Abstract
The use of computational complexity in planning, and in AI in general, has always been a disputed topic. A major problem with ordinary worst-case analyses is that they do not provide any quantitative information: they do not tell us much about the running time of concrete algorithms, nor do they tell us much about the running time of optimal algorithms. We address problems like this by presenting results based on the exponential time hypothesis (ETH), which is a widely accepted hypothesis concerning the time complexity of 3-SAT. By using this approach, we provide, for instance, almost matching upper and lower bounds onthe time complexity of propositional planning.
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Meysam Aghighi and Simon Ståhlberg are partially supported by the National Graduate School in Computer Science (CUGS), Sweden. Christer Bäckström is partially supported by the Swedish Research Council (VR) under grant 621-2014-4086.
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Aghighi, M., Bäckström, C., Jonsson, P. et al. Refining complexity analyses in planning by exploiting the exponential time hypothesis. Ann Math Artif Intell 78, 157–175 (2016). https://doi.org/10.1007/s10472-016-9521-y
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DOI: https://doi.org/10.1007/s10472-016-9521-y