Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories

Abstract

We present the language {m P}{cal 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 P}{cal 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.

Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)

References in corpus (1)

• Causes and Explanations: A Structural-Model Approach. Part II: Explanations

Cited by in corpus (2)

• Reinforcement Learning in Partially Observable Markov Decision Processes using Hybrid Probabilistic Logic Programs
• Bridging the Gap between Reinforcement Learning and Knowledge Representation: A Logical Off- and On-Policy Framework