A Decentralized Shotgun Approach for Team Deception

TL;DR

This paper introduces a scalable decentralized method for team deception in multi-agent systems, enabling agents to mask their true intentions from a supervisor while ensuring task completion.

Contribution

It proposes a novel decentralized deceptive policy synthesis algorithm that minimizes divergence from reference behaviors and handles supervisor-based agent elimination scenarios.

Findings

Algorithm scales linearly with number of agents

Ensures at least one agent achieves the task with high probability

Demonstrated effectiveness in package delivery scenario

Abstract

Deception is helpful for agents masking their intentions from an observer. We consider a team of agents deceiving their supervisor. The supervisor defines nominal behavior for the agents via reference policies, but the agents share an alternate task that they can only achieve by deviating from these references. As such, the agents use deceptive policies to complete the task while ensuring that their behaviors remain plausible to the supervisor. We propose a setting with centralized deceptive policy synthesis and decentralized execution. We model each agent with a Markov decision process and constrain the agents' deceptive policies so that, with high probability, at least one agent achieves the task. We then provide an algorithm to synthesize deceptive policies that ensure the deviations of all agents are small by minimizing the worst Kullback-Leibler divergence between any agent's deceptive and reference policies. Thanks to decentralization, this algorithm scales linearly with the number of agents and also facilitates the efficient synthesis of reference policies. We then explore a more general version of the deceptive policy synthesis problem. In particular, we consider a supervisor who selects a subset of agents to eliminate based on the agents' behaviors. We give algorithms to synthesize deceptive policies so that, after the supervisor eliminates some agents, the remaining agents complete the task with high probability. We demonstrate the developed methods in a package delivery example.