We present a proactive communication approach that allows CBR agents to gauge the strengths and weaknesses of other CBR agents. The communication protocol allows CBR agents to learn from communicating with other CBR agents in such a way that each agent is able to retain certain cases provided by other agents that are able to improve their individual performance (without need to disclose all the contents of each case base). The selection and retention of cases is modeled as a case bartering process, where each individual CBR agent autonomously decides which cases offers for bartering and which offered barters accepts. Experimental evaluations show that the sum of all these individual decisions result in a clear improvement in individual CBR agent performance with only a moderate increase of individual case bases.
Read the paper:
Case-Based Learning from Proactive Communication
by Santi Ontañón and Enric Plaza
International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 999-1004www.cc.gatech.edu/faculty/ashwin/papers/er-07-18.pdf
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Cognitive Computing 