By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)
This e-book constitutes the refereed complaints of the twentieth convention of the Canadian Society for Computational reviews of Intelligence, Canadian AI 2007, held in Montreal, Canada, in may possibly 2007.
The forty six revised complete papers provided have been rigorously reviewed and chosen from 260 submissions. The papers are equipped in topical part on brokers, bioinformatics, category, constraint pride, facts mining, wisdom illustration and reasoning, studying, common language, and planning.
Read 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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Additional 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
They can be conventionally divided onto three groups: (1) Opponent Modelling algorithms [3,5], (2) Policy Gradient based algorithms [2,4] and Adaptivity Modelling algorithms [9,10,11]. Although the algorithms of the third group are very interesting and empirically shown to have several attractive properties, such as exploiting their opponents in adversarial games [10,11] and converging to a solution maximizing welfare of both players in non-adversarial two-player matrix games , there are still no theoretical proofs of their correctness, while in the ﬁrst two groups there are algorithms that were formally proven to have such properties as rationality and convergence.
So, we see, that this game is strictly competitive. We set the following values of the other parameters of the model. 65. These values equalize the chances of winning of both predator and prey, as it was determined in self-play (when both predator and prey used the same learning algorithms). 95. The conﬁguration of the grid, the start cells and the refuge cell for the prey are depicted in Figure 1(b). , IGA versus IGA, IGA versus PHC, and so on) Competition and Coordination in Stochastic Games 33 in the both environments presented in Figure 1.
The adaptive learning agents, on the contrary, make no assumptions about their opponents’ rationality and learning capabilities, and about the solution type they are searching. Adaptive agents are adapting to their opponents and a solution is found as an emerging result of this adaptation. Among adaptive algorithms, the most outstanding and theoretically sound ones are Inﬁnitesimal Gradient Ascent (IGA) , Policy Hill-Climbing (PHC)  and Adaptive Play Q -learning (APQ) . These algorithms were empirically tested by their respective authors on the diﬀerent test benches.
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.)