Reformulating the Situation Calculus and the Event Calculus in the General Theory of Stable Models and in Answer Set Programming
This work provides a computational bridge between classical action formalisms and answer set programming, potentially benefiting AI researchers in nonmonotonic reasoning and automated planning.
The authors reformulated the situation calculus and event calculus using the general theory of stable models, enabling their translation into answer set programs for computation with efficient solvers.
Circumscription and logic programs under the stable model semantics are two well-known nonmonotonic formalisms. The former has served as a basis of classical logic based action formalisms, such as the situation calculus, the event calculus and temporal action logics; the latter has served as a basis of a family of action languages, such as language A and several of its descendants. Based on the discovery that circumscription and the stable model semantics coincide on a class of canonical formulas, we reformulate the situation calculus and the event calculus in the general theory of stable models. We also present a translation that turns the reformulations further into answer set programs, so that efficient answer set solvers can be applied to compute the situation calculus and the event calculus.