A Proposal for Intelligent Agents with Adjusting Self-Reputation Capability for Preventing Fraud in Multi-Agent Societies

Abstract

Intelligent agents are increasingly being used for tasks such as e-commerce auto-bidding. Cooperation and defection challenges commonly found in multi-agent systems make the design and construction of a reliable reputation mechanism an important research and engineering goal. Here we describe our proposal for an intelligent agent model that addresses learning issues in terms of self-reputation. The agents in our proposed model are capable of evaluating their behaviors based on a mix of public and private interest considerations, and of testing various solutions aimed at mitigating self-discrepancy and meeting social standards. Our results indicate promise for integrating a self-reputation mechanism into a self-learning framework in a manner that encourages the early emergence of social cooperation among multiple agents and specific cooperation with individual agents.