Structuralist Analysis of Theories of Fundamental Measurement. Adolfo GARCÍA DE LA SIENRA and Pedro REYES: The Theory of Finite Games in Extensive Form. Hans Joachim BURSCHEID und Horst STRUVE: The Theory of Stochastic Fairness - its Historical Development, Formulation and Justification. Wolfgang BALZER and Richard MATTESSICH: Formalizing the Basis of Accounting. Werner DIEDERICH: A Reconstruction of Marxian Economics. Bert HAMMINGA and Wolfgang BALZER: The Basic Structure of Neoclassical (...) General Equilibrium Theory. Klaus MANHART: Balance Theories: Two Reconstructions and the Problem of Intended Applications. Rainer WESTERMANN: Festinger's Theory of Cognitive Dissonance: A Structuralist Theory-Net. Rainer REISENZEIN: Wundt's Three-Dimensional Theory of Emotion. Pablo LORENZANO: Classical Genetics and the Theory-Net of Genetics. Hinne HETTEMA and Theo A.F. KUIPERS: The Formalisation of the Periodic Table. C. ULISES MOULINES: The Basic Core of Simple Equilibrium Thermodynamics. Thomas BARTELBORTH: An Axiomatization of Classical Electrodynamics. Author's Index. Subject Index. (shrink)

Two different but closely related issues in current cognitive science will be considered in this essay. One is the controversial and extensively discussed question of how connectionist and symbolic representations of knowledge are related to each other. The other concerns the notion of connectionist learning and its relevance for the understanding of the distinction between propositional and nonpropositional knowledge. More specifically, I shall give an overview of a result in Rantala and Vadén (1994) establishing a limiting case correspondence (...) between symbolic and connectionist representations and, on the other hand, study the problem, preliminarily investigated in Rantala (1998), of how propositional knowledge may arise from nonpropositional knowledge. I shall also try to point out that on some more or less plausible assumptions, often made by cognitive scientists, these results may have some significance when we try to comprehend the nature of human knowledge representation. Some of these assumptions are rather hypothethical and debatable for the time being and they will become justified in the future only if there will be more progress in the empirical and theoretical research on the brain and on artificial networks. The assumptions concern, besides some questions of the behavior of neural networks, such things as the relevance of pattern recognition for modelling human cognition, in particular, knowledge acquisition, and the relation between emergence and reduction. (shrink)

Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with problems in knowledge representation and inference has often been questioned. This paper partly answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach. The paper presents a connectionist realization of semantic networks, that is, it describes how (...) knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter‐free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast—in time proportional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions and conflicting multiple inheritance situations during inheritance, and the best‐match or partial‐match computation during recognition. The paper also identifies constraints that must be satisfied by the conceptual structure in order to arrive at an efficient parallel realization. (shrink)

An introduction to the various logics of truth and modality as part of a foundation for the construction of theories of knowledge representation. The book reviews various semantic theories and employs them as the basis for the development of logics of truth and modality.

Logic has been a—disputed—ingredient in the emergence and development of the now very large field known as knowledge representation and reasoning. In this book (in progress), I select some central topics in this highly fruitful, albeit controversial, association (e.g., non-monotonic reasoning, implicit belief, logical omniscience, closed world assumption), identifying their sources and analyzing/explaining their elaboration in highly influential published work.

Knowledge representation, which lies at the core of artificial intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The aims are to help readers make their computer smarter, handle qualitative and uncertain information, and improve computational tractability.

Several approaches to implementing symbol-like representations in neurally plausible models have been proposed. These approaches include binding through synchrony, “mesh” binding, and conjunctive binding. Recent theoretical work has suggested that most of these methods will not scale well, that is, that they cannot encode structured representations using any of the tens of thousands of terms in the adult lexicon without making implausible resource assumptions. Here, we empirically demonstrate that the biologically plausible structured representations employed in the Semantic Pointer Architecture approach (...) to modeling cognition do scale appropriately. Specifically, we construct a spiking neural network of about 2.5 million neurons that employs semantic pointers to successfully encode and decode the main lexical relations in WordNet, which has over 100,000 terms. In addition, we show that the same representations can be employed to construct recursively structured sentences consisting of arbitrary WordNet concepts, while preserving the original lexical structure. We argue that these results suggest that semantic pointers are uniquely well-suited to providing a biologically plausible account of the structured representations that underwrite human cognition. (shrink)

In many computer applications we face the following situation: we are given set of objects which are characterized by means of some features like: temperature, height, weight etc., and we want to dene in terms of those features some concepts. For example we have a data le concerning patients suering from a certain disease. State of each patient is characterized by some symptoms like, for example: blood pressure, temperature etc. The question arises whether wa are able to dene this disease (...) in terms of avail- able symptoms. Notice that we identify here concept of a specic disease with a set of patients suering from this disease. Hence the denition of a disease depends upon specic example of a disease under consideration. This approach refers to inductive reasoning, when a concept is built upon the basis of a nite number of examples of this concept. More detailed discussion of problems considered here one { can nd in Pawlak , Pawlak and Or lowska and Pawlak. (shrink)

Taking the perspective of knowledge representation, we introduce a simple logic of agency where the agents’ actions are described by their precondition and effects and whose semantics is based on the concept of attempt. We give its syntax, semantics, and axiomatics and discuss the relation with other proposals, in particular Belnap and Horty’s ‘branching time and agent choice’ semantics (BT+AC) and Czelakowski’s relational semantics.

Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they (...) are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems. (shrink)

Event sequences or scripts are the conceptual representations of activities in memory. Traditional views hold that events are represented in amodal networks and are retrieved by associative strategies. The embodied cognition approach holds that knowledge is grounded in perception and retrieved by mental simulation. We used a script generation task where event sequences of activities had to be produced. Activities varied in their degree of familiarity. In a regressional design we investigated whether amodal or perceptual variables best predicted (...) class='Hi'>knowledge retrieval to gain insight into the underlying representation. Retrieval depended on the familiarity of the activity. While novel activities mainly relied on perception‐based simulation and to a lesser extent on associative strategies, moderately familiar activities showed the opposite pattern, and events of familiar activities were retrieved by association alone. We conclude that amodal structures exist in all representations that become stronger with increasing frequency and finally prevail over perceptual structures. (shrink)

This paper briefly outlines some advancements in paraconsistent logics for modelling knowledge representation and reasoning. Emphasis is given on the so-called Logics of Formal Inconsistency (LFIs), a class of paraconsistent logics that formally internalize the very concept(s) of consistency and inconsistency. A couple of specialized systems based on the LFIs will be reviewed, including belief revision and probabilistic reasoning. Potential applications of those systems in the AI area of KRR are tackled by illustrating some examples that emphasizes the (...) importance of a fine-tuned treatment of consistency in modelling reputation systems, preferences, argumentation, and evidence. (shrink)

The focus of this paper will be on the problem of perceptual presence and on a solution to this problem pioneered by Kant [1781; 1783] and refined by Sellars [Sellars, 1978] and Strawson [Strawson, 1971]. The problem of perceptual presence is that of explaining how our perceptual experience of the world gives us a robust sense of the presence of objects in perception over and above those sensory aspects of the object given in perception. Objects possess other properties which are, (...) one might say, phenomenologically present even though they are admittedly sensorily absent. The general form of the solution to this problem that Kant developed seems to me to be a neglected resource in contemporary work on perceptual consciousness. Kant solves the problem of perceptual presence by appealing to that which he called the productive use of the imagination. This faculty of mind supplies schematic representations of the object of perception that explains a phenomenological sense of perceptual presence even of those features that are not, in a sense to be further clarified,. (shrink)

This contribution reflects on the current role of ontologies in terminology research and practice and their future role, especially with a view to the creation of fully digital terminographic resources. The very notion of (domain) ontology, its concept and term, is discussed, highlighting metaterminological differences and substantial ambiguities arising from the interdisciplinary contact between Ontology Engineering and Terminology. Major challenges in ontology building, e.g. subjectivity, are mentioned, also with respect to the distinction between realist and non-realist ontologies and their relevance (...) in Terminology. In addition, this contribution presents some examples of terminology resources with a distinct ontological component, showing a diversity of approaches depending on the purpose of the resource and its scope. In this context, more specific topics are addressed, such as the acquisition of ontological data and suitable formats and models for representing domain knowledge. The contribution ends with a vision of the integration of complex concept systems such as ontologies in future terminology work: here, the development of models based on terminology-specific requirements and typical users will be fundamental. (shrink)

ABSTRACT This is a continuing attempt in a series of papers [KM 93, Mur 93, Mur 96] to show how computer-represented knowledge can be arranged as elements of an effectively represented semantic (or algebraic) domain in the sense of [GS 90]. We present a direct deductive description of the domain, which was defined semantically in [KM 93], via the Scott's notion of information system. Also, the internal structure of the continuous ampliative operations coordinated with the domain's effective basis is (...) established. Though we always remain in the paradigm of the toleration of contradictory information described in [Bel 75, Bel 76], the approach in question could be extended to include domains for consistency knowledge bases. (shrink)

The notion of modularity, introduced by Noam Chomsky and developed with special emphasis on perceptual and linguistic processes by Jerry Fodor in his important book The Modularity of Mind, has provided a significant stimulus to research in cognitive science. This book presents essays in which a diverse group of philosophers, linguists, psycholinguists, and neuroscientists - including both proponents and critics of the modularity hypothesis - address general questions and specific problems related to modularity. Jay L. Garfield is Associate Professor of (...) Philosophy in the School of Communications and Cognitive Science at Hampshire College. (shrink)

. The current literature in the Artificial Intelligence and Law field reveals uncertainty concerning the potential role of deontic logic in legal knowledge representation. For instance, the Logic Programming Group at Imperial College has shown that a good deal can be achieved in this area in the absence of explicit representation of the deontic notions. This paper argues that some rather ordinary parts of the law contain structures which, if they are to be represented in logic, will (...) call for use of a reasonably sophisticated deontic logic. (shrink)

Contents: Foreword. Wolfgang BALZER and C. ULISES MOULINES: Introduction. José A. DÍEZ CALZADA: Structuralist Analysis of Theories of Fundamental Measurement. Adolfo GARCÍA DE LA SIENRA and Pedro REYES: The Theory of Finite Games in Extensive Form. Hans Joachim BURSCHEID und Horst STRUVE: The Theory of Stochastic Fairness - its Historical Development, Formulation and Justification. Wolfgang BALZER and Richard MATTESSICH: Formalizing the Basis of Accounting. Werner DIEDERICH: A Reconstruction of Marxian Economics. Bert HAMMINGA and Wolfgang BALZER: The Basic Structure of Neoclassical (...) General Equilibrium Theory. Klaus MANHART: Balance Theories: Two Reconstructions and the Problem of Intended Applications. Rainer WESTERMANN: Festinger's Theory of Cognitive Dissonance: A Structuralist Theory-Net. Rainer REISENZEIN: Wundt's Three-Dimensional Theory of Emotion. Pablo LORENZANO: Classical Genetics and the Theory-Net of Genetics. Hinne HETTEMA and Theo A.F. KUIPERS: The Formalisation of the Periodic Table. C. ULISES MOULINES: The Basic Core of Simple Equilibrium Thermodynamics. Thomas BARTELBORTH: An Axiomatization of Classical Electrodynamics. Author's Index. Subject Index. (shrink)

The information in the working environment of industrial Internet is characterized by diversity, semantics, hierarchy, and relevance. However, the existing representation methods of environmental information mostly emphasize the concepts and relationships in the environment and have an insufficient understanding of the items and relationships at the instance level. There are also some problems such as low visualization of knowledge representation, poor human-machine interaction ability, insufficient knowledge reasoning ability, and slow knowledge search speed, which cannot meet (...) the needs of intelligent and personalized service. Based on this, this paper designs a cognitive information representation model based on a knowledge graph, which combines the perceptual information of industrial robot ontology with semantic description information such as functional attributes obtained from the Internet to form a structured and logically reasoned cognitive knowledge graph including perception layer and cognition layer. Aiming at the problem that the data sources of the knowledge base for constructing the cognitive knowledge graph are wide and heterogeneous, and there are entity semantic differences and knowledge system differences among different data sources, a multimodal entity semantic fusion model based on vector features and a system fusion framework based on HowNet are designed, and the environment description information such as object semantics, attributes, relations, spatial location, and context acquired by industrial robots and their own state information are unified and standardized. The automatic representation of robot perceived information is realized, and the universality, systematicness, and intuition of robot cognitive information representation are enhanced, so that the cognition reasoning ability and knowledge retrieval efficiency of robots in the industrial Internet environment can be effectively improved. (shrink)