Cooperative multiagent learning

Authors:
Enric Plaza;Santiago Ontañón
Affiliations:
Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Bellaterra, Catalonia, Spain;Artificial Intelligence Research Institute, Spanish Council for Scientific Research, Bellaterra, Catalonia, Spain
Venue:
Adaptive agents and multi-agent systems
Year:
2003

Citing 6
Cited 5

Bagging predictors

Machine Learning
A bartering approach to improve multiagent learning

Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

Data Mining and Knowledge Discovery
Neural Network Ensembles

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modelling the Competence of Case-Bases

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Bidding Mechanisms for Data Allocation in Multi-Agent Environments

ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages

An evidential and genetic method for cooperative learning systems

Multiagent and Grid Systems
Boosting CBR Agents with Genetic Algorithms

ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Using case-based reasoning in autonomic electronic institutions

COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
PISA: A framework for multiagent classification using argumentation

Data & Knowledge Engineering
Role assignment for an agent group in consideration of conflicts among agents

Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cooperation and learning are two ways in which an agent can improve its performance. Cooperative Multiagent Learning is a framework to analyze the tradeoff between cooperation and learning in multiagent systems. We focus on multiagent systems where individual agents are capable of solving problems and learning using CBR (Case-based Reasoning). We present several collaboration strategies for agents that learn and their empirical results in several experiments. Finally we analyze the collaboration strategies and their results along several dimensions, like number of agents, redundancy, CBR technique used, and individual decision policies.