Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories
This work addresses the challenge of probabilistic action reasoning in AI, presenting a formal language extension that is incremental in nature.
The authors tackled the problem of reasoning about actions with probabilistic and nondeterministic effects by introducing the language P C+, a generalization of C+, and defined its formal semantics using probabilistic transitions between states. They demonstrated that key problems in action reasoning can be concisely formulated using this formalism, though no concrete numerical results were provided.
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.