Belief change with noisy sensing in the situation calculus

Jianbing Ma, Weiru Liu, Paul Miller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


Situation calculus has been applied widely in arti?cial intelligence to model and reason about actions and changes in dynamic systems. Since actions carried out by agents will cause constant changes of the agents’ beliefs, how to manage
these changes is a very important issue. Shapiro et al. [22] is one of the studies that considered this issue. However, in this framework, the problem of noisy sensing, which often presents in real-world applications, is not considered. As a
consequence, noisy sensing actions in this framework will lead to an agent facing inconsistent situation and subsequently the agent cannot proceed further. In this paper, we investigate how noisy sensing actions can be handled in iterated
belief change within the situation calculus formalism. We extend the framework proposed in [22] with the capability of managing noisy sensings. We demonstrate that an agent can still detect the actual situation when the ratio of noisy sensing actions vs. accurate sensing actions is limited. We prove that our framework subsumes the iterated belief change strategy in [22] when all sensing actions are accurate. Furthermore, we prove that our framework can adequately handle belief introspection, mistaken beliefs, belief revision and belief update even with noisy sensing, as done in [22] with accurate sensing actions only.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence (UAI 2011)
PublisherAUAI Press
Number of pages8
Publication statusPublished - Jul 2011
Event27th Conference on Uncertainty in Artificial Intelligence - Barcelona, Spain
Duration: 01 Jul 201101 Jul 2011


Conference27th Conference on Uncertainty in Artificial Intelligence

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