We present a probabilistic extension of action language. Just like is defined as a high-level notation of answer set programs for describing transition systems, the proposed language, which we call p, is defined as a high-level notation of LPMLN programs - a probabilistic extension of answer set programs. We show how probabilistic reasoning about transition systems, such as prediction, postdiction, and planning problems, as well as probabilistic diagnosis for dynamic domains, can be modeled in p and computed using an implementation of LPMLN.
ASJC Scopus subject areas
- Theoretical Computer Science
- Hardware and Architecture
- Computational Theory and Mathematics
- Artificial Intelligence