Evolving Finite-State Machine Strategies for Protecting Resources
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Evolution of strategies for resource protection problems
Advances in evolutionary computing
Java-MaC: A Run-Time Assurance Approach for Java Programs
Formal Methods in System Design
Distributed, Physics-Based Control of Swarms of Vehicles
Autonomous Robots
Journal of Artificial Intelligence Research
Coordination and control of multi-agent dynamic systems: models and approaches
SAB'06 Proceedings of the 2nd international conference on Swarm robotics
An overview of physicomimetics
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
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In this paper, we combine two frameworks in the context of an important application. The first framework, called "artificial physics," is described in detail in a companion paper by Spears and Gordon (this conference).The purpose of artificial physics is the distributed spatial control of large collections of mobile physical agents. The agents can be composed into geometric patterns (e.g., to act as a sensing grid) by having them sense and respond to local artificial forces that are motivated by natural physics laws.The purpose of the second framework is global monitoring of the agent formations developed with artificial physics. Using only limited global information, the monitor checks that the desired geometric pattern emerges over time as expected. If there is a problem, the global monitor steers the agents to self-repair. Our combined approach of local control through artificial physics, global monitoring, and "steering" for self-repair is implemented and tested on a problem where multiple agents form a hexagonal lattice pattern.