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Residual-Guided Look-Ahead in AND/OR Search for Graphical Models
We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are di...
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Published in: | The Journal of artificial intelligence research 2017-10, Vol.60, p.287-346 |
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container_title | The Journal of artificial intelligence research |
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creator | Lam, William Kask, Kalev Larrosa, Javier Dechter, Rina |
description | We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the mini-bucket heuristic. We present and analyze methods for compiling the local bucket-errors (exactly and approximately) and show that they can be used to yield an effective tool for balancing look-ahead overhead during search. This can be especially instrumental when memory is restricted, accommodating the generation of only weak compiled heuristics. We illustrate the impact of the proposed schemes in an extensive empirical evaluation for both finding exact solutions and anytime suboptimal solutions. |
doi_str_mv | 10.1613/jair.5475 |
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subjects | Artificial intelligence Combinatorial analysis Combinatorial optimization Empirical analysis Exact solutions Graphic methods Heuristic Informàtica Informàtica teòrica Luminance distribution Mètodes gràfics Optimització combinatòria Optimization Searching Àrees temàtiques de la UPC |
title | Residual-Guided Look-Ahead in AND/OR Search for Graphical Models |
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