Department of Computer Science and Engineering
University of South Carolina
Author : Bridgette Parsons
Advisor : Dr. Jose Vidal
Date : Dec 14th, 2018
Time : 1:30 pm
Place : Meeting room 2267, Innovation Center
Modeling believable human behavior for use in simulations is a difficult task. It requires a great deal of time, and frequently requires coordination between members of different disciplines. In our research, we propose a method of partially automating the process, reducing the time it takes to create the model, and more easily allowing domain experts that are not programmers to adjust the models as necessary. Using Agent-Based modeling, we present MAGIC (Models Automatically Generated from Information Collected), an algorithm designed to automatically find points in the model's decision process that require interaction with other agents or with the simulation environment and create a decision graph that contains the agent's behavior pattern based upon raw data composed of time-sequential observations. We also present an alternative to the traditional Markov Decision Process that allows actions to be completed until a set condition is met, and a tool to allow domain experts to easily adjust the resulting models as needed. After testing the accuracy of our algorithm using synthetic data, we show the results of this process when it is used in a real-world simulation based upon a study of the medical administration process in hospitals conducted by the University of South Carolina's Healthcare Process Redesign Center.
In the healthcare study, it was necessary for the nurses to follow a very consistent process. In order to show the ability to use our algorithm in a variety of situations, we create a video game and record players' movements. However, unlike the nursing simulation, the environment in the game simulation is more prone to changes that limit the appropriate set of actions taken by the humans being modeled. In order to account for the changes in the simulation, we present a simple method using the addition of a hierarchy of rules with our previous algorithm to limit the actions taken by the agent to ones that are appropriate for the current situation.
In both the healthcare study and the video game, we find that there are multiple distinct patterns of behavior. As a single model would not accurately represent the behavior of all of the humans in the studies, we present a simple method of classifying the behavior of individuals using the decision graphs created by our algorithm. We then use our algorithm to create models for each cluster of behaviors, producing multiple models from one set of observational data. Believability is highly subjective. In our research, we present methods to partially automate the process of producing believable human agents, and test our results with real-world data using focus groups and a pseudo-Turing test. Our findings show that under the right conditions, it is possible to partially automate the modeling of human decision processes, but ultimately, believability is greatly dependent upon the similarity between the viewer and the humans being modeled.