Solving Graph Optimization Problems in a Framework for Monte-Carlo Search
DOI:
https://doi.org/10.1609/socs.v8i1.18419Abstract
In this paper we solve fundamental graph optimization problems like Maximum Clique and Minimum Coloring with recent advances of Monte-Carlo Search. The optimization problems are implemented as single-agent games in a generic state-space search framework, roughly comparable to what is encoded in PDDL for an action planner.
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Published
2021-09-01
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Extended Abstracts