Steven.Minton@CAD.CS.CMU.EDU (03/18/87)
This week's speaker is Sridhar Mahadevan. As usual, the seminar is in 7220 Wean on Friday at 3:15. Come one, come all. LEARNING DECOMPOSITION METHODS TO IMPROVE HIERARCHICAL PROBLEM-SOLVING PERFORMANCE Previous work in machine learning on improving problem-solving performance has usually assumed a @i(state-space) or "flat" problem-solving model. However, problem-solvers in complex domains, such as design, usually employ a hierarchical or problem-reduction strategy to avoid the combinatorial explosion of possible operator sequences. Consequently, in order to apply machine learning to complex domains, hierarchical problem-solvers that automatically improve their performance need to designed. One general approach is to design an @i(interactive) problem-solver -- a @i(learning apprentice) -- that learns from the problem-solving activity of expert users. In this talk we propose a technique, VBL, by which such a system can learn new problem-reduction operators, or @i(decomposition methods), based on a verification of the correctness of example decompositions. We also discuss two important limitations of the VBL technique -- intractability of verification and specificity of generalization -- and propose solutions to them.