Mlnet Familiarization Workshop Knowledge Level Models of Machine Learning

Report on the workshop on Knowledge Level Models of Machine Learning that was organized in the context of the second series of MLNet familiariza-1 Topic Description The aim of this workshop was to discuss knowledge level modeling applied to machine learning systems and algorithms. An important distinction in current expert systems research is the one between knowledge level and symbol level Newell, 1982]. Systems can be described at either of these levels. Brieey stated, a knowledge level description emphasizes the knowledge contents of a system (e.g. goals, actions and knowledge used in a rational way) whereas the symbol level describes its computational realization (in terms of representations and inference mechanisms). There is a consensus that modeling at the knowledge level is a useful intermediate step in the development of an expert system Steels and McDermott, 1993]. So called second generation expert systems explicitly incorporate aspects of their knowledge level structure, resulting in potential advantages for knowledge acquisition, design, implementation, explanation and maintenance (see David et al., 1993] for an overview on the state of the art). The technical goal is to construct generic components which can be reused and reened 1