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Giving Personal Assistant Agents a Case-Based Memory

Author

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  • Ke-Jia Chen

    (Nanjing University of posts and telecommunications, China, and Université de Technologie de Compiègne, France)

  • Jean-Paul A. Barthès

    (Université de Technologie de Compiègne, France)

Abstract

We consider Personal Assistant (PA) agents as cognitive agents capable of helping users handle tasks at their workplace. A PA must communicate with the user using casual language, sub-contract the requested tasks, and present the results in a timely fashion. This leads to fairly complex cognitive agents. However, in addition, such an agent should learn from previous tasks or exchanges, which will increase its complexity. Learning requires a memory, which leads to the two following questions: Is it possible to design and build a generic model of memory? If it is, is it worth the trouble? The article tries to answer the questions by presenting the design and implementation of a memory for PA agents, using a case approach, which results in an improved agent model called MemoPA.

Suggested Citation

  • Ke-Jia Chen & Jean-Paul A. Barthès, 2010. "Giving Personal Assistant Agents a Case-Based Memory," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 4(1), pages 45-64, January.
    • Handle: RePEc:igg:jcini0:v:4:y:2010:i:1:p:45-64
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