Machine learning under felicity conditions: exploiting pedagogical behavior

  • John D. Lewis
  • Bruce A. MacDonald
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)


In task instruction a knowledgeable and well-intentioned teacher will provide focusing information, highlight decision points, indicate structure, and so forth. Recognition of these communicative acts is critical for practical learning. This paper describes an extension to explanation-based learning that augments domain knowledge with knowledge of constraints on the structure of instructional discourse. VanLehn’s felicity conditions have formalized some of the constraints guiding a teacher during the presentation of examples. In this paper we weaken his condition that each lesson be an optimal set of examples, instead assuming that the teacher provides an explanation. Instruction is viewed as planned explanation, and plan recognition is applied to the problem at both domain and discourse levels, and extended to allow the learner to have incomplete knowledge. The model includes (a) a domain level plan recognizer and (b) a discourse level plan recognizer that cues (c) a third level of plan structure rewriting rules. Details are given of an example in which a robot apprentice is instructed in the building of arches. Tractable learning and instructability are our goals.


Domain Knowledge Pedagogical Behavior Discourse Context Abstract Operator Explanatory Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© British Computer Society 1993

Authors and Affiliations

  • John D. Lewis
    • 1
  • Bruce A. MacDonald
    • 1
  1. 1.Knowledge Sciences Inst., Computer Science Dept.The University of CalgaryCalgaryCanada

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