Implementing Metric Temporal Answer Set Programming
This work addresses scalability issues in temporal reasoning for researchers and practitioners in computational logic and AI planning, but it is incremental as it builds on existing ASP extensions.
The authors tackled the challenge of maintaining scalability in Metric Answer Set Programming (ASP) when handling fine-grained temporal constraints like durations and deadlines, by decoupling metric ASP from time granularity using extensions with difference constraints, resulting in a solution unaffected by time precision.
We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constraints, like durations and deadlines. A central challenge is to maintain scalability when dealing with fine-grained timing constraints, which can significantly exacerbate ASP's grounding bottleneck. To address this issue, we leverage extensions of ASP with difference constraints, a simplified form of linear constraints, to handle time-related aspects externally. Our approach effectively decouples metric ASP from the granularity of time, resulting in a solution that is unaffected by time precision.