Distributional logic programming for Bayesian knowledge representation

Logic Programming for Knowledge Representation

Logic Programming and Knowledge Representation

In this paper, we review recent work aimed at the application of declarative logic programming to knowledge representation in arti cial intelligence. We consider extensions of the language of de nite logic programs by classical (strong) negation, disjunction, and some modal operators and show how each of the added features extends the representational power of the language. We also discuss exte...

Knowledge Representation and Logic Programming

This issue focuses on several important themes related to research on logic programming and knowledge representation. Papers published in this volume are selected from those presented at LPNMR99—5th International Conference on Logic Programming and Non-Monotonic Reasoning, held in 1999 in El Paso, Texas. These papers are significant extensions of the respective versions presented at the confere...

Logic, Knowledge Representation, and Bayesian Decision Theory

In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks for reasoning that emphasize different aspects of intelligent reasoning. Belief networks (Bayesian networks) are representations of independence that form the basis for understanding much of the recent work on reason...

A Methodology for Knowledge Representation in Defeasible Logic Programming∗

Humans have always been intrigued by their ability to reason. We have constantly attempted to emulate this process, trying almost everything, from physiological explanations, to sociological accounts. The approach with possibly the longest tradition conceives this process as a mere manipulation of symbols. Yet, symbolic reasoning cannot be applied directly over the problem at hand: we require t...