Keywords
Contexts, CxBR, Reinforcement, Learning, Machine Learning, Human Behavior Models
Abstract
A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.
Notes
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Graduation Date
2008
Advisor
Gonzalez, Avelino
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Electrical Engineering and Computer Science
Degree Program
Computer Engineering
Format
application/pdf
Identifier
CFE0002466
URL
http://purl.fcla.edu/fcla/etd/CFE0002466
Language
English
Release Date
December 2008
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
STARS Citation
Aihe, David, "A Reinforcement Learning Technique For Enhancing Human Behavior Models In A Context-based Architecture" (2008). Electronic Theses and Dissertations. 3500.
https://stars.library.ucf.edu/etd/3500