We show that simple perceptual competences can emerge from an internal simulation of action effects and are thus grounded in behavior. A simulated agent learns to distinguish between dead ends and corridors without the necessity to represent these concepts in the sensory domain. Initially, the agent is only endowed with a simple value system and the means to extract low‐level features from an image. In the interaction with the environment, it acquires a visuo‐tactile forward model that allows the agent to (...) predict how the visual input is changing under its movements, and whether movements will lead to a collision. From short‐term predictions based on the forward model, the agent learns an inverse model. The inverse model in turn produces suggestions about which actions should be simulated in long‐term predictions, and long‐term predictions eventually give rise to the perceptual ability. (shrink)

In the last years, the vision of the Semantic Web fostered the interest in reasoning over large and very large sets of assertional statements in knowledge bases. Traditional tableau-based reasoning...