Predatory sequence learning for synthetic characters

The process of mammalian predatory sequence development offers a number of insights relevant to the goal of designing synthetic characters that can quickly and easily learn complicated and interesting behavior. We propose a number of principles for designing such learning systems, inspired by a targeted review of animal developmental phenomena, with particular emphasis on the development of predatory behavior in certain felid and canid species. We describe the implementation of a few of these principles as an extension to a popular algorithm for learning in autonomous systems called hierarchical Q-learning. In this new approach, the agent starts out with only one skill, and then new skills are added one at a time to its available repertoire as time passes. The agent is motivated to experiment thoroughly with each new skill as it is introduced. Simulation results are presented which empirically demonstrate the advantages of this new algorithm for the speed and effectiveness of the learning process.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2003

Author dc:creator

• Berlin, Matthew Roberts, 1980-

Advisor dc:contributor.advisor

• Bruce M. Blumberg.

• Architecture. Program In Media Arts and Sciences.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/61141

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/61141