ACM Computing Surveys (CSUR) - The MIT Press scientific computation series
Made to Measure: Ecological Rationality in Structured Environments
Minds and Machines
Connecting the Physical World with Pervasive Networks
IEEE Pervasive Computing
Layered Learning and Flexible Teamwork in RoboCup Simulation Agents
RoboCup-99: Robot Soccer World Cup III
Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects
Biocomputing and emergent computation: Proceedings of BCEC97
The organization of work in social insect colonies
Complexity - Special issue: Selection, tinkering, and emergence in complex networks
Anthill: A Framework for the Development of Agent-Based Peer-to-Peer Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Information-driven phase changes in multi-agent coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
RoboCup Rescue: A Grand Challenge for Multi-Agent Systems
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Modeling Indirect Interaction in Open Computational Systems
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
The Internet As a Large-Scale Complex System
The Internet As a Large-Scale Complex System
Dynamic Polyethism and Competition for Tasks in Threshold Reinforcement Models of Social Insects
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
Distributed Cooperative Control for Adaptive Performance Management
IEEE Internet Computing
Achieving Self-Management via Utility Functions
IEEE Internet Computing
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Flexible service provisioning with advance agreements
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Methods for task allocation via agent coalition formation
Artificial Intelligence
Engineering Self-Organising Systems: methodologies and Applications
Engineering Self-Organising Systems: methodologies and Applications
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The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the runtime reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system's scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions. In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximized when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organizes into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimize any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes.