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 booklet constitutes the refereed court cases 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 conscientiously reviewed and chosen from 304 submissions. The papers are prepared in topical sections on disbursed AI and multi-agent platforms, wisdom engineering and case-based reasoning, making plans and scheduling, desktop studying and data acquisition, traditional language processing, wisdom illustration and reasoning, wisdom discovery and knowledge mining, robotics, computing device imaginative and prescient, uncertainty and fuzzy structures, genetic algorithms and neural networks, AI in schooling, and miscellaneous topics.

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

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To unify the description of software components public interfaces avoiding the use of proprietary interface description languages (IDLs), we propose the use of OWL-S (before known as DAML-S). OWLS consists of a set of ontologies that provide a vocabulary to describe services. The OWL-S profile and model descriptions provide enough information to enable automated execution based on well-defined descriptions of a service’s inputs and outputs. The use of ontologies allows sharing a semantic agreement about the services provided by and included on an agent, and not just a syntactic one as traditional IDLs.

Tihomir Gabri and Emma Norling and Gil Tidhar and Liz Sonenberg and Nicholas Howden: Multi-agent Design of a Traffic-Flow Control System. (1994) 10. Vikram Manikonda and Renato Levy and Goutam Satapathy and David Lovell and Peter Chang and Anna Teittinen: Autonomous Agents for Traffic Simulation and Control. Transportation Research Record 1774 (1–10) (2003) 11. Danko A. H. Rogier: Agent controlled traffic lights. ESIT(2000) 12. Alexander Huber and Bernd Ludwig: A Natural Language Multi-Agent System for Controlling Model Trains.

MARCS – Multi-agent Railway Control System 15 Following what has been said before, the figure shows the interaction between four distinct agent types: Supervisor. These agents must control, guide and guarantee security in traffic network for each specific area. Each area is delimited by latitude and longitude coordinates. Supervisor agents are the only ones that simultaneously belong to control and learning sub-systems. Train. This agent type exclusively belongs to control sub-system. Train agents represent correspondent interests and are responsible for train velocity control, depending on free-distances (“distance-to-go”) assigned by Supervisor agents.

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