Decentralized Opportunistic Navigation Strategies for Multi-agent Systems in the Presence of an Adversary

We investigate a jamming attack on the communication network of a multi-agent system in a formation. We propose a communication and motion model for the agents. The communication model provides a relation in the spatial domain for effective jamming by an intruder. We formulate the problem as a zero-sum pursuit-evasion game. In our earlier work we used Isaacs’ approach to obtain motion strategies for a network of agents to evade the jamming attack. In this work, we imagine a scenario in which each agent has a knowledge about the value function under perfect state information, beforehand. Due to lack of information about all the agents in the team each agent is constrained to make a local decision based on the information about his neighbors. We propose online algorithms under decentralized information patterns which converge for each player. We propose approximation algorithms for the agents, based on averaging and provide some bounds on their performance.