Journal article
Learning collaboration strategies for committees of learning agents
Autonomous agents and multi-agent systems, v 13(3), pp 429-461
01 Nov 2006
Abstract
A main issue in cooperation in multi-agent systems is how an agent decides in which situations is better to cooperate with other agents, and with which agents does the agent cooperate. Specifically in this paper we focus on multi-agent systems composed of learning agents, where the goal of the agents is to achieve a high accuracy on predicting the correct solution of the problems they encounter. For that purpose, when encountering a new problem each agent has to decide whether to solve it individually or to ask other agents for collaboration. We will see that learning agents can collaborate forming committees in order to improve performance. Moreover, in this paper we will present a proactive learning approach that will allow the agents to learn when to convene a committee and with which agents to invite to join the committee. Our experiments show that learning results in smaller committees while maintaining (and sometimes improving) the problem solving accuracy than forming committees composed of all agents.
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Details
- Title
- Learning collaboration strategies for committees of learning agents
- Creators
- Enric Plaza - Artificial Intelligence Research InstituteSantiago Ontanon - Artificial Intelligence Research Institute
- Publication Details
- Autonomous agents and multi-agent systems, v 13(3), pp 429-461
- Publisher
- Springer Nature
- Number of pages
- 33
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000240316500006
- Scopus ID
- 2-s2.0-33748429061
- Other Identifier
- 991021869112804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Automation & Control Systems
- Computer Science, Artificial Intelligence