By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)
This e-book constitutes the refereed court cases of the 20 th convention of the Canadian Society for Computational stories of Intelligence, Canadian AI 2007, held in Montreal, Canada, in may well 2007.
The forty six revised complete papers awarded have been conscientiously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, class, constraint delight, facts mining, wisdom illustration and reasoning, studying, traditional language, and planning.
Read Online or Download Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings PDF
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Extra info for Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings
In: Proceedings of the Sixteenth Conference on Uncertainty in Artiﬁcial Intelligence (UAI’94), San Francisco, CA, Morgan Kaufman (1994) 5. : The dynamics of reinforcement learning in cooperative multiagent systems. In: Proceedings of the Fifteenth National Conference on Artiﬁcial Intelligence (AAAI’98), Menlo Park, CA, AAAI Press (1998) 6. : Multiagent reinforcement learning: Theoretical framework and an algorithm. In: Proceedings of the Fifteenth International Conference on Machine Learning (ICML’98), San Francisco, CA, Morgan Kaufmann (1998) 7.
The dynamics of learning of the last 100,000 trials in the two-robot-predatorprey game, with a 5 × 5 grid. The curves show the length of a trial as a function of the trials number, where the agents use the algorithms PHC, IGA/ModIGA and APQ. in adversarial games than pure strategies can do. For the same reasons, APQ cannot perform better than PHC in this case. But, surprisingly for us, IGA×IGA case demonstrated the longest average trial length at the end of the learning, which is somewhat unexpected, since its convergence properties are the same as for the PHC algorithm.
On the other hand, the cases APQ×APQ and IGA×IGA demonstrated the best average solutions. This can be explained by the fact that both APQ and IGA can observe the actions of their opponents, and, by so doing, to adapt better to the strategy of the opponent. Moreover, since in the two-robot-on-the-grid problem the solution is deterministic (a pair of trajectories) and APQ learns pure strategies directly, it is obvious that in that case the solution found by APQ×APQ cannot be worse than the others.
Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)