Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given. Key words: defaults, conjectures, explanation, prediction, abduction, dialectics, logic, nonmonotonicity, theory formation Explanation and Prediction 2 1 Introduction One way to do research i...

Probabilistic Horn abduction is a simple framework to combine probabilistic and logical reasoning into a coherent practical framework. The numbers can be consistently interpreted probabilistically, and all of the rules can be interpreted logically. The relationship between probabilistic Horn abduction and logic programming is at two levels. At the first level probabilistic Horn abduction is an extension of pure Prolog, that is useful for diagnosis and other evidential reasoning tasks. At another level, current logic programming implementation techniques can be used to efficiently implement probabilistic Horn abduction. This forms the basis of an "anytime" algorithm for estimating arbitrary conditional probabilities. The focus of this paper is on the implementation. Scholar, Canadian Institute for Advanced Research Logic Programming, Abduction and Probability 2 1 Introduction Probabilistic Horn Abduction [22, 21, 23] is a framework for logic-based abduction that incorporates proba...

this paper we introduce the class of so called stationary extensions of a default theory. Stationary extensions include, as a special case, Reiter's original default extensions but allow us to eliminate their drawbacks that were mentioned above. Every default theory \Delta has at least one stationary extension and among its extensions there always exists the least stationary extension E \Delta . The (cautious) stationary semantics S (\Delta) of a default theory \Delta, i.e., the theory consisting of sentences which are true in all stationary extensions of \Delta, is always well-defined, and, since it clearly coincides with the least stationary extension E \Delta of \Delta, it is itself a stationary extension of \Delta. The stationary semantics of default theories is always cumulatively monotonic and it can be computed by means of a natural iterative procedure. The complexity of its computation essentially coincides with the computational complexity of satisfiability tests on the underlying first order theory and therefore it does not involve any additional complexity caused by the non-monotonicity of default logic. More precisely, for default theories consisting of

The formalization of abductive reasoning is still an open question: there is no general agreement on the boundary of some basic concepts, such as preference criteria for explanations, and the extension to first order logic has not been settled. Investigating the nature of abduction outside the context of resolution based logic programming still deserves attention, in order to characterize abductive explanations without tailoring them to any fixed method of computation. In fact, resolution is surely not the best tool for facing meta-logical and proof-theoretical questions. In this work the analysis of the concepts involved in abductive reasoning is based on analytical proof systems, i.e. tableaux and Gentzen-type systems. A proof theoretical abduction method for first order classical logic is defined, based on the sequent calculus and a dual one, based on semantic tableaux. The methods are sound and complete and work for full first order logic, without requiring any preliminary reductio...

We provide a simple formulation of a framework where some extensions of logic programming with non-monotonic reasoning are treated uniformly, namely two kinds of negation and abduction. The resulting semantics is purely model-theoretic, and gives meaning to any noncontradictory abductive logic program. Moreover, it embeds and generalizes some existing semantics which deal with negation and abduction. The framework is equipped with a correct top-down proof procedure. Keywords: Programming languages, Logic programming, Non-monotonic reasoning, Negation, Abduction. Dipartimento di Informatica, Universit`a di Pisa, Corso Italia 40, Pisa, Italy. brogi@di.unipi.it y DEIS, Universit`a di Bologna, Viale Risorgimento 2, 40136 Bologna, Italy. elamma@deis.unibo.it z Dipartimento di Informatica, Universit`a di Pisa, Corso Italia 40, Pisa, Italy. paolo@di.unipi.it x DEIS, Universit`a di Ferrara, Via Saragat, 41100 Ferrara, Italy. pmello@ing.unife.it Contents 1 Introduction and Motiva...

Building smart meeting rooms requires the support of a computing system architecture. In this paper, we describe the Context Broker Architecture (CoBrA), a broker-centric agent architecture for pervasive contextaware systems. CoBrA exploits the Web Ontology Language OWL for supporting knowledge sharing and data fusion, uses logic inferences for resolving and detecting inconsistent context knowledge, and provides users with a policy language to control their private information. Central to CoBrA is an intelligent broker agent that maintains a share model of context for all agents, services, and devices in the space, and protects users’ privacy by enforcing the policy rules that they have defined. We also describe the use of CoBrA ontologies, context reasoning mechanisms, and privacy protection in a smart meeting room system called EasyMeeting.

Abstract. This paper describes SOUPA (Standard Ontology for Ubiquitous and Pervasive Applications) and the use of this ontology in building the Context Broker Architecture (CoBrA). CoBrA is a new agent architecture for supporting pervasive context-aware systems in a smart space environment. The SOUPA ontology is expressed using the Web Ontology Language OWL and includes modular component vocabularies to represent intelligent agents with associated beliefs, desire, and intentions, time, space, events, user profiles, actions, and policies for security and privacy. Central to CoBrA is an intelligent broker agent that exploits ontologies to support knowledge sharing, context reasoning, and user privacy protection. We also describe two prototype systems that we have developed to demonstrate the feasibility and the use of CoBrA. 1.

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We present a general proof theoretical methodology for default systems. Given a default theory D#, the default rules D are simply understood as restrictions on the tableaux construction of the logic. Di#erent default approaches have their own way of understanding these restrictions and executing them. For each default approach (such as Reiter, Brewka or Lukaszewicz), the allowable default extensions can be obtained from the default tableau construction. The advantage of our approach, besides being simple and neat, is in its generality: it allows for the development of a default theory for any logic with a tableau formulation, such as intuitionistic logic, linear logic or modal logic.

Logic programs with nonmonotonic negation are embedded in a general, abstract disputation-based framework for nonmonotonic logics. This formalization induces a particular semantics, which is proved to extend well-founded semantics and whose expanded expressiveness is illustrated by di#erent examples involving reasoning by cases. Moreover, we develop a formal proof procedure for skeptical reasoning in the general disputation framework. Its adaption to the logic programming context provides a goal-oriented and local proof procedure for the induced semantics.