Improving Heuristics Through Relaxed Search - An Analysis of TP4 and HSP in the 2004 Planning Competition
The hm admissible heuristics for (sequential and temporal) regression planning are defined by a parameterized relaxation of the optimal cost function in the regression search space, where the parameter m offers a trade-off between the accuracy and computational cost of the heuristic. Existing methods for computing the hm heuristic require time exponential in m, limiting them to small values (m ≤ 2). The hm heuristic can also be viewed as the optimal cost function in a relaxation of the search...[Show more]
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|Source:||Journal of Artificial Intelligence Research|
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