Extending learning to multiple agents: issues and a model for multi-agent machine learning (MA-ML)
EWSL-91 Proceedings of the European working session on learning on Machine learning
Lookahead-based algorithms for anytime induction of decision trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Theory revision with queries: horn, read-once, and parity formulas
Artificial Intelligence
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
SMILE: Sound Multi-agent Incremental LEarning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Case-based learning from proactive communication
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning in a Fixed or Evolving Network of Agents
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning in BDI multi-agent systems
CLIMA IV'04 Proceedings of the 4th international conference on Computational Logic in Multi-Agent Systems
| Hi-index | 0.00 |
This article addresses collaborative concept learning in a MAS. In a concept learning problem an agent incrementally revises a hypothetical representation of some target concept to keep it consistent with the whole set of examples that it receives from the environment or from other agents. In the program SMILE, this notion of consistency was extended to a group of agents. A surprising experimental result of that work was that a group of agents learns better the difficult boolean problems, than a unique agent receiving the same examples. The first purpose of the present paper is to propose some explanation about such unexpected superiority of collaborative learning. Furthermore, when considering large societies of agents, using pure sequential protocols is unrrealistic. The second and main purpose of this paper is thus to propose and experiment broadcast protocols for collaborative learning.