Programming by Demonstration: An Inductive Learning Formulation

Although Programming by Demonstration (PBD) has the potential to improve the productivity of unsophisticated users, previous PBD systems have used brittle, heuristic, domain-speci c approaches to execution-trace generalization. In this paper we de ne two applicationindependent methods for performing generalization that are based on well-understood machine learning technology. TGenvs uses version-space generalization, and TGenfoil is based on the FOIL inductive logic programming algorithm. We analyze each method both theoretically and empirically, arguing that TGenvs has lower sample complexity, but TGenfoil can learn a much more interesting class of programs. Keywords Programming by Demonstration, machine learning, inductive logic programming, version spaces

• Type: Conference
• Year: 1999
• Where: IUI
• Authors: Tessa A. Lau, Daniel S. Weld
• Keywords: FOIL Inductive Logic, Inductive Logic Programming, IUI 1999, Machine Learning, Software Engineering