Proceedings

Reasoning About Causal Knowledge in Nondeterministic Domains

Shakil M. Khan, Yves Lespérance, Maryam Rostamigiv

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4553-4561.

https://doi.org/10.24963/ijcai.2025/507

Keywords

  • Knowledge Representation and Reasoning: KRR: Causality
  • Knowledge Representation and Reasoning: KRR: Reasoning about actions
  • Knowledge Representation and Reasoning: KRR: Reasoning about knowledge and belief

Abstract

Reasoning about causality and agent causal knowledge is critical for effective decision-making and planning in multi-agent contexts. Previous work in the area generally assumes that the domain is deterministic, but in fact many agents operate in nondeterministic domains where the outcome of their actions depends on unpredictable environment reactions. In this paper, we propose a situation calculus-based framework for reasoning about causal knowledge in nondeterministic domains. In such domains, the agent may not know the environment reactions to her actions and their outcomes, and may be uncertain about which actions caused a condition to come about. But she can perform sensing actions to acquire knowledge about the state and use it to gain knowledge about causes. Our formalization recognizes sensing actions as causes of both physical and epistemic effects. We also examine how regression can be used to reason about causal knowledge.

Shakil M. Khan, Yves Lespérance, and Maryam Rostamigiv. 2025. Reasoning About Causal Knowledge in Nondeterministic Domains. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4553–4561. https://doi.org/10.24963/ijcai.2025/507
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