Learning from multiple sources

• Type: Article in International Conference Proceedings Book
• Date: 2004

Authors

Luís Nunes (Author) Other; Eugénio Oliveira (Author) FEUP

Conference proceedings International

Title: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004

Pages: 1106-1113

3rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), 19-23 August 2004, New York, NY, USA

Indexing

ISI Web of Knowledge

Scopus - 22 Citations

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Authenticus ID: P-007-MAY

DOI: 10.1109/aamas.2004.10162

Abstract (EN): This work aims at defining and testing a set of techniques that enables agents to use information from several sources during learning. In Multiagent Systems (MAS) it is frequent that several agents need to learn similar concepts in parallel. In this type of environment there are more possibilities for learning than in classical Machine Learning. Exchange of information between teams of agents that are attempting to solve similar problems may increase accuracy and learning speed at the expense of communication. One of the most interesting possibilities to explore is when the agents may have different structures and learning algorithms, thus providing different perspectives on the problems they are facing. In this paper the authors report the results of experiments made in a traffic control simulation with and without exchanging information between learning agents.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8