AIJan 16, 2014

On Action Theory Change

arXiv:1401.3835v117 citations
Originality Incremental advance
AI Analysis

This work addresses the need for revision methods in action theories for knowledge engineers, but it is incremental as it builds on existing semantics and modularity principles.

The paper tackles the problem of updating action domain descriptions in multimodal logic when new information arises, by proposing more robust contraction operators based on distance between Kripke-models and providing algorithms for syntactical contraction with correctness proofs. It also establishes AGM-like postulates for action theory contraction and extends the approach to revision.

As historically acknowledged in the Reasoning about Actions and Change community, intuitiveness of a logical domain description cannot be fully automated. Moreover, like any other logical theory, action theories may also evolve, and thus knowledge engineers need revision methods to help in accommodating new incoming information about the behavior of actions in an adequate manner. The present work is about changing action domain descriptions in multimodal logic. Its contribution is threefold: first we revisit the semantics of action theory contraction proposed in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness with respect to our semantics for those action theories that satisfy a principle of modularity investigated in previous work. Since modularity can be ensured for every action theory and, as we show here, needs to be computed at most once during the evolution of a domain description, it does not represent a limitation at all to the method here studied. Finally we state AGM-like postulates for action theory contraction and assess the behavior of our operators with respect to them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction.

Foundations

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