A Model of Interactive Teaching

Previous teaching models in the learning theory community have been batch models. That is, in these models the teacher has generated a single set of helpful examples to present to the learner. In this paper we present an interactive model in which the learner has the ability to ask queries as in the query learning model of Angluin. We show that this model is at least as powerful as previous teaching models. We also show that anything learnable with queries, even by a randomized learner, is teachable in our model. In all previous teaching models, all classes shown to be teachable are known to be efficiently learnable. An important concept class that is not known to be learnable is DNF formulas. We demonstrate the power of our approach by providing a deterministic teacher and learner for the class of DNF formulas. The learner makes only equivalence queries and all hypotheses are also DNF formulas.

[1]  Balas K. Natarajan,et al.  On learning Boolean functions , 1987, STOC.

[2]  M. Kearns,et al.  On the complexity of teaching , 1991, COLT '91.

[3]  Sally A. Goldman,et al.  Teaching a Smarter Learner , 1996, J. Comput. Syst. Sci..

[4]  Simon Kasif,et al.  Learning with a Helpful Teacher , 1991, IJCAI.

[5]  D. Angluin,et al.  Randomly fallible teachers: Learning monotone DNF with an incomplete membership oracle , 1991, Machine Learning.

[6]  Tibor Hegedűs,et al.  Generalized teaching dimensions and the query complexity of learning , 1995, Annual Conference Computational Learning Theory.

[7]  Ronald L. Rivest,et al.  Learning Binary Relations and Total Orders , 1989, COLT 1989.

[8]  Ronald L. Rivest,et al.  Being taught can be faster than asking questions , 1995, COLT '95.

[9]  Paul W. Goldberg,et al.  Learning unions of boxes with membership and equivalence queries , 1994, COLT '94.

[10]  Nader H. Bshouty,et al.  Exact learning via the Monotone theory , 1993, Proceedings of 1993 IEEE 34th Annual Foundations of Computer Science.

[11]  Michael Kearns,et al.  Computational complexity of machine learning , 1990, ACM distinguished dissertations.

[12]  Sampath Kannan,et al.  Oracles and Queries That Are Sufficient for Exact Learning , 1996, J. Comput. Syst. Sci..

[13]  Andrew Tomkins,et al.  A computational model of teaching , 1992, COLT '92.

[14]  Rusins Freivalds,et al.  On the Power of Inductive Inference from Good Examples , 1993, Theor. Comput. Sci..

[15]  Carl Smith,et al.  Testing Geometric Objects , 1994, Comput. Geom..

[16]  Jeffrey C. Jackson,et al.  An efficient membership-query algorithm for learning DNF with respect to the uniform distribution , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[17]  Tibor Hegedüs Combinatorial Results on the Complexity of Teaching and Learning , 1994, MFCS.

[18]  Kathleen Romanik,et al.  Approximate testing and learnability , 1992, COLT '92.

[19]  John Shawe-Taylor,et al.  On exact specification by examples , 1992, COLT '92.

[20]  Dana Angluin,et al.  Learning with malicious membership queries and exceptions (extended abstract) , 1994, COLT '94.