Bridging Uncertainty and Optimization in Probabilistic ASP

Probabilistic Answer Set Programming (PASP) combines the expressiveness of Answer Set Programming with probabilistic reasoning, providing a powerful framework for representing uncertain knowledge. However, current PASP semantics do not support optimization criteria, limiting their applicability in real-world decision-making scenarios where uncertainty and optimization naturally coexist. In the paper entitled “A Novel Framework for Reasoning over Optimization Problems in Probabilistic Answer Set Programming” and published in the proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning (KR2025), we introduced Optimal Probabilistic Answer Set Programming (OptPASP), an extension of PASP that integrates weak constraints into the probabilistic framework. In this discussion paper, we overview the proposed framework, highlight its modeling capabilities, discuss its computational complexity, and outline promising research directions.