Advances in Computer Games
About: Advances in Computer Games is an academic conference. The conference publishes majorly in the area(s): Chess endgame & Evaluation function. Over the lifetime, 159 publication(s) have been published by the conference receiving 1818 citation(s).
Topics: Chess endgame, Evaluation function, Monte Carlo tree search, Computer Go, Tree (data structure)
••01 Jan 2004
TL;DR: Two Go programs are described, Olga and Oleg, developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach, and the ever-increasing power of computers lead us to think that Monte- carlo approaches are worth considering for computer Go in the future.
Abstract: We describe two Go programs, Olga and Oleg, developed by a Monte-Carlo approach that is simpler than Bruegmann’s (1993) approach. Our method is based on Abramson (1990). We performed experiments, to assess ideas on (1) progressive pruning, (2) all moves as first heuristic, (3) temperature, (4) simulated annealing, and (5) depth-two tree search within the Monte-Carlo framework. Progressive pruning and the all moves as first heuristic are good speed-up enhancements that do not deteriorate the level of the program too much. Then, using a constant temperature is an adequate and simple heuristic that is about as good as simulated annealing. The depth-two heuristic gives deceptive results at the moment. The results of our Monte-Carlo programs against knowledge-based programs on 9x9 boards are promising. Finally, the ever-increasing power of computers lead us to think that Monte-Carlo approaches are worth considering for computer Go in the future.
••11 May 2009
TL;DR: It is shown that MCTS can be adapted successfully to multi-agent environments, and the results show that the agent has a considerable playing strength when compared to game implementation with existing heuristics.
Abstract: Games are considered important benchmark opportunities for artificial intelligence research. Modern strategic board games can typically be played by three or more people, which makes them suitable test beds for investigating multi-player strategic decision making. Monte-Carlo Tree Search (MCTS) is a recently published family of algorithms that achieved successful results with classical, two-player, perfect-information games such as Go. In this paper we apply MCTS to the multi-player, non-deterministic board game Settlers of Catan. We implemented an agent that is able to play against computer-controlled and human players. We show that MCTS can be adapted successfully to multi-agent environments, and present two approaches of providing the agent with a limited amount of domain knowledge. Our results show that the agent has a considerable playing strength when compared to game implementation with existing heuristics. So, we may conclude that MCTS is a suitable tool for achieving a strong Settlers of Catan player.
••20 Nov 2011
TL;DR: A state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go and three notable original improvements: an adaptive time control algorithm, dynamic komi, and the usage of the criticality statistic are described.
Abstract: We present a state of the art implementation of the Monte Carlo Tree Search algorithm for the game of Go. Our Pachi software is currently one of the strongest open source Go programs, competing at the top level with other programs and playing evenly against advanced human players. We describe our implementation and choice of published algorithms as well as three notable original improvements: (1) an adaptive time control algorithm, (2) dynamic komi, and (3) the usage of the criticality statistic. We also present new methods to achieve efficient scaling both in terms of multiple threads and multiple machines in a cluster.
••11 May 2009
TL;DR: This paper test the generality of Monte-Carlo Tree Search and Upper Confidence Bounds by experimenting on the game, Havannah, and shows that the same results hold, with slight differences related to the absence of clearly known patterns for the game of Havannah.
Abstract: Monte-Carlo Tree Search and Upper Confidence Bounds provided huge improvements in computer-Go. In this paper, we test the generality of the approach by experimenting on the game, Havannah, which is known for being especially difficult for computers. We show that the same results hold, with slight differences related to the absence of clearly known patterns for the game of Havannah, in spite of the fact that Havannah is more related to connection games like Hex than to territory games like Go.
••11 May 2009
TL;DR: A new exploration term, more efficient than classical UCT-like exploration terms, which combines efficiently expert rules, patterns extracted from datasets, All-Moves-As-First values, and classical online values is presented.
Abstract: We present a new exploration term, more efficient than classical UCT-like exploration terms. It combines efficiently expert rules, patterns extracted from datasets, All-Moves-As-First values, and classical online values. As this improved bandit formula does not solve several important situations (semeais, nakade) in computer Go, we present three other important improvements which are central in the recent progress of our program MoGo. We show an expert-based improvement of Monte-Carlo simulations for nakade situations; we also emphasize some limitations of this modification. We show a technique which preserves diversity in the Monte-Carlo simulation, which greatly improves the results in 19x19. Whereas the UCB-based exploration term is not efficient in MoGo, we show a new exploration term which is highly efficient in MoGo. MoGo recently won a game with handicap 7 against a 9Dan Pro player, Zhou JunXun, winner of the LG Cup 2007, and a game with handicap 6 against a 1Dan pro player, Li-Chen Chien.
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