Situation Calculus by Term Rewriting
This work addresses planning problems in AI by offering a more flexible representation for situations and actions, though it appears incremental relative to existing approaches like the fluent calculus.
The paper introduces a version of the situation calculus where situations are represented as first-order terms, with fluents computed from term structure and actions as rewrite rules, enabling efficient planning via completion methods for bidirectional actions. It provides examples and construction methods, noting flexibility and subterm utilization compared to the fluent calculus.
A version of the situation calculus in which situations are represented as first-order terms is presented. Fluents can be computed from the term structure, and actions on the situations correspond to rewrite rules on the terms. Actions that only depend on or influence a subset of the fluents can be described as rewrite rules that operate on subterms of the terms in some cases. If actions are bidirectional then efficient completion methods can be used to solve planning problems. This representation for situations and actions is most similar to the fluent calculus of Thielscher \cite{Thielscher98}, except that this representation is more flexible and more use is made of the subterm structure. Some examples are given, and a few general methods for constructing such sets of rewrite rules are presented. This paper was submitted to FSCD 2020 on December 23, 2019.