New Generation Computing - Selected papers from the international workshop on algorithmic learning theory,1990
Learning in multiagent systems
Multiagent systems
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Remembering similitude terms in CBR
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Scaling up: distributed machine learning with cooperation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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In multi-agent systems, individual problem solving capabilities can be improved thanks to the interaction with other agents. In the classification problem solving task each agent is able to solve the problems alone, but in a collaborative scenario, an agent can take advantage of the knowledge of others. In our approach, when an agent decides to collaborate with other agents, in addition to the solution for the current problem, it acquires new domain knowledge. This domain knowledge consists on explanations (or justifications) that other agents done for the solution they proposed. In that way, the first agent can store these justifications and use them like some kind of domain rules for solving new problems. As a consequence, the agent acquires experience and it is capable to solve on its own problems that initially were outside of his experience.