A Translation of Probabilistic Event Calculus into Markov Decision Processes
This work addresses a gap in logical frameworks for AI researchers, enabling goal-directed reasoning in probabilistic narrative domains, though it is incremental as it builds on existing PEC and MDP methods.
The paper tackled the lack of goal-directed reasoning in Probabilistic Event Calculus (PEC) by developing a formal translation of PEC domains into Markov Decision Processes (MDPs), enabling the application of MDP algorithms to PEC's interpretable narrative domains for tasks like temporal reasoning and planning.
Probabilistic Event Calculus (PEC) is a logical framework for reasoning about actions and their effects in uncertain environments, which enables the representation of probabilistic narratives and computation of temporal projections. The PEC formalism offers significant advantages in interpretability and expressiveness for narrative reasoning. However, it lacks mechanisms for goal-directed reasoning. This paper bridges this gap by developing a formal translation of PEC domains into Markov Decision Processes (MDPs), introducing the concept of "action-taking situations" to preserve PEC's flexible action semantics. The resulting PEC-MDP formalism enables the extensive collection of algorithms and theoretical tools developed for MDPs to be applied to PEC's interpretable narrative domains. We demonstrate how the translation supports both temporal reasoning tasks and objective-driven planning, with methods for mapping learned policies back into human-readable PEC representations, maintaining interpretability while extending PEC's capabilities.