Evolving Models From Observed Human Performance

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.

Author dc:creator

• Fernlund, Hans Karl Gustav

Contributors dc:contributor

• Gonzalez, Avelino

• Context based reasoning
• Genetic programming
• Human behavioral modeling
• Learning by observation
• Simulation
• Electrical and Computer Engineering
• Engineering
• Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

Language dc:language

English

Identifier

CFE0000013

OAI identifier oai:identifier

oai:stars.library.ucf.edu:etd-1089