Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Dynamic adaptive autonomy in multi-agent systems

Dynamic adaptive autonomy in multi-agent systems Multi-agent systems require adaptability to perform effectively in complex and dynamic environments. This article shows that agents should be able to benefit from dynamically adapting their decision-making frameworks. A decision-making framework describes the set of multi-agent decision-making interactions exercised by members of an agent group in the course of pursuing a goal or set of goals. The decision-making interaction style an agent adopts with respect to other agents influences that agent's degree of autonomy. The article introduces the capability of Dynamic Adaptive Autonomy (DAA), which allows an agent to dynamically modify its autonomy along a defined spectrum (from command-driven to consensus to locally autonomous/master) for each goal it pursues. This article presents one motivation for DAA through experiments showing that the ‘best’ decision-making framework for a group of agents depends not only on the problem domain and pre-defined characteristics of the system, but also on run-time factors that can change during system operation. This result holds regardless of which performance metric is used to define ‘best’. Thus, it is possible for agents to benefit by dynamically adapting their decision-making frameworks to their situation during system operation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Experimental & Theoretical Artificial Intelligence Online Taylor & Francis

Dynamic adaptive autonomy in multi-agent systems

19 pages

Loading next page...
 
/lp/taylor-francis/dynamic-adaptive-autonomy-in-multi-agent-systems-GtUBO39ubp

References (24)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1362-3079
eISSN
0952-813X
DOI
10.1080/095281300409793
Publisher site
See Article on Publisher Site

Abstract

Multi-agent systems require adaptability to perform effectively in complex and dynamic environments. This article shows that agents should be able to benefit from dynamically adapting their decision-making frameworks. A decision-making framework describes the set of multi-agent decision-making interactions exercised by members of an agent group in the course of pursuing a goal or set of goals. The decision-making interaction style an agent adopts with respect to other agents influences that agent's degree of autonomy. The article introduces the capability of Dynamic Adaptive Autonomy (DAA), which allows an agent to dynamically modify its autonomy along a defined spectrum (from command-driven to consensus to locally autonomous/master) for each goal it pursues. This article presents one motivation for DAA through experiments showing that the ‘best’ decision-making framework for a group of agents depends not only on the problem domain and pre-defined characteristics of the system, but also on run-time factors that can change during system operation. This result holds regardless of which performance metric is used to define ‘best’. Thus, it is possible for agents to benefit by dynamically adapting their decision-making frameworks to their situation during system operation.

Journal

Journal of Experimental & Theoretical Artificial Intelligence OnlineTaylor & Francis

Published: Apr 1, 2000

Keywords: Multi-AGENT Systems Agent Autonomy Decision-MAKING Frameworks Multi-AGENT Reorganization

There are no references for this article.