LEW: learning by watching

Abstract

LEW (learning by watching), a machine learning system, is described. It was designed for knowledge acquisition in cooperation with an expert. LEW learns from examples of problem-solution (or question-answer) pairs by generalizing on differences in those pairs. In this sense, it belongs to the family of inductive learning methods. It provides for using background knowledge through the environment component of problem-solution pairs, thereby making constructive learning possible. The user can control the extent of the generalizations performed by LEW. The learning method is incremental and, to some extent, noise-resistant. The authors give an informal overview of the knowledge representation and the basic learning algorithm of LEW and indicate that the system's design meets the stated criteria and enables it to give helpful assistance, even in situations characterized by noisy or conflicting information and by lack of extensive background knowledge. The theory behind LEW is presented, along with rigorous definitions of its fundamental concepts and a general description of its learning algorithm. LEW's functioning with some larger examples, one from the QUIZ Advisor domain and another from the domain of block-world planning, is illustrated. The authors compare LEW with several other knowledge acquisition tools and introduce a precise characterization of learning from near misses and a near-miss metric. Possible extensions and enhancements to the system are noted. >