Using Negotiation Techniques as Time-Restricted Scheduling Policies on Intelligent Agents

  • Patricia Maldonado
  • Carlos Carrascosa
  • Vicente Botti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3690)


Tasks scheduling policies for real-time systems are generally not very flexible due to the time restrictions they have to fulfill. Nowadays, research lines to apply artificial intelligence techniques to real-time systems are becoming more relevant, because they can be used to soften tasks scheduling. In this work, we present a proposal in this line. That is, to apply negotiation techniques to optimize real-time systems decisions by increasing and improving the available information to schedule the tasks of an intelligent agent working in a real-time environment. To implement our proposal, we have used an agent working in a hard real-time environment such as \({\mathcal ARTIS}\) (A Real-Time Intelligence System). Finally, we show some results obtained of including such methods in an \({\mathcal ARTIS}\) agent.


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  1. 1.
    Musliner, D.J., Hendler, J., Agrawala, A., Durfee, E., Strosnider, J., Paul, C.: The challenges of real-time ai. IEEE Computer 28 (1995)Google Scholar
  2. 2.
    Terrasa, A., García-Fornes, A., Botti, V.: Flexible real-time linux. Real-Time Systems Journal, 149–170 (2002)Google Scholar
  3. 3.
    Carrascosa, C., Fabregat, J., Terrasa, A., Botti, V.: Real-time agents: Reaction vs. deliberation. In: Second European Workshop on Multi-Agent Systems, EUMAS 2004, Barcelona – Espaa (2004)Google Scholar
  4. 4.
    Hernández, L., Botti, V., García-Fornes, A., Gonzalez, M.: A quality-based heuristic for real-time scheduling. Artificial Intelligence Research and Development. Frontiers in Artificial Intelligence Research and Development. 100, 462–473 (2003)Google Scholar
  5. 5.
    Maldonado, P., Carrascosa, C., Botti, V.: Negotiation in real-time multi-agent systems. In: IADIS International Conference – Applied Computing 2005, Algarve, Portugal, vol. II, pp. 247–254 (2005) isbn: 972-99353-6-XGoogle Scholar
  6. 6.
    FIPASpec: Fipa specifications. Foundation for Intelligence Phisycal Agents (2000), (FIPA)
  7. 7.
    Bernat, G., Burns, A., Llamosí, A.: Weakly hard real-time systems. IEEE Transaction on Computers 50, 308–321 (2001)CrossRefGoogle Scholar
  8. 8.
    Campos, A.M., García, D.: A real-time expert system architecture based on a novel dynamic task scheduling technique. In: IEEE Int. Conference on Industrial Electronics, Control and Instrumentation, IECON 2002, pp. 1893–1898 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Patricia Maldonado
    • 1
    • 2
  • Carlos Carrascosa
    • 1
  • Vicente Botti
    • 1
  1. 1.Universidad Politécnica de ValenciaValenciaEspaña
  2. 2.Universidad de MagallanesPunta ArenasChile

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