Article ID Journal Published Year Pages File Type
6853037 Artificial Intelligence 2018 93 Pages PDF
Abstract
We present a technique for automatically extracting mutual exclusion invariants from temporal planning instances. It first identifies a set of invariant templates by inspecting the lifted representation of the domain and then checks these templates against properties that assure invariance. Our technique builds on other approaches to invariant synthesis presented in the literature but departs from their limited focus on instantaneous actions by addressing temporal domains. To deal with time, we formulate invariance conditions that account for the entire temporal structure of the actions and the possible concurrent interactions between them. As a result, we construct a more comprehensive technique than previous methods, which is able to find not only invariants for temporal domains but also a broader set of invariants for sequential domains. Our experimental results provide evidence that our domain analysis is effective at identifying a more extensive set of invariants, which results in the generation of fewer multi-valued state variables. We show that, in turn, this reduction in the number of variables reflects positively on the performance of the temporal planners that use a variable/value representation.
Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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