Publication Date
2021
Document Type
Dissertation
Committee Members
Michael T. Cox, Ph.D. (Committee Co-Chair); Mateen M. Rizki, Ph.D. (Committee Co-Chair); Matthew Mollineaux, Ph.D. (Committee Member); Michael Raymer, Ph.D. (Committee Member); Tanvi Banerjee, Ph.D. (Committee Member)
Degree Name
Doctor of Philosophy (PhD)
Abstract
Autonomous agents in a multi-agent system coordinate to achieve their goals. However, in a partially observable world, current multi-agent systems are often less effective in achieving their goals. In much part, this limitation is due to an agent's lack of reasoning about other agents and their mental states. Another factor is the agent's inability to share required knowledge with other agents and the lack of explanations in justifying the reasons behind the goal. This research addresses these problems by presenting a general approach for agent goal management in unexpected situations. In this approach, an agent applies three main concepts: goal reasoning - to determine what goals to pursue and share; theory of mind - to select an agent(s) for goal delegation; explanation - to justify to the selected agent(s) the reasons behind the delegated goal. Our approach presents several algorithms required for goal management in multi-agent systems. We demonstrate that these algorithms will help agents in a multi-agent context better manage their goals and improve their performance. In addition, we evaluate the performance of our multi-agent system in a marine life survey domain and a rover domain. Finally, we compare our work to different multi-agent systems and present empirical results that support our claim.
Page Count
83
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2021
Copyright
Copyright 2021, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
