Progression with probabilities in the Situation Calculus: Representation and succinctness

Daxin Liu*, Vaishak Belle*

*Corresponding author for this work

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

Abstract / Description of output

Progression in the Situation Calculus is perhaps one of the most extensively studied cases of updating logical theories over a sequence of actions. While it generally requires second-order logic, several useful first-order and tractable cases have been identified. Recently, there has been an interest in studying the progression of probabilistic knowledge bases expressed using degrees of belief on first-order formulas. However, although a few results exist, they do not provide much clarity about how this progression can be computed or represented in a feasible manner. In this paper, we address this problem for the first time. We first examine the progression of a probabilistic knowledge base (PKB) in a world-level representation; in particular, we show that such a representation is closed under progression for any local-effect actions with quantifier-free contexts. We also propose a more succinct representation of the probabilistic knowledge base, i.e. factored-representation PKB. For this type of PKB, we study the conditions for progression to remain succinct.
Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems
PublisherACM
Pages1210-1218
Number of pages9
ISBN (Print)9798400704864
DOIs
Publication statusPublished - 6 May 2024
EventThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems
- Auckland, New Zealand
Duration: 6 May 202410 May 2024
Conference number: 23
https://www.aamas2024-conference.auckland.ac.nz/

Conference

ConferenceThe 23rd International Conference on Autonomous Agents and Multi-Agent Systems
Abbreviated titleAAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/2410/05/24
Internet address

Keywords / Materials (for Non-textual outputs)

  • knowledge representation
  • probabilistic progression
  • reasoning about action

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