Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
ADEPT: an agent-based approach to business process management
ACM SIGMOD Record
Emergence of complex ecologies in ECHO
Proceedings from the international conference on complex systems on Unifying themes in complex systems
Adaptive task resources allocation in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Core specification and experiments in DIET: a decentralised ecosystem-inspired mobile agent system
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
An approach to the analysis and design of multiagent systems based on interaction frames
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Exploiting Social Reasioning to Enhance Adaption in Open-Multi-Agent Systems
SBIA '95 Proceedings of the 12th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
An Adaptive Agent Based Economic Model
Learning Classifier Systems, From Foundations to Applications
Evolving Behaviors for Cooperating Agents
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
An Adaptive Organizational Policy for Multi Agent Systems - AASMAN
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Genetic Algorithms in a Multi-Agent System
INTSYS '98 Proceedings of the IEEE International Joint Symposia on Intelligence and Systems
Adaptive Agents and Multiagent Systems
IEEE Distributed Systems Online
A Model of Adaptation in Collaborative Multi-Agent Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Is Echo a Complex Adaptive System?
Evolutionary Computation
Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning (Lecture Notes in Computer Science)
An architecture for adaptive intelligent systems
Artificial Intelligence
Modeling adaptive autonomous agents
Artificial Life
Adaptive agents and multi-agent systems: adaptation and multi-agent learning
Adaptive agents and multi-agent systems: adaptation and multi-agent learning
Evolving preferences among emergent groups of agents
Adaptive agents and multi-agent systems
Structuring agents for adaptation
Adaptive agents and multi-agent systems
Business Ecosystem Modelling: Combining Natural Ecosystems and Multi-Agent Systems
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Engineering Business Ecosystems Using Environment-Mediated Interactions
Engineering Environment-Mediated Multi-Agent Systems
Component Based Approach for Composing Adaptive Mobile Agents
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Runtime Adaptation of Multiagent Systems for Ubiquitous Environments
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Self-Adaptable Discovery and Composition of Services Based on the Semantic CompAA Approach
International Journal of Adaptive, Resilient and Autonomic Systems
| Hi-index | 0.00 |
This paper proposes a classification framework to help with the understanding and integration of contributions in the field of adaptive multi-agent systems. This framework is used to highlight gaps in the field and derive directions for further research. The need for this framework has arisen from the proliferation of fragmented streams of research, aiming to enable adaptation of agent systems to rapidly changing circumstances and requirements. Multi-agent systems are purported to provide flexible support for users and organisations in dynamic and complex open environments because of their capabilities of autonomous problem-solving. However, exploring the boundaries of flexibility quickly uncovers limitations when agents have to adapt to situations which have not been considered during design time. This issue has been addressed by different research groups using approaches such as flexible systems, evolutionary computation, control systems, and complex adaptive systems. Nevertheless, exchange of ideas between different groups is rare, and systematic analysis of achievements is overdue. The classification framework proposed here is used for such analysis and covers both the analysis and the results in terms of directions for future work.