C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Lazy Learning of Bayesian Rules
Machine Learning
Using self-diagnosis to adapt organizational structures
Proceedings of the fifth international conference on Autonomous agents
Multiagent teamwork: analyzing the optimality and complexity of key theories and models
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Coalition formation with uncertain heterogeneous information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Role allocation and reallocation in multiagent teams: towards a practical analysis
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)
A Combinatorial Auction for Collaborative Planning
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Learning when and how to coordinate
Web Intelligence and Agent Systems
Organization-Based Cooperative Coalition Formation
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Survivability of Multiagent-Based Supply Networks: A Topological Perspective
IEEE Intelligent Systems
Agent-organized networks for dynamic team formation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Journal of Artificial Intelligence Research
Cognitive Systems Research
Trusting Groups in Coalition Formation Using Social Distance
ATC '08 Proceedings of the 5th international conference on Autonomic and Trusted Computing
A developmental algorithm for multi-agent swarms with scalable hierarchies
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A developmental approach to evolving scalable hierarchies for multi-agent swarms
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Team formation and optimization for service provisioning
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Autonomous Agents and Multi-Agent Systems
A sociologically inspired heuristic for optimization algorithms: A case study on ant systems
Expert Systems with Applications: An International Journal
Weighted synergy graphs for effective team formation with heterogeneous ad hoc agents
Artificial Intelligence
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Networked multi-agent systems are comprised of many autonomous yet interdependent agents situated in a virtual social network. Two examples of such systems are supply chain networks and sensor networks. A common challenge in many networked multi-agent systems is decentralized team formation among the spatially and logically extended agents. Even in cooperative multi-agent systems, efficient team formation is made difficult by the limited local information available to the individual agents. We present a model of distributed multi-agent team formation in networked multi-agent systems, describe a policy learning framework for joining teams based on local information, and give empirical results on improving team formation performance. In particular, we show that local policy learning from limited information leads to a significant increase in organizational team formation performance compared to a random policy.