Probabilistic Logic Programming under Answer Sets Semantics

<正> Although traditional logic programming languages provide powerful tools for knowledge representation, they cannot deal with uncertainty information (e. g. probabilistic information). In this paper, we propose a probabilistic logic programming language by introduce probability into a general logic programming language. The work combines 4-valued logic with probability. Conditional probability can be easily represented in a probabilistic logic program. The semantics of such a probabilistic logic program is base on the method of stable model which can generate more precise answer for a query.