Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An Incremental Self-Deployment Algorithm for Mobile Sensor Networks
Autonomous Robots
Maximizing Reward in a Non-Stationary Mobile Robot Environment
Autonomous Agents and Multi-Agent Systems
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Distributed Sensor Networks: A Multiagent Perspective
Distributed Sensor Networks: A Multiagent Perspective
Planning in Intelligent Systems
Planning in Intelligent Systems
The coverage problem in a wireless sensor network
Mobile Networks and Applications
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Coevolution of heterogeneous multi-robot teams
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sensor networks with mobile agents
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
IEEE Communications Magazine
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Distributed sensor networks are an attractive area for research in agent systems. This is due primarily to the level of information available in applications where sensing technology has improved dramatically. These include energy systems and area coverage where it is desirable for sensor networks to have the ability to self-organize and be robust to changes in network structure. The challenges presented when investigating distributed sensor networks for such applications include the need for small sensor packages that are still capable of making good decisions to cover areas where multiple types of information may be present. For example in energy systems, singular areas in power plants may produce several types of valuable information, such as temperature, pressure, or chemical indicators. The approach of the work presented in this paper provides agent fitness functions for use with a neuro-evolutionary algorithm to address some of these challenges. In particular, we show that for self-organization and robustness to network changes, it is more advantageous to evolve individual policies, rather than a shared policy that all sensor units utilize. Further, we show that using a difference objective approach to the decomposition of system-level fitness functions provides a better target for evolving these individual policies. This is because the difference evaluation for fitness provides a cleaner signal, while maintaining vital information from the system level that implicitly promotes coordination among individual sensor units in the network.