KEDAR-CABELLI@RED.RUTGERS.EDU (Smadar) (11/09/86)
Reminder: Dissertation Defense for Rich Keller Time and Place: Thursday, Nov. 13, 1:30 p.m., Hill 423 Committee: Tom Mitchell (chair) Thorne McCarty Lou Steinberg Jack Mostow Abstract: The Role of Explicit Contextual Knowledge in Learning Concepts to Improve Performance Richard M. Keller (KELLER@RED.RUTGERS.EDU) This dissertation addresses some of the difficulties encountered when using artificial intelligence-based, inductive concept learning methods to improve an existing system's performance. The underlying problem is that inductive methods are insensitive to changes in the system being improved by learning. This insensitivity is due to the manner in which contextual knowledge is represented in an inductive system. Contextual knowledge consists of knowledge about the context in which concept learning takes place, including knowledge about the desired form and content of concept descriptions to be learned (target concept knowledge), and knowledge about the system to be improved by learning and the type of improvement desired (performance system knowledge). A considerable amount of contextual knowledge is "compiled" by an inductive system's designers into its data structures and procedures. Unfortunately, in this compiled form, it is difficult for the learning system to modify its contextual knowledge to accommodate changes in the learning context over time. This research investigates the advantages of making contextual knowledge explicit in a concept learning system by representing that knowledge directly, in terms of express declarative structures. The thesis of this research is that aside from facilitating adaptation to change, explicit contextual knowledge is useful in addressing two additional problems with inductive systems. First, most inductive systems are unable to learn approximate concept descriptions, even when approximation is necessary or desirable to improve performance. Second, the capability of a learning system to generate its own concept learning tasks appears to be outside the scope of current inductive systems. To investigate the thesis, this study introduces an alternative concept learning framework -- the concept operationalization framework -- that requires various types of contextual knowledge as explicit inputs. To test this new framework, an existing inductive concept learning system (the LEX system [Mitchell et al. 81]) was rewritten as a concept operationalization system (the MetaLEX system). This dissertation describes the design of MetaLEX and reports results of several experiments performed to test the system. Results confirm the utility of explicit contextual knowledge, and suggest possible improvements in the representations and methods used by the system. -------