Bottom-Up Induction of Feature Terms
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cooperative Case-Based Reasoning
ECAI '96 Selected papers from the Workshop on Distributed Artificial Intelligence Meets Machine Learning, Learning in Multi-Agent Environments
Lazy Induction of Descriptions for Relational Case-Based Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
On the Importance of Similitude: An Entropy-Based Assessment
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Perspectives: A Declarative Bias Mechanism for Case Retrieval
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Case Exchange Strategies in Multiagent Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatically Selecting Strategies for Multi-Case-Base Reasoning
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Cooperative Case Bartering for Case-Based Reasoning Agents
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Language games for meaning negotiation between human and computer agents
ESAW'05 Proceedings of the 6th international conference on Engineering Societies in the Agents World
| Hi-index | 0.00 |
Multiagent systems offer a new paradigm where learning techniques can be useful. We focus on the application of lazy learning to multiagent systems where each agents learns individually and also learns when to cooperate in order to improve its performance. We show some experiments in which CBR agents use an adapted version of LID (Lazy Induction of Descriptions), a CBR method for classification. We discuss a collaboration policy (called Bounded Counsel) among agents that improves the agents' performance with respect to their isolated performance. Later, we use decision tree induction and discretization techniques to learn how to tune the Bounded Counsel policy to a specific multiagent system--preserving always the individual autonomy of agents and the privacy of their case-bases. Empirical results concerning accuracy, cost, and robustness with respect to number of agents and case base size are presented. Moreover, comparisons with the Committee collaboration policy (where all agents collaborate always) are also presented.