Programming by demonstration
- 1 December 1998
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 145-152
- https://doi.org/10.1145/291080.291104
Abstract
Although Programming by Demonstration (PBD) has the potential to improve the productivity of unsophis-ticated users, previous PBD systems have used brittle, heuristic, domain-speci c approaches to execution-trace generalization. In this paper we de ne two application-independent methods for performing generalization that are based on well-understood machine learning technol-ogy.,vs uses version-space generalization, and,foil is based on the FOIL inductive logic pro-gramming algorithm. We analyze each method both theoretically and empirically, arguing that TGen vs has lower sample complexity, but TGen foil can learn a much more interesting class of programs.Keywords
This publication has 7 references indexed in Scilit:
- Cima: An interactive concept learning system for end-user applicationsApplied Artificial Intelligence, 1997
- Grammatically biased learning: Learning logic programs using an explicit antecedent description languageArtificial Intelligence, 1994
- Software Agents: Completing Patterns and Constructing User InterfacesJournal of Artificial Intelligence Research, 1993
- The Utility of Knowledge in Inductive LearningMachine Learning, 1992
- Editorial: Advice to Machine Learning AuthorsMachine Learning, 1990
- Generalization as searchArtificial Intelligence, 1982
- On Closed World Data BasesPublished by Springer Nature ,1978