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Probabilistic Logic Programming under Answer Sets Semantics
作者姓名:王洁  鞠实儿
作者单位:中山大学逻辑与认知研究所 博士(王洁),中山大学逻辑与认知研究所 教授博士生导师(鞠实儿)
摘    要: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.

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