Case-based learning from proactive communication

  • Ontañón S
  • Plaza E
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Abstract

We present a proactive communication approach that allows CBR agents to gauge the strengths and weaknesses of other CBR agents. The communi- cation 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 dis- close 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. Exper- imental 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.

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  • PUI: 369445988
  • SGR: 38549137425
  • SCOPUS: 2-s2.0-38549137425
  • ISSN: 10450823

Authors

  • Santiago Ontañón

  • Enric Plaza

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