Hybrid Probabilistic Inference with Logical and Algebraic Constraints: A Survey
Abstract
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.
Cite
Text
Morettin et al. "Hybrid Probabilistic Inference with Logical and Algebraic Constraints: A Survey." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/617Markdown
[Morettin et al. "Hybrid Probabilistic Inference with Logical and Algebraic Constraints: A Survey." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/morettin2021ijcai-hybrid/) doi:10.24963/IJCAI.2021/617BibTeX
@inproceedings{morettin2021ijcai-hybrid,
title = {{Hybrid Probabilistic Inference with Logical and Algebraic Constraints: A Survey}},
author = {Morettin, Paolo and Dos Martires, Pedro Zuidberg and Kolb, Samuel and Passerini, Andrea},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4533-4542},
doi = {10.24963/IJCAI.2021/617},
url = {https://mlanthology.org/ijcai/2021/morettin2021ijcai-hybrid/}
}