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EVOLVING MODELS FROM OBSERVED HUMAN PERFORMANCE

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Date Issued:
2004
Abstract/Description:
To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
Title: EVOLVING MODELS FROM OBSERVED HUMAN PERFORMANCE.
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Name(s): Fernlund, Hans Karl Gustav, Author
Gonzalez, Avelino J., Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2004
Publisher: University of Central Florida
Language(s): English
Abstract/Description: To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
Identifier: CFE0000013 (IID), ucf:46068 (fedora)
Note(s): 2004-05-01
Ph.D.
College of Engineering and Computer Science, Department of Electrical and Computer Engineering
This record was generated from author submitted information.
Subject(s): Genetic Programming
Context-based Reasoning
Learning by Observation
Human Behavioral Modeling
Simulation
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0000013
Restrictions on Access: public
Host Institution: UCF

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