Knowledge representation language p-log --- a short introduction

Authors:
Michael Gelfond
Affiliations:
Texas Tech University
Venue:
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Year:
2010

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Abstract

The paper gives a short informal introduction to the knowledge representation language P-Log. The language allows natural and elaboration tolerant representation of commonsense knowledge involving logic and probabilities. The logical framework of P-Log is Answer Set Prolog which can be viewed as a significant extension of Datalog. On the probabilistic side, the authors adopt the view which understands probabilistic reasoning as commonsense reasoning about degrees of belief of a rational agent, and use causal Bayes nets as P-log probabilistic foundation. Several examples are aimed at explaining the syntax and semantics of the language and the methodology of its use.