An approach to multi-agent area protection using bayes risk
| Autor: | Daniel J. Stilwell, Apoorva Shende, Matthew J. Bays |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Optimization problem
Linear programming business.industry media_common.quotation_subject Multi-agent system Machine learning computer.software_genre Task (project management) Vehicle dynamics Bayes' theorem Artificial intelligence Function (engineering) business Integer programming computer media_common Mathematics |
| Zdroj: | ICRA |
| DOI: | 10.1109/icra.2012.6224632 |
| Popis: | We introduce a novel approach to controlling the motion of a team of agents so that they jointly minimize a cost function utilizing Bayes risk. We use a particle-based approach and approximations that allow us to express the optimization problem as a mixed-integer linear program. We illustrate this approach with an area protection problem in which a team of mobile agents must intercept mobile targets before the targets enter a specified area. Bayes risk is a useful measure of performance for applications where agents must perform a classification task. By minimizing Bayes risk, agents are able to explicitly account for the cost of incorrect classification. In our application, a team of mobile agents must classify potential mobile targets as threat or safe based on the likelihood the targets will enter the specified area. The agents must also maneuver to intercept targets that are classified as threat. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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