Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Agent modeling methods using limited rationality
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Learning cases to resolve conflicts and improve group behavior
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Learning Coordination Strategies for Cooperative Multiagent Systems
Machine Learning
Learning Situation-Specific Coordination in Cooperative Multi-agent Systems
Autonomous Agents and Multi-Agent Systems
Chunking in Soar: The Anatomy of a General Learning Mechanism
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Learning to Reduce Communication Cost on Task Negotiation among Multiple Autonomous Mobile Robots
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Retrieval and Reasoning in Distributed Case Bases
Retrieval and Reasoning in Distributed Case Bases
Learning to better coordinate in joint activities
Learning to better coordinate in joint activities
Journal of Artificial Intelligence Research
A Distributed Approach for Coordination of Traffic Signal Agents
Autonomous Agents and Multi-Agent Systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Learning the required number of agents for complex tasks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Effects of social learning and team familiarity on team performance
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation
Multiagent and Grid Systems
Mutual state-based capabilities for role assignment in heterogeneous teams
Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots
Agent-Based evolutionary labor market model with strategic coalition
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
| Hi-index | 0.01 |
A fundamental difficulty faced by groups of agents that work together is how to efficiently coordinate their efforts. This coordination problem is both ubiquitous and challenging, especially in environments where autonomous agents are motivated by personal goals.Previous AI research on coordination has developed techniques that allow agents to act efficiently from the outset based on common built-in knowledge or to learn to act efficiently when the agents are not autonomous. The research described in this paper builds on those efforts by developing distributed learning techniques that improve coordination among autonomous agents.The techniques presented in this work encompass agents who are heterogeneous, who do not have complete built-in common knowledge, and who cannot coordinate solely by observation. An agent learns from her experiences so that her future behavior more accurately reflects what works (or does not work) in practice. Each agent stores past successes (both planned and unplanned) in their individual casebase. Entries in a casebase are represented as coordinated procedures and are organized around learned expectations about other agents.It is a novel approach for individuals to learn procedures as a means for the group to coordinate more efficiently. Empirical results validate the utility of this approach. Whether or not the agents have initial expertise in solving coordination problems, the distributed learning of the individual agents significantly improves the overall performance of the community, including reducing planning and communication costs.