# A Translation of Probabilistic Event Calculus into Markov Decision Processes

arXiv: https://arxiv.org/abs/2507.12989 · HTML: https://mlsanity.com/paper/2507.12989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Discount Factor as a Regularizer in Reinforcement Learning](https://mlsanity.com/paper/2007.02040/) (2020)
- [Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning, Extended version](https://mlsanity.com/paper/1904.10090/) (2019)
- [Online Learning Probabilistic Event Calculus Theories in Answer Set Programming](https://mlsanity.com/paper/2104.00158/) (2021)
- [Foundations for a Probabilistic Event Calculus](https://mlsanity.com/paper/1703.06815/) (2017)
- [An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems](https://mlsanity.com/paper/2209.13043/) (2022)

## Abstract (arXiv)

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.