Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic

Reasoning about Beliefs and Meta-Beliefs by Regression in an Expressive Probabilistic Action Logic

Daxin Liu, Gerhard Lakemeyer

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 1951-1958. https://doi.org/10.24963/ijcai.2021/269

In a recent paper Belle and Lakemeyer proposed the logic DS, a probabilistic extension of a modal variant of the situation calculus with a model of belief based on weighted possible worlds. Among other things, they were able to precisely capture the beliefs of a probabilistic knowledge base in terms of the concept of only-believing. While intuitively appealing, the logic has a number of shortcomings. Perhaps the most severe is the limited expressiveness in that degrees of belief are restricted to constant rational numbers, which makes it impossible to express arbitrary belief distributions. In this paper we will address this and other shortcomings by extending the language and modifying the semantics of belief and only-believing. Among other things, we will show that belief retains many but not all of the properties of DS. Moreover, it turns out that only-believing arbitrary sentences, including those mentioning belief, is uniquely satisfiable in our logic. For an interesting class of knowledge bases we also show how reasoning about beliefs and meta-beliefs after performing noisy actions and sensing can be reduced to reasoning about the initial beliefs of an agent using a form of regression.
Keywords:
Knowledge Representation and Reasoning: Action, Change and Causality
Knowledge Representation and Reasoning: Reasoning about Knowledge and Belief
Uncertainty in AI: Uncertainty Representations