Download Advances in Artificial Intelligence: 20th Conference of the by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.) PDF

By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)

This e-book constitutes the refereed court cases of the twentieth convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2007, held in Montreal, Canada, in may well 2007.

The forty six revised complete papers offered have been conscientiously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, class, constraint delight, information mining, wisdom illustration and reasoning, studying, typical language, and planning.

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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

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Additionally, the convergence properties can be held in this situation, because the agents were not able to distinguish whether the other agent was using the same algorithm or not. Figures 4-5 show the results of the experiments in the two-robot-predatorprey problem. As in the coordination problem’s case, we tested all the possible two-by-two combinations of the chosen algorithms. The curves represent average trial length of the predator agent. For the same reasons as sated above, we did not present the curves for the prey agent.

Young [12] proved the convergence of Adaptive Play to an equilibrium when played in self-play for a big class of games such as the coordination and common interest games. Adaptive Play Q -learning (APQ) is an extension of Young’s algorithm to the multi-state stochastic game context. To do that, the usual single-agent Q-learning update rule [13] was modified to consider multiple agents as follows: Qj (s, a) ← (1 − α)Qj (s, a) + α[Rj (s, a) + γ ˆ ) ∪ {π j (s )})] max U j (Π(s aj ∈π j (s ) where j is an agent, a is a joint action played by the agents in state s ∈ S, Qj (s, a) is the current value for player j of playing the joint action a in state s, Rj (s, a) is the immediate reward the player j receives if the joint action a is played in the state s and π j (s ) are all possible pure strategies that are available for player j in state s .

On Trust Management, LNCS, Vol. 2995 (2004) 251-265 8. : Trust-aware Collaborative Filtering for Recommender Systems. In Proc. of Int. Conf. on Cooperative Information Systems (2004) 9. : An Algorithmic Framework for Performing Collaborative Filtering. In Proc. of the 22nd ACM SIGIR Conf. on Research and Development in Information Retrieval (1999) 10. : Programming and Deploying Java Mobile Agents with Aglets. Addison-Wesley (1998) 11. Jung, J. : Visualizing recommendation flow on social networks.

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