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Many problems have a huge state space and no good heuristic to order moves so as to guide the search toward the best positions. Random games can be used to score positions and evaluate their interest. Random games can also be improved using random games to choose a move to try at each step of a game. Nested Monte-Carlo Search addresses the problem of(More)
Knowledge about forced moves enables to select a small number of moves from the set of possible moves. It is very important in complex domains where search trees have a large branching factor. Knowing forced moves drastically cuts the search trees. We propose a language and a metaprogram to create automatically the knowledge about interesting and forced(More)
We propose a method to gradually expand the moves to consider at the nodes of game search trees. The algorithm begins with an iterative deepening search using the minimal set of moves, and if the search does not succeed , iteratively widens the set of possible moves, performing a complete iterative deepening search after each widening. When designing(More)
In this paper, we are interested in the minimization of the travel cost of the traveling salesman problem with time windows. In order to do this minimization we use a Nested Rollout Policy Adaptation (NRPA) algorithm. NRPA has multiple levels and maintains the best tour at each level. It consists in learning a rollout policy at each level. We also show how(More)