This paper introduces a new parser-driven Japanese tutor called BANZAI, and presents an experimental study comparing the relative effectiveness of deductive (rule driven) feedback and inductive (example driven) feedback implemented in the BANZAI program. The study finds that deductive feedback can be superior to inductive feedback when the learning task involves relatively complex structures wh...

This paper introduces a logical model of inductive generalization, and specifically of the machine learning task of inductive concept learning (ICL). We argue that some inductive processes, like ICL, can be seen as a form of defeasible reasoning. We define a consequence relation characterizing which hypotheses can be induced from given sets of examples, and study its properties, showing they co...

This paper examines and compares several di erent approaches to the design of intelligent systems for diagnosis applications. These include expert systems (or knowledgebased systems), truth (or reason) maintenance systems, case-based reasoning systems, and inductive approaches like decision trees, arti cial neural networks (or connectionist systems), and statistical pattern classi cation system...

Experimental results are presented of experiments on using Machine Learning algorithms to extract a model of a human control skill The con trol task to be modeled is that of a pilot ying a F ight simulation on a levelled left turn Machine Learning algorithms used include C as a decision tree representative and FOIL and Indlog as Inductive Logic Pro gramming representatives Improvements on the m...

Morphological analysis is an important subtask in text-to-speech conversion, hyphenation, and other language engineering tasks. The traditional approach to performing morphological analysis is to combine a morpheme lexicon, sets of (linguistic) rules, and heuristics to find a most probable analysis. In contrast we present an inductive learning approach in which morphological analysis is reformu...