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An experiment in the application of similarity‐based learning to programming by example
Author(s) -
Mitrović Antonija,
Witten Ian H.,
Maulsby David L.
Publication year - 1994
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.4550090402
Subject(s) - computer science , similarity (geometry) , domain (mathematical analysis) , process (computing) , artificial intelligence , task (project management) , scheme (mathematics) , machine learning , theoretical computer science , programming language , image (mathematics) , mathematics , management , economics , mathematical analysis
Programming by example is a powerful way of bestowing on nonprogrammers the ability to communicate tasks to a computer. When creating procedures from examples it is necessary to be able to infer the existence of variables, conditional branches, and loops. This article explores the role of empirical or “similarity‐based” learning in this process. For a concrete example of a procedure induction system, we use an existing scheme called M ETAMOUSE which allows graphical procedures to be specified from examples of their execution. A procedure is induced from the first example, and can be generalized in accordance with examples encountered later on. We describe how the system can be enhanced with Mitchell's candidate elimination algorithm, one of the simplest empirical learning techniques, to improve its ability to recognize constraints in a comprehensive and flexible manner. Procedure induction is, no doubt, a very complex task. This work revealed usefulness and effectiveness of empirical learning in procedure induction, although it cannot be a complete substitute for specific preprogrammed, domain knowledge in situations where this is readily available. However, in domains such as graphical editing, where knowledge is incomplete and/or incorrect, the best way to pursue may prove to be a combination of similarity‐ and explanation‐based learning. © 1994 John Wiley & Sons, Inc.