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Toward Computational Motivation for Multi-Agent Systems and Swarms.

Md Mohiuddin Khan1, Kathryn Kasmarik1, Michael Barlow1

  • 1School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia.

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Summary
This summary is machine-generated.

This study reviews computational motivation models for artificial agents, exploring their application in multi-agent systems and swarm intelligence. It highlights challenges and opportunities for creating advanced autonomous systems.

Keywords:
artificial intelligencecognitive developmentcuriosityexplorationintrinsic motivationmulti-agent systemsswarms

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Motivation is key to animal and human development, influencing behavior and goal-directed actions.
  • Computational models of motivation exist for individual artificial agents, but research in multi-agent settings is limited.

Purpose of the Study:

  • To review computational motivation models for artificial agents.
  • To explore the application and potential of these models in multi-agent systems and swarm intelligence.
  • To identify research challenges and future directions in this domain.

Main Methods:

  • Literature review of computational motivation models.
  • Analysis of existing models' settings, mechanisms, functions, and evaluation methods.
  • Discussion of extending individual agent motivation to multi-agent and swarm contexts.

Main Results:

  • Identified a gap in research concerning motivation theories within multi-agent and swarm intelligence settings.
  • Outlined current approaches to computational motivation for artificial agents.
  • Discussed the potential for novel system functionalities through multi-agent motivation.

Conclusions:

  • Extending computational motivation to multi-agent systems offers significant potential for advanced autonomous behaviors.
  • Further research is needed to address the unique challenges of motivation in collective artificial intelligence.
  • This review provides a foundation for future work in developing sophisticated multi-agent motivated systems.