A deductive solution for plan generation
New Generation Computing
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
Space Complexity in Propositional Calculus
SIAM Journal on Computing
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Anytime coordination using separable bilinear programs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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To appropriately configure agents so as to avoid resource exhaustion, it is necessary to determine the minimum resource (time & memory) requirements necessary to solve reasoning problems. In this paper we show how the problem of reasoning under bounded resources can be recast as a planning problem. Focusing on propositional reasoning, we propose different recasting styles, which are equally interesting, since they require solving different classes of planning problems, and allow representing different reasoner architectures. We implement our approach by automatically encoding problems for the MBP planner. Our experimental results demonstrate that even simple problems can give rise to non-trivial (and often counter intuitive) time and memory saving strategies.