Design and control of large collections of learning agents

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The Collective Intelligence project at NASA/Ames provides an elegant, machinelearning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This thesis will focus on creating additional key properties and algorithms within the mathematics of the Framework of Collectives. The additions will allow agents to learn quickly in more complex systems. Also they will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2003

Author dc:creator

• Agogino, Adrian Kujaneck

Advisor dc:contributor.advisor

• Ghosh, Joydeep

Statement dc:rights

• Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works.

Language dc:language.iso

eng

Identifier

b56700246

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/424