Using self-aware agents to analyze public self-consciousness in the iterated prisoner's dilemma

  • Authors:
  • Chung-Yuan Huang;Sheng-Wen Wang;Chuen-Tsai Sun

  • Affiliations:
  • Department of Computer Science and Information Engineering,Chang Gung University, Taiwan, Research Center for Emerging Viral Infections, ChangGung University, Taiwan;Department of Computer Science, National Chiao TungUniversity, Taiwan;Department of Computer Science, National Chiao TungUniversity, Taiwan

  • Venue:
  • Simulation
  • Year:
  • 2011

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Abstract

Self-aware individuals are more likely to consider whether their actions are appropriate in terms of public self-consciousness, and to use that information to execute behaviors that match external standards and/or expectations. The learning concepts through which individuals monitor themselves have generally been overlooked by artificial intelligence researchers. Here we report on our attempt to integrate a self-awareness mechanism into an agentâ聙聶s learning architecture. Specifically, we describe (a) our proposal for a self-aware agent model that includes an external learning mechanism and internal cognitive capacity with super-ego and ego characteristics; and (b) our application of a version of the iterated prisonerâ聙聶s dilemma representing conflicts between the public good and private interests to analyze the effects of self-awareness on an agentâ聙聶s individual performance and cooperative behavior. Our results indicate that self-aware agents who consider public self-consciousness utilize rational analysis in a manner that promotes cooperative behavior and supports faster societal movement toward stability. We found that a small number of self-aware agents are sufficient for improving social benefits and resolving problems associated with collective irrational behaviors.