Agent-based Collaborative Plan Adaptation with Resource Constraints

Open Access
Author:
Wang, Rui
Graduate Program:
Information Sciences and Technology
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
September 27, 2006
Committee Members:
  • John Yen, Committee Chair
Keywords:
  • resource
  • collaboration
  • agent
  • plan adaptation
Abstract:
One important lesson learned from the crisis of Hurricane Katrina is that preplanned allocation of scarce resources (e.g., helicopters, vehicles, medical equipment, etc.) must be dynamically modified to adapt to the changing situation. Furthermore, multiple response teams, which own different resources, need to collaborate to determine tradeoffs among competing resource needs to reallocate such resources. The challenge of this collaborative resource reallocation is further complicated by the fact that each team may not have complete information about other teams¡¯ resources and the utility of their tasks. This limitation makes it difficult to assess tradeoffs among resource needs of different teams. In order to solve such problems, this research developed team-based software intelligent agents to collaboratively adapt existing plans for resource use by consistently reallocating the limited resources among distributed tasks. Based on an innovative multi-agent teamwork model called R-CAST (RPD-enabled Collaborative Agents for Simulating Teamwork), this research developed a framework for collaborative plan adaptation under resource constraints. The framework addresses the challenges mentioned above in three ways. First, it extends R-CAST with explicit representation of resources and related reasoning algorithms about resources. Second, it uses a combinatorial auction mechanism to enable agents to exchange utility information regarding competing needs of bundled resources, so that agents can reason about resource tradeoffs for effective reallocation of scarce resources. Third, the framework implements an algorithm for an agent to assess the opportunity cost of offering a resource bundle that has already been assigned to a task. The algorithm considers alternative ways to accomplish the task, the utility of such alternatives, and associated costs for obtaining required resources. In summary, the work presented in this thesis facilitates distributed teams to reason about tradeoffs among competing requests for resource bundles by exchanging relevant information through combinatorial auctions. Experimental results have suggested that this research can significantly improve the utilization of limited resources in adapting plans to the dynamic situation.