Giving Personal Assistant Agents a Case-Based Memory

Ke-Jia Chen and Jean-Paul A. Barthès
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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

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2010, vol. 4, issue 1, 45-64

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.

Date: 2010
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