MVILAIN@G.BBN.COM (Marc Vilain) (04/28/88)
BBN Science Development Program
AI Seminar Series Lecture
LEARNING EFFECTIVE SEARCH CONTROL KNOWLEDGE:
AN EXPLANATION-BASED APPROACH
Steven Minton
Carnegie-Mellon University
(Steven.Minton@cad.cs.cmu.edu)
BBN Labs
10 Moulton Street
2nd floor large conference room
10:30 am, Tuesday May 3
In order to solve problems more effectively with accumulating
experience, a problem solver must be able to learn and exploit search
control knowledge. In this talk, I will discuss the use of
explanation-based learning (EBL) for acquiring domain-specific control
knowledge. Although previous research has demonstrated that EBL is a
viable approach for acquiring control knowledge, in practice EBL may not
always generate useful control knowledge. For control knowledge to be
effective, the cumulative benefits of applying the knowledge must
outweigh the cumulative costs of testing whether the knowledge is
applicable. Generating effective control knowledge may be difficult, as
evidenced by the complexities often encountered by human knowledge
engineers. In general, control knowledge cannot be indiscriminately
added to a system; its costs and benefits must be carefully taken into
account.
To produce effective control knowledge, an explanation-based learner
must generate "good" explanations -- explanations that can be profitably
employed to control problem solving. In this talk, I will discuss the
utility of EBL and describe the PRODIGY system, a problem solver that
learns by searching for good explanations. Extensive experiments testing
the PRODIGY/EBL architecture in several task domains will be discussed.
I will also briefly describe a formal model of EBL and a proof that
PRODIGY's generalization algorithm is correct with respect to this model.
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