The deceptive number changing game, in the absence of symmetry
International Journal of Game Theory
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Distributed Constraint Optimization for Large Teams of Mobile Sensing Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Artificial Intelligence - Special issue: Distributed constraint satisfaction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Stochastic dominance in stochastic DCOPs for risk-sensitive applications
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Concurrent forward bounding for distributed constraint optimization problems
Artificial Intelligence
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Recent studies have investigated how a team of mobile sensors can cope with real world constraints, such as uncertainty in the reward functions, dynamically appearing and disappearing targets, technology failures end changes in the environment conditions. In this study we consider an additional element, deception by an adversary, which is relevant in many (military) applications. The adversary is expected to use deception to prevent the sensor team from performing its tasks. We employ a game theoretic model to analyze the expected strategy of the adversary and find the best response. More specifically we consider that the adversary deceptively changes the importance that agents give to targets in the area. The opponent is expected to use camouflage in order to create confusion among the sensors regarding the importance of targets, and reduce the team's efficiency in target coverage. We represent a Mobile Sensor Team problem using the Distributed Constraint Optimization Problem (DCOP) framework. We propose an optimal method for the selection of a position of a single agent facing a deceptive adversary. This method serves as a heuristic for agents to select their position in a full scale problem with multiple agents in a large area. Our empirical study demonstrates the success of our model as compared with existing models in the presence of deceptions.