By Maarten P. D. Schadd, Mark H. M. Winands, H. Jaap van den Herik (auth.), H. Jaap van den Herik, Xinhe Xu, Zongmin Ma, Mark H. M. Winands (eds.)
This e-book constitutes the refereed complaints of the sixth foreign convention on pcs and video games, CG 2008, held in Beijing, China, in September/October 2008 co-located with the thirteenth laptop Olympiad and the sixteenth international Computer-Chess Championship.
The 24 revised complete papers offered have been rigorously reviewed and chosen from forty submissions. The papers hide all facets of synthetic intelligence in computer-game enjoying facing many alternative study subject matters, corresponding to cognition, combinatorial video game thought, seek, wisdom illustration, and optimization.
Read Online or Download Computers and Games: 6th International Conference, CG 2008, Beijing, China, September 29 - October 1, 2008. Proceedings PDF
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Additional resources for Computers and Games: 6th International Conference, CG 2008, Beijing, China, September 29 - October 1, 2008. Proceedings
We use the game Lines of Action (LOA) as a testbed. It is an ideal candidate because its intricacies are less complicated than those of chess. So, we can focus on the sudden-death property. Furthermore, because LOA was used as a domain for various other AI techniques [5,12,20], the level of the state-of-the-art LOA programs is high, allowing us to look at how MCTS approaches perform against increasingly stronger evaluation functions. Moreover, the search engine of a LOA program is quite similar to the one of a chess program.
Thus, we may conclude that MCTS-Solver is a genuine improvement, signiﬁcantly enhancing MCTS. Although MCTS-Solver is still lacking behind the best αβ-based program, we view this work as one step towards that goal of making simulationbased approaches work in a wider variety of games. For these methods, to be able to handle proven outcomes is one essential step to make. With continuing improvements it is not unlikely that in the not so distant future enhanced simulation-based approaches may become a competitive alternative to αβ search in games dominated by the latter so far.
By adding these basic UCT features to INVADERMC we have created a program that outperforms INVADER. This version of INVADERMC is able to beat INVADER approximately 60% of the time. We now describe further enhancements that move us closer to our best version that achieves a win rate of 80% against INVADER. J. 5 Choosing the Proper Evaluation Function As is the case with most game-playing algorithms, the speed of the evaluation function is critical. Not only does an evaluation take place with every simulation, but every time a UCT tree node is expanded there will typically be on the order of 1,000 evaluations to perform corresponding to the average number of legal moves from a position.
Computers and Games: 6th International Conference, CG 2008, Beijing, China, September 29 - October 1, 2008. Proceedings by Maarten P. D. Schadd, Mark H. M. Winands, H. Jaap van den Herik (auth.), H. Jaap van den Herik, Xinhe Xu, Zongmin Ma, Mark H. M. Winands (eds.)