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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.)

This ebook constitutes the refereed complaints of the ninth Ibero-American convention on synthetic 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 dispensed AI and multi-agent structures, wisdom engineering and case-based reasoning, making plans and scheduling, desktop studying and information acquisition, average language processing, wisdom illustration and reasoning, wisdom discovery and knowledge mining, robotics, laptop imaginative and prescient, uncertainty and fuzzy platforms, genetic algorithms and neural networks, AI in schooling, and miscellaneous topics.

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Extra 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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1982. 9. V. Kaptelinin, B. A. Nardi, C. Macaulay: The Activity Checklist: A tool for representing the “space” of context. Interactions, 6 (4), pp. 27-39. 1999. 10. A. N. Leontiev: Activity, Consciousness, and Personality. Prentice-Hall. 1978. 11. Pattie Maes: Modeling Adaptative Autonomous Agents. Artificial Life Journal, C. , Vol. 1, No. 1 & 2, MIT Press, 1994. 12. G. M. McGrath, L. Uden: Modelling Softer Aspects of the Software Development Process: An Activity Theory based approach. Proceedings of the HIemCSS-33 - Software Process Improvement.

3 Related Work Probably induced by increasing traffic congestion problems, multi-agent systems applied to transportation domain, usually concern road transportation. Typical approach tend to divide covered area by several traffic management agents, each one coping with decision responsibilities in a specific parcel. Often, authors decide to implement agents that represent every vehicle in circulation and the self physical infrastructure. “TraMas” [7] is a system aiming at the study of multi-agent systems viability in the road transportation domain, as well as testing applicability of different models and cooperation strategies in multi-agent systems.

Let be a frequent set with dimension Adding a constraint to force that consequent only have one item, we build a group of association rules R, with For our propose, we consider relevant association rules those with consequent type equal to “Conflict”, meaning that we are interested in identifying those items (states) that usually occur together with a “Conflict” item. For every frequent set with dimension we can identify an association rule set of dimension such that: Like referred above, filtering only two distinct items type (“Location” and “Conflict”), all identified rules during the learning process have the form: “IF Train is at with destination AND Train is at with destination AND ...

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