Utilizing Case-Based Reasoning and Automatic Case Elicitation to Develop a Self-Taught Knowledgeable Agent

TitleUtilizing Case-Based Reasoning and Automatic Case Elicitation to Develop a Self-Taught Knowledgeable Agent
Publication TypeClassical
Year of Publication2004
AuthorsPowell JH, Hauff BM, Hastings JD
CityNebraska
Abstract

Traditionally case-based reasoning (CBR) systems have
relied on information manually provided by domain experts
to form their knowledge bases. Additional domain
knowledge is often used to improve performance
of such systems. A less costly method of knowledge acquisition
is automatic case elicitation, a learning technique
in which a CBR system acquires knowledge automatically
during real-time interaction with its environment
with no prior domain knowledge (e.g., rules
or cases). For problems that are observable, discrete
and either deterministic or strategic in nature, automatic
case elicitation can lead to the development of
a self-taught knowledgeable agent. This paper describes
the use of automatic case elicitation in CHEBR,
a CHEckers case-Based Reasoner that employs selftaught
knowledgeable agents. CHEBR was tested using
model-based versus non-model-based matching to
evaluate its ability to learn without predefined domain
knowledge. The results suggest that additional experience
can substitute for the inclusion of precoded modelbased
knowledge.

URLhttp://www.aaai.org/Papers/Workshops/2004/WS-04-04/WS04-04-016.pdf