Advances in Computer Games: 12th International Conference, by Guillaume Chaslot, Christophe Fiter, Jean-Baptiste Hoock

By Guillaume Chaslot, Christophe Fiter, Jean-Baptiste Hoock (auth.), H. Jaap van den Herik, Pieter Spronck (eds.)

This quantity constitutes the completely refereed post-conference complaints of the 12th Advances in laptop video games convention, ACG 2009, held in Pamplona, Spain, in might 2009. The 20 revised complete papers offered have been rigorously reviewed and chosen from forty-one submissions for inclusion within the ebook. the subjects addressed comprise Monte-Carlo tree seek, Bayesian modeling, selective seek, brute strength, clash solution, fixing video games, optimization, suggestion discovery, incongruity idea, and information insurance.

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Extra info for Advances in Computer Games: 12th International Conference, ACG 2009, Pamplona Spain, May 11-13, 2009. Revised Papers

Sample text

The results are given in Table 2. Surprisingly, the heavily evaluation-function based Greedy strategy was the weakest of the four. The Corrective strategy was better than the Evaluation Cut-Off and Greedy strategy. But, the Mixed strategy, a combination of Corrective and Greedy, outperformed the other ones. The latter result shows that the evaluation function can be directly used for selecting moves as done by Greedy, but not at the start of a simulation. The first moves should be highly randomized.

MCTS can be applied to any two-player, deterministic, perfect information game of finite length (but its extension to multiple players and non-deterministic games is straightforward). Its basis is the simulation of games where both the AI-controlled player and its opponents play random moves. From a single random game (where every player selects his actions randomly), very little can be learned. But from simulating a multitude of random games, a suitable strategy can be inferred. 1 we discuss the effect of a starting position, or otherwise stated the effect of the seating order Studies of MCTS in Go have shown that inclusion of domain knowledge can improve the performance significantly [17,20].

On the one hand, the task often consists of selecting the move that leads to the best results so far (exploitation). On the other hand, the less promising moves still must be tried, due to the uncertainty of the evaluation (exploration). We use the UCT (Upper Confidence Bounds applied to Trees) strategy [12], enhanced with Progressive Bias (PB [7]). UCT is easy to implement and used in many Monte-Carlo Go programs. PB is a technique to embed the domainknowledge bias into the UCT formula. UCT with PB works as follows.

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