Stable Model Semantics

Stable model semantics provides a formal framework for reasoning under uncertainty and non-monotonicity, primarily within logic programming and argumentation frameworks. Current research focuses on extending its applicability to higher-order logic, integrating it with probabilistic reasoning and constraint satisfaction, and refining its computational efficiency through techniques like approximation fixpoint theory and optimized algorithms for specific semantics (e.g., admissibility-based semantics). This work has significant implications for artificial intelligence, particularly in knowledge representation, reasoning under uncertainty, and the development of more robust and explainable AI systems.