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.
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