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.

[1]  Henry A. Kautz.  A formal theory of plan recognition , 1987 .

[2]  Henry Lieberman,et al.  Watch what I do: programming by demonstration , 1993 .

[3]  Ian H. Witten,et al.  Cima: An Interactive Concept Learning System for End-User Applications , 1997, Appl. Artif. Intell..

[4]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[5]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[6]  David Kurlander,et al.  Chimera: example-based graphical editing , 1993 .

[7]  Oren Etzioni,et al.  A Sound and Fast Goal Recognizer , 1995, IJCAI.

[8]  Ian H. Witten,et al.  Detecting sequential structure , 1995 .

[9]  Ian H. Witten,et al.  Interacting with learning agents: Implications for ML from HCI , 1996 .

[10]  Henry Lieberman,et al.  Tinker: a programming by demonstration system for beginning programmers , 1993 .

[11]  Brad A. Myers.  Peridot: creating user interfaces by demonstration , 1993 .

[12]  Pattie Maes,et al.  Learning Interface Agents , 1993, AAAI.

[13]  Martha E. Pollack,et al.  Inferring domain plans in question-answering , 1986 .

[14]  William W. Cohen.  Grammatically Biased Learning: Learning Logic Programs Using an Explicit Antecedent Description Language , 1994, Artif. Intell..

[15]  Daniel S. Weld.  An Introduction to Least Commitment Planning , 1994, AI Mag..

[16]  Allen Cypher,et al.  Eager: programming repetitive tasks by demonstration , 1993 .

[17]  Pat Langley.  Editorial: Advice to Machine Learning Authors , 2005, Machine Learning.

[18]  Oren Etzioni,et al.  Scaling Up Goal Recognition , 1996, KR.

[19]  Mathias Bauer,et al.  Acquisition of Abstract Plan Descriptions for Plan Recognition , 1998, AAAI/IAAI.

[20]  Jeffrey C. Schlimmer,et al.  Software Agents: Completing Patterns and Constructing User Interfaces , 1993, J. Artif. Intell. Res..

[21]  Ian H. Witten,et al.  Identifying Hierarchical Structure in Sequences: A linear-time algorithm , 1997, J. Artif. Intell. Res..

[22]  Nicholas Kushmerick,et al.  Wrapper Induction for Information Extraction , 1997, IJCAI.

[23]  Diane J. Litman,et al.  Terminological Reasoning with Constraint Networks and an Application to Plan Recognition , 1992, KR.

[24]  Gordon W. Paynter,et al.  Generalising programming by demonstration , 1996, Proceedings Sixth Australian Conference on Computer-Human Interaction.

[25]  Gordon W. Paynter.  Generalizing Programming by Demonstration , 1996 .

[26]  Jack Mostow,et al.  Learning Logical Definitions from Relations , 1990 .