Download e-book for kindle: Advances in Artificial Intelligence – IBERAMIA 2004: 9th by Rubén Fuentes, Jorge J. Gómez-Sanz, Juan Pavón (auth.),

By Rubén Fuentes, Jorge J. Gómez-Sanz, Juan Pavón (auth.), Christian Lemaître, Carlos A. Reyes, Jesús A. González (eds.)

ISBN-10: 3540238069

ISBN-13: 9783540238065

ISBN-10: 3540304983

ISBN-13: 9783540304982

This publication constitutes the refereed lawsuits of the ninth Ibero-American convention on man made Intelligence, IBERAMIA 2004, held in Puebla, Mexico in November 2004.

The ninety seven revised complete papers provided have been rigorously reviewed and chosen from 304 submissions. The papers are equipped in topical sections on disbursed AI and multi-agent structures, wisdom engineering and case-based reasoning, making plans and scheduling, computing device studying and information acquisition, common language processing, wisdom illustration and reasoning, wisdom discovery and information mining, robotics, desktop imaginative and prescient, uncertainty and fuzzy platforms, genetic algorithms and neural networks, AI in schooling, and miscellaneous topics.

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Additional resources for Advances in Artificial Intelligence – IBERAMIA 2004: 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004. Proceedings

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0) move respectively to and it will occur one traffic conflict at vertex 0 Repeating simulation process, we observe that demand to reduce his optimal velocity, being this action enough to avoid the conflict at 5 Conclusions and Work in Progress Experimental results allow us to conclude that, in specific cases, MARCS performance has been improved by applying learning system’s inferred rules. Extending the learning process perspective, it is our intention to apply it to actions performed by the system in result of conflict anticipations.

Station agents objectives consist in administrate platforms, giving orders for trains arrivals and departures and providing useful users (passengers in train stations) information. Learning. This agent type belong to “Learning” sub-system. Learning agents task consists in asking for control agent registry log, analyze it, to identify possible existent meaningful patterns to infer possible rules that can optimize traffic fluidity. 1 Control Sub-system Control sub-system main objective consists of providing secure and efficient routing for all trains while preventing crash situations and maximizing traffic fluidity.

To improve this situation, there is currently an increasing amount of research on the different types of congestion control on the networks [1]. These controls can be implemented using different artificial intelligence mechanisms, such as fuzzy logic [2, 3] or agent systems [4], and it is common to combine these mechanisms [5]. Videoconferencing services are becoming more available on the Internet and their uses cover a large range of areas [6, 7]. Videoconferencing specifically requires a set * This work is partially supported by the Spanish Ministry for Science and Technology within the following projects: “A Multi-Agent System Architecture for the automatic and dynamic Implementation of security protocols (TIC2001-3376)” and “Open System of Multimedia Digital Presentation in Real Time (TIC2000-1734-C03-02)”.

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Advances in Artificial Intelligence – IBERAMIA 2004: 9th Ibero-American Conference on AI, Puebla, Mexico, November 22-26, 2004. Proceedings by Rubén Fuentes, Jorge J. Gómez-Sanz, Juan Pavón (auth.), Christian Lemaître, Carlos A. Reyes, Jesús A. González (eds.)


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