The contract net protocol: high-level communication and control in a distributed problem solver
Distributed Artificial Intelligence
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Adaptive task resources allocation in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Cloning for Intelligent Adaptive Information Agents
Revised Papers from the Second Australian Workshop on Distributed Artificial Intelligence: Multi-Agent Systems: Methodologies and Applications
Environment centered analysis and design of coordination mechanisms
Environment centered analysis and design of coordination mechanisms
Evolution of the GPGP/TÆMS Domain-Independent Coordination Framework
Autonomous Agents and Multi-Agent Systems
A Dynamically Formed Hierarchical Agent Organization for a Distributed Content Sharing System
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Adaptive decision-making frameworks for dynamic multi-agent organizational change
Autonomous Agents and Multi-Agent Systems
Organizational self-design in semi-dynamic environments
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
A capabilities-based model for adaptive organizations
Autonomous Agents and Multi-Agent Systems
Predictability & criticality metrics for coordination in complex environments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Agent cloning: an approach to agent mobility and resource allocation
IEEE Communications Magazine
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Organizational Self-Design (OSD) has been proposed as an approach to constructing suitable organizations at runtime in which the agents are responsible for constructing their own organizational structures. OSD has also been shown to be especially suited for environments that are dynamic and semi-dynamic. Most existing OSD approaches work by changing the organizational structure in response to changes in the environment - usually by spawning a new agent when an agent is overloaded and composing agents when they are free. One approach to spawning involves "breaking" up a problem into smaller sub-problems and assigning one of the sub-problems to the newly spawned agent. An alternative approach works by "cloning" the source agent and assigning the clone agent a portion of the source's work load. We posit that both of these approaches are complementary, have their own advantages, and can be used together. In this paper we analyze the tradeoffs between cloning and breakup and generate a hybrid model that uses both cloning and breakup to generate more suitable organizations than those that could be generated when using a single approach.