A Robust Audit Mechanism to Prevent Malicious Behaviors in Multi-robot Systems

Market-based mechanisms can be used to coordinate self-interested multi-robot systems in fully distributed environments, where by self-interested we mean that each robot agent attempts to maximize a payoff function that accounts for both the resources consumed and the contribution made by the robot. In previous work, we have studied the effect of various market rules and bidding strategies on the global performance of the multi-robot system. However, rather than use a central monitoring and enforcement mechanisms, we rely on agents to self-report their actions. This assumes that the agents act honestly. In this paper, we drop the honesty assumption, raising the possibility that agents may exaggerate their contribution in order to increase their payoff. To address the problem of such malicious behavior, we propose an audit mechanism to maintain the integrity of reported payoffs. Our algorithm extends previous work on preventing free-riding in peer-to-peer networks. Specifically, we consider locality and mobility in multi-robot systems. We show that our approach efficiently detects malicious behaviors with a high probability.