Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories

2012/10/19 by Thomas Eiter, Thomas Lukasiewicz, Eiter, Thomas +1
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Semantic Web and Ontologies

paper · pdf · doi:10.48550/arxiv.1212.2461

openalex publication_date 2012/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present the language m Pcal C+ for probabilistic reasoning about actions, which is a generalization of the action language cal C+ that allows to deal with probabilistic as well as nondeterministic effects of actions. We define a formal semantics of m Pcal C+ in terms of probabilistic transitions between sets of states. Using a concept of a history and its belief state, we then show how several important problems in reasoning about actions can be concisely formulated in our formalism.

Citations

• Planning and acting in partially observable stochastic domains

Cited by

• Reinforcement Learning in Partially Observable Markov Decision Processes\n using Hybrid Probabilistic Logic Programs

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