Dynamic reorganization of decision-making groups

  • Authors:

  • K. S. Barber;C. E. Martin

  • Affiliations:

  • The Laboratory for Intelligent Processes and Systems, Electrical and Computer Engineering Department, The University of Texas at Austin, Austin, TX;The Laboratory for Intelligent Processes and Systems, Electrical and Computer Engineering Department, The University of Texas at Austin, Austin, TX

  • Venue:
  • Proceedings of the fifth international conference on Autonomous agents
  • Year:
  • 2001

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Abstract

Agents in a multi- agent system must coordinate to achieve their goals, in general. Establishing an organizational structure that specifies how agents in the system should work together helps multi- agent systems achieve effective coordination. Among other things, an organizational structure specifies agent decisionmaking frameworks. A decision- making framework identifies the locus of decision- making control for a given goal and the authority of decision- makers to assign subtasks in order to achieve that goal. Agents may participate in different decision- making frameworks for each goal they pursue. Agents who implement the capability of Adaptive Decision- Making Frameworks (ADMF) are able to dynamically modify their decision- making frameworks at run- time to best meet the needs of their current situation. Through ADMF, agents are able to reorganize decision- making groups by dynamically changing (1) who makes the decisions for a particular goal and (2) who must carry out these decisions. This paper presents experiments exploring the following hypothesis: Multi- agent systems that implement ADMF can perform better and more robustly across run- time changes in situation than systems that maintain static decision- making frameworks. Experimental results show that no one decision- making framework performs best across various situations that may be faced at run- time. Further experiments show that the implementation of ADMF in multi- agent systems can improve system performance across changing situations. These experimental results clearly motivate the implementation of Adaptive Decision- Making Frameworks in multi- agent systems.