Temporal Answer Set Programming on Finite Traces
This work addresses the problem of efficient temporal reasoning in AI for domains like action and change, though it is incremental as it builds on existing ASP frameworks.
The paper tackles the challenge of balancing expressiveness and computational feasibility in Temporal Answer Set Programming by introducing a variation of Temporal Equilibrium Logic for finite traces, resulting in an approach that is implementable via multi-shot ASP systems and supports a rich temporal modeling language with future and past operators.
In this paper, we introduce an alternative approach to Temporal Answer Set Programming that relies on a variation of Temporal Equilibrium Logic (TEL) for finite traces. This approach allows us to even out the expressiveness of TEL over infinite traces with the computational capacity of (incremental) Answer Set Programming (ASP). Also, we argue that finite traces are more natural when reasoning about action and change. As a result, our approach is readily implementable via multi-shot ASP systems and benefits from an extension of ASP's full-fledged input language with temporal operators. This includes future as well as past operators whose combination offers a rich temporal modeling language. For computation, we identify the class of temporal logic programs and prove that it constitutes a normal form for our approach. Finally, we outline two implementations, a generic one and an extension of clingo.