Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey
Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey
Paolo Morettin, Pedro Zuidberg Dos Martires, Samuel Kolb, Andrea Passerini
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Survey Track. Pages 4533-4542.
https://doi.org/10.24963/ijcai.2021/617
Real world decision making problems often involve both discrete and continuous variables and require a combination of probabilistic and deterministic knowledge. Stimulated by recent advances in automated reasoning technology, hybrid (discrete+continuous) probabilistic reasoning with constraints has emerged as a lively and fast growing research field. In this paper we provide a survey of existing techniques for hybrid probabilistic inference with logic and algebraic constraints. We leverage weighted model integration as a unifying formalism and discuss the different paradigms that have been used as well as the expressivity-efficiency trade-offs that have been investigated. We conclude the survey with a comparative overview of existing implementations and a critical discussion of open challenges and promising research directions.
Keywords:
Uncertainty in AI: General
Knowledge representation and reasoning: General
Constraints and SAT: General