By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)
This e-book constitutes the refereed complaints of the 20 th 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 awarded have been conscientiously reviewed and chosen from 260 submissions. The papers are equipped in topical part on brokers, bioinformatics, class, constraint delight, info mining, wisdom illustration and reasoning, studying, normal 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
1 Introduction Competition and coordination between autonomous agents are two classical and most important tasks in multiagent systems. Coordination is especially important in multi-robotic systems where a number of non-adversarial robots (but not necessarily explicitly cooperative) are aimed to accomplish a task while being limited in communication and in knowledge about principles of rationality underlying their counterparts. On the other hand, competition is a natural condition of most real life situations.
Towards Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness. to appear in ACM Transactions on Internet Technology, Vol. 7, No. 2 (2007) 15. : Attack Resistant Trust Metrics. D thesis, UC Berkeley, Berkeley, CA, USA (2003) 16. : The Pagerank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Library Technologies Project (1998) 17. : Attack-resistance of computational trust models. In Proc. of the 12th IEEE Int. ca Abstract. Agent competition and coordination are two classical and most important tasks in multiagent systems.
Revue d’Intelligence Artiﬁcielle 20(2-3) (2006) 385–412 4. : Nash convergence of gradient dynamics in general-sum games. 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.