Case-Based Parameter Selection for Plans: Coordinating Autonomous Vehicle Teams
Executing complex plans for coordinating the behaviors of multiple heterogeneous agents often requires setting several parameters. For example, we are developing a decision aid for deploying a set of autonomous vehicles to perform situation assessment in a disaster relief operation. Our system, the...
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| Published in | Case-Based Reasoning Research and Development pp. 32 - 47 |
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| Main Authors | , , |
| Format | Book Chapter |
| Language | English |
| Published |
Cham
Springer International Publishing
2014
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| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319112084 3319112082 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-319-11209-1_4 |
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| Summary: | Executing complex plans for coordinating the behaviors of multiple heterogeneous agents often requires setting several parameters. For example, we are developing a decision aid for deploying a set of autonomous vehicles to perform situation assessment in a disaster relief operation. Our system, the Situated Decision Process (SDP), uses parameterized plans to coordinate these vehicles. However, no model exists for setting the values of these parameters. We describe a case-based reasoning solution for this problem and report on its utility in simulated scenarios, given a case library that represents only a small percentage of the problem space. We found that our agents, when executing plans generated using our case-based algorithm on problems with high uncertainty, performed significantly better than when executing plans using baseline approaches. |
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| ISBN: | 9783319112084 3319112082 |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.1007/978-3-319-11209-1_4 |