Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
(De)Composition of Situation Calculus Theories
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Logical aspects of events: quantification, sorts, composition and disjointness
AOW '05 Proceedings of the 2005 Australasian Ontology Workshop - Volume 58
A modular action description language
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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We design a representation based on the situation calculus to facilitate development, maintenance and elaboration of very large taxonomies of actions. This representation leads to more compact and modular basic action theories (BATs) for reasoning about actions than currently possible. We compare our representation with Reiter's BATs and prove that our representation inherits all useful properties of his BATs. Moreover, we show that our axioms can be more succinct, but extended Reiter's regression can still be used to solve the projection problem (this is the problem of whether a given logical expression will hold after executing a sequence of actions). We also show that our representation has significant computational advantages. For taxonomies of actions that can be represented as finitely branching trees, the regression operator can work exponentially faster with our theories than it works with Reiter's BATs. Finally, we propose general guidelines on how a taxonomy of actions can be constructed from the given set of effect axioms in a domain.