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Anthropic agency: a multiagent system for physiological processes.

Francesco Amigoni1, Marco Dini, Nicola Gatti

  • 1Artificial Intelligence and Robotics Project, Dipartimento di Elettronica e Informazione, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano, Italy. amigoni@elet.polimi.it

Artificial Intelligence in Medicine
|April 2, 2003
PubMed
Summary

Multiagent systems offer a novel approach to managing complex phenomena by decomposing them into agent-controlled portions. This study introduces the anthropic agency architecture for modeling and regulating intricate physiological processes.

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Area of Science:

  • Computational Science
  • Systems Biology
  • Artificial Intelligence

Background:

  • Complex phenomena, such as physiological processes, are challenging to model and regulate.
  • Multiagent systems provide a framework for managing complexity by distributing control among agents.
  • Overlapping control domains among agents necessitate negotiation mechanisms to resolve conflicts.

Purpose of the Study:

  • To introduce a general multiagent architecture named anthropic agency.
  • To enable the modeling and regulation of complex physiological phenomena.
  • To address the challenges in controlling systems with overlapping agent responsibilities.

Main Methods:

  • Development of a novel multiagent architecture termed anthropic agency.
  • Application of agent-based modeling principles to physiological systems.

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  • Incorporation of negotiation protocols for cooperative agent interaction.

    • Main Results:

    • The proposed anthropic agency architecture provides a structured approach to multiagent system design.
    • Demonstrated potential for modeling complex physiological interactions.
    • Highlighted the importance of negotiation in achieving coordinated agent behavior.

    Conclusions:

    • The anthropic agency offers a flexible and powerful tool for understanding and controlling complex physiological processes.
    • Multiagent systems, through cooperative and negotiated interactions, can effectively manage intricate biological systems.
    • This architecture presents a promising direction for future research in computational physiology and systems biology.