Learning from cooperation using justifications

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. Keywords. Machine Learning, Multi-agent System, Cooperation, Justifications

• Type: Conference
• Year: 2006
• Where: CCIA
• Authors: Eloi Puertas, Eva Armengol
• Keywords: Artificial Intelligence, CCIA 2006, Multi-agent System, Problem Solving, Problem Solving Capabilities