Advances in Artificial Intelligence: 17th Conference of the by Roberto A. Flores, Robert C. Kremer (auth.), Ahmed Y. PDF

By Roberto A. Flores, Robert C. Kremer (auth.), Ahmed Y. Tawfik, Scott D. Goodwin (eds.)

ISBN-10: 3540220046

ISBN-13: 9783540220046

ISBN-10: 3540248404

ISBN-13: 9783540248408

This publication constitutes the refereed lawsuits of the seventeenth convention of the Canadian Society for Computational reviews of Intelligence, Canadian AI 2004, held in London, Ontario, Canada in may possibly 2004.

The 29 revised complete papers and 22 revised brief papers have been rigorously reviewed and chosen from a hundred and five submissions. those papers are offered including the prolonged abstracts of 14 contributions to the graduate scholars' song. the complete papers are prepared in topical sections on brokers, usual language processing, studying, constraint delight and seek, wisdom illustration and reasoning, uncertainty, and neural networks.

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Extra resources for Advances in Artificial Intelligence: 17th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, London, Ontario, Canada, May 17-19, 2004. Proceedings

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When agents got stuck in autonomous trials, they would often remain stuck. In the blending trials, if an agent became stuck, the operator was often able to free the agent. Since the operator was notified by the intervention recognition system whenever an agent became stuck, the operator was often able to free the agent in a timely manner, reducing the amount of time any 26 R. Wegner and J. Anderson particular blending agent spent immobile. In the lower obstacle coverage trials (5% and 10% obstacle coverage), agents became stuck less overall.

The effect is that the agent does the right thing in this example, which is to completely fulfill one need before attending to the other. For cross-exclusion to work, the inhibitory values must be set high enough to ensure that there is no flip-flopping of states after one need is satisfied. We determined these values through trial and error, although it would be possible to automate this process using, for instance, an evolutionary algorithm. Among humans, different people have different thresholds for hunger, thirst, and so on, thus there is no one best set of threshold values.

Thus hunger remains as the most urgent need, and so the agent stays at the food, eating until it is no longer hungry. When hunger is satisfied, it stops inhibiting thirst, causing the agent to go South for water. The same thing happens with thirst: when thirst is excited, hunger is proportionally inhibited. The effect is that the agent does the right thing in this example, which is to completely fulfill one need before attending to the other. For cross-exclusion to work, the inhibitory values must be set high enough to ensure that there is no flip-flopping of states after one need is satisfied.

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Advances in Artificial Intelligence: 17th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, London, Ontario, Canada, May 17-19, 2004. Proceedings by Roberto A. Flores, Robert C. Kremer (auth.), Ahmed Y. Tawfik, Scott D. Goodwin (eds.)


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