2024 IJCAI IJCAI 2024

Computational Aspects of Progression for Temporal Equilibrium Logic

Abstract

Temporal logic plays a crucial role in specifying and reasoning about dynamic systems, where temporal constraints and properties to be monitored are essential. Traditional approaches like LTL-monitoring assume monotonicity, which limits their applicability to scenarios involving non-monotonic temporal properties. We delve into complexity aspects of monitoring temporal specifications using non-monotonic Temporal Equilibrium Logic (TEL), a temporal extension of Answer Set Programming defined over Temporal Here and There Logic (THT) with a minimality criterion enforcing stable models. Notably, we study the complexity gap between monitoring properties in THT and TEL semantics, and the complexity of monitoring approximations based on progression, which is widely used in verification and in AI. In that, we pay particular attention to the fragment of temporal logic programs.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio