2023/12/14 by Fabio Aurelio D'Asaro, Fabio Aurelio D’Asaro, Antonis Bikakis +2
Computer Science · #AI-based Problem Solving and Planning #Bayesian Modeling and Causal Inference #Logic, Reasoning, and Knowledge
paper · doi:10.1016/j.ijar.2023.109101
openalex publication_date 2023/12/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/19
We describe a general procedure for translating Epistemic Probabilistic Event Calculus (EPEC) action language domains into Answer Set Programs (ASP), and show how the Python-driven features of the ASP solver Clingo can be used to provide efficient computation in this probabilistic setting. EPEC supports probabilistic, epistemic reasoning in domains containing narratives that include both an agent's own action executions and environmentally triggered events. Some of the agent's actions may be belief-conditioned, and some may be imperfect sensing actions that alter the strengths of previously held beliefs. We show that our ASP implementation can be used to provide query answers that fully correspond to EPEC's own declarative, Bayesian-inspired semantics.