NDLI: Adaptive decision-making frameworks for dynamic multi-agent organizational change

Content Provider	Springer Nature Link
Author	Martin, Cheryl Barber, K. Suzanne
Copyright Year	2006
Abstract	This article presents a capability called Adaptive Decision-Making Frameworks (ADMF) and shows that it can result in significantly improved system performance across run-time situation changes in a multi-agent system. Specifically, ADMF can result in improved and more robust performance compared to the use of a single static decision-making framework (DMF). The ADMF capability allows agents to dynamically adapt the DMF in which they participate to fit their run-time situation as it changes. A DMF identifies a set of agents and specifies the distribution of decision-making control and the authority to assign subtasks among these agents as they determine how a goal or set of goals should be achieved. The ADMF capability is a form of organizational adaptation and differs from previous approaches to organizational adaptation and dynamic coordination in that it is the first to allow dynamic and explicit manipulation of these DMF characteristics at run-time as variables controlling agent behavior. The approach proposed for selecting DMFs at run-time parameterizes all domain-specific knowledge as characteristics of the agents’ situation, so the approach is application-independent. The presented evaluation empirically shows that, for at least one multi-agent system, there is no one best DMF for multiple agents across run-time situational changes. Next, it motivates the further exploration of ADMF by showing that adapting DMFs to run-time variations in situation can result in improved overall system performance compared to static or random DMFs.
Starting Page	391
Ending Page	428
Page Count	38
File Format	PDF
ISSN	13872532
Journal	Autonomous Agents and Multi-Agent Systems
Volume Number	13
Issue Number	3
e-ISSN	15737454
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-06-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Agent autonomy Adjustable autonomy Dynamic self-organization Organizational adaptation Multiagent systems Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems User Interfaces and Human Computer Interaction Computing Methodologies Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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