Reasoning about Actual Causes in Nondeterministic Domains -- Extended Version
This work addresses a gap in formalizing rationality for AI agents in realistic, nondeterministic settings, though it appears incremental as it builds on prior preliminary work.
The paper tackles the problem of reasoning about actual causes in nondeterministic domains, where agents lack control or knowledge of environmental choices, by formalizing notions of 'Certainly Causes' and 'Possibly Causes' and extending regression in the situation calculus to reason about these causes.
Reasoning about the causes behind observations is crucial to the formalization of rationality. While extensive research has been conducted on root cause analysis, most studies have predominantly focused on deterministic settings. In this paper, we investigate causation in more realistic nondeterministic domains, where the agent does not have any control on and may not know the choices that are made by the environment. We build on recent preliminary work on actual causation in the nondeterministic situation calculus to formalize more sophisticated forms of reasoning about actual causes in such domains. We investigate the notions of ``Certainly Causes'' and ``Possibly Causes'' that enable the representation of actual cause for agent actions in these domains. We then show how regression in the situation calculus can be extended to reason about such notions of actual causes.