for dissemination) The main assumption taken in LarKC is that a distributed Semantic Web reasoning infrastructure has to go beyond current reasoning paradigms that are strictly based on logic in order to scale to the size of the current and future Web. In LarKC this paradigm shift will be obtained by fusing reasoning (in the sense of logic) with search (in the sense of information retrieval), a...

Most common sense reasoning formalisms do not account for the passage of time as the reasoning occurs, and hence are inadequate from the point of view of modeling an agent's ongoing process of reasoning. We present a modal active-logic that treats time as a valuable resource that is consumed in each step of the agent's reasoning. We provide a sound and complete characterization for this logic a...

Polynomial time complexity is the usuaìthreshold' for distinguishing the tractable from the intractable and it may seem reasonable to adopt this notion of tractability in the context of knowledge representation and reasoning. It is argued that doing so may be inappropriate in the context of common sense reasoning underlying language understanding. A more stringent criteria of tractability is pr...

An important difference between traditional AI systems and human intelligence is our ability to harness common sense knowledge gleaned from a lifetime of learning and experiences to inform our decision making and behavior. This allows humans to adapt easily to novel situations where AI fails catastrophically for lack of situation-specific rules and generalization capabilities. Common sense know...

1 Abstract We present a system for common sense reasoning based on propositional logic, the probability calculus and the concept of model-quantiication. The task of this system PIT (for Probability Induction Tool) is to deliver decisions under incomplete knowledge but to keep the necessary additional assumptions as minimal as possible. Following this task it shows non-monotonic behavior in two ...

We use common-sense reasoning to make predictions about what normally will be the case. Such reasoning is captured by using a nonmonotonic semantics selecting a set of intended models from a larger collection of models. Such a selection process, however, might fail: although a theory might have a nonempty set of non-intended models, the set of intended models might be empty. We call such a theo...

In this paper we present ongoing work on integrating qualitative and metric spatial reasoning into planning for robots. We propose a knowledge representation and reasoning technique, grounded on well-established constraint-based spatial calculi, for combining qualitative and metric knowledge and obtaining plans expressed in actionable metric terms.