Using Self-Aware Agents to Analyze Public Self-Consciousness in the Iterated Prisoner's Dilemma

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. In this project we will integrate a self-awareness mechanism into an agent’s learning architecture. Specifically, we will propose (a) our framework 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 prisoner’s dilemma representing conflicts between the public good and private interests to analyze the effects of self-awareness on an agent’s individual performance and cooperative behavior. We expect our simulation results will indicate that self-aware agents that consider public self-consciousness utilize rational analysis in a manner that promotes cooperative behavior and supports faster societal movement toward stability. In addition, we also will show that a small number of selfaware agents are sufficient for improving social benefits and resolving problems associated with collective irrational behaviors.

Project ID:PB10007-2285
External Project ID:NSC100-2221-E182-058