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

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

This booklet 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 rigorously reviewed and chosen from 260 submissions. The papers are geared up in topical part on brokers, bioinformatics, class, constraint pride, information mining, wisdom illustration and reasoning, studying, normal language, and planning.

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Extra resources 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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Com Abstract. Real-time vehicle location information enables to facilitate more efficient decision-making in dynamic automotive shipment yard environment. This paper proposes a multiagent-based decentralized decision-making model for the vehicle deployment planning in a shipment yard. A multiagent architecture is designed to facilitate decentralized algorithms and coordinate different agents dynamically. The results of computational experiments show that the proposed deployment model outperforms a current deployment practice with respect to the deployment performance measures.

The first robot (player 1) is called “predator” and its goal in the game is to catch (to achieve a collision with) the second robot, called “prey”. The goal of the “prey” (player 2) is to reach a refuge where it cannot be catched. , the goal situations for both robots are opposite. , catched the prey) its reward for any action in this state is 0 and the prey, in turn, receives a negative reward of −1 for any action. On the other hand, if the prey has reached the refuge, its reward for any action in this state is 0 and the reward of the predator is −1 regardless its position and action.

Whenever the PO gives feedback, AGENTPo deletes IPk’s column from her model and adds it to {ITEMSPo} because it now has its own rating. Updated information that is, {ITEMSPo} including a pair , is propagated to personal agents of users in {TRUSTPo}. From PC’s point of view who is given this information by AGENTPo, item IPk has not been included in {ITEMSPo} before, so the similarities between IPk and items in {ITEMSPo} ∩ {ITEMSPc} cannot be included in PC’s model. Delete IP1 IO1 IO2 IO7 ...

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