Compiling Metric Temporal Answer Set Programming
By: Arvid Becker , Pedro Cabalar , Martin Diéguez and more
Potential Business Impact:
Lets computers plan with time limits.
We develop a computational approach to Metric Answer Set Programming (ASP) to allow for expressing quantitative temporal constrains, 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.
Similar Papers
Towards Constraint Temporal Answer Set Programming
Artificial Intelligence
Helps computers understand changing things over time.
Causal Graph Recovery in Neuroimaging through Answer Set Programming
Machine Learning (CS)
Finds hidden causes even with missing data.
A framework for Conditional Reasoning in Answer Set Programming
Artificial Intelligence
Lets computers reason with "if this, then that" rules.