Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs

In this paper, we present a revision strategy of revising a conditional probabilistic logic program (PLP) when new information is received (which is in the form of probabilistic formulae), through the technique of variable forgetting. We first extend the traditional forgetting method to forget a conditional event in PLPs. We then propose two revision operators to revise a PLP based on our forgetting method. By revision through forgetting, the irrelevant knowledge in the original PLP is retained according to the minimal change principle. We prove that our revision operators satisfy most of the postulates for probabilistic belief revision. A main advantage of our revision operators is that a new PLP is explicitly obtained after revision, since our revision operator performs forgetting a conditional event at the syntax level.

Authors

• Anbu Yue
• Weiru Liu

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