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Auflistung nach Schlagwort "Monte Carlo Tree Search"

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  • Zeitschriftenartikel
    CadiaPlayer: Search-Control Techniques
    (KI - Künstliche Intelligenz: Vol. 25, No. 1, 2011) Finnsson, Hilmar; Björnsson, Yngvi
    Effective search control is one of the key components of any successful simulation-based game-playing program. In General Game Playing (GGP), learning of useful search-control knowledge is a particularly challenging task because it must be done in real-time during online play. In here we describe the search-control techniques used in the 2010 version of the GGP agent CadiaPlayer, and show how they have evolved over the years to become increasingly effective and robust across a wide range of games. In particular, we present a new combined search-control scheme (RAVE/MAST/FAST) for biasing action selection. The scheme proves quite effective on a wide range of games including chess-like games, which have up until now proved quite challenging for simulation-based GGP agents.
  • Textdokument
    A Monte Carlo Tree Search Based Conflict-Driven Clause Learning SAT Solver
    (INFORMATIK 2017, 2017) Schloeter, Jens
    Most modern state-of-the-art Boolean Satisfiability (SAT) solvers are based on the Davis-Putnam-Logemann-Loveland (DPLL) algorithm and exploit techniques like unit propagation and Conflict-Driven Clause Learning. Even though this approach proved to be successful in practice and most recent publications focus on improving it, the success of the Monte Carlo Tree Search (MCTS) algorithm in other domains led to research in using it to solve SAT problems. While a MCTS-based algorithm was successfully used to solve SAT problems, a number of established SAT solving techniques like clause learning and parallelization were not included in the algorithm. Therefore this paper presents ways to combine the MCTS-based SAT solving approach with established SAT solving techniques like Conflict-Driven Clause Learning and shows that the addition of those techniques improves the performance of a plain MCTS-based SAT solving algorithm.