arXiv:2605.29965v1 Announce Type: new Abstract: The development of temporal extensions of Answer Set Programming (ASP) has led to the emergence of non-monotonic linear-time (TEL), dynamic (DEL), and metric (MEL) temporal equilibrium logics. However, the inherent rigidity of highly optimized ASP systems often hinders the rapid exploration and implementation of alternative logical designs. In this work, we propose a flexible meta-programming framework that operationalizes the semantics of varied temporal logics through a unified, declarative framework. Our approach extends standard ASP meta-prog
The increasing complexity of temporal logic programming demands more flexible and rapid development environments, which meta-programming addresses directly.
This development could significantly accelerate the research and deployment of advanced AI systems that require sophisticated temporal reasoning, particularly in areas like autonomous agents.
The ability to more easily design and experiment with diverse temporal logics will reduce the rigidity of existing ASP systems, fostering innovation in AI reasoning.
- · AI researchers
- · Developers of autonomous systems
- · Logicians
- · Software tooling companies
- · Legacy rigid logic programming systems
- · Slower-moving AI research labs
More efficient and versatile development of temporal AI systems becomes possible.
This framework could lead to breakthroughs in areas requiring complex sequence understanding and dynamic planning for AI agents.
The democratization of advanced temporal reasoning tools could accelerate the adoption of sophisticated autonomous AI in various industries.
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Read at arXiv cs.AI