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Explanatory machine learning for sequential human teaching

Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid

TL;DR

The results show that sequential teaching of concepts with increasing complexity has a beneficial effect on human comprehension and leads to human re-discovery of divide-and-conquer problem-solving strategies, and allows adaptations of human problem-Solving strategy with better performance when machine-learned explanations are also presented.

Abstract

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.

Explanatory machine learning for sequential human teaching

TL;DR

The results show that sequential teaching of concepts with increasing complexity has a beneficial effect on human comprehension and leads to human re-discovery of divide-and-conquer problem-solving strategies, and allows adaptations of human problem-Solving strategy with better performance when machine-learned explanations are also presented.

Abstract

The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.
Paper Structure (8 sections, 1 equation, 1 figure, 1 table)

This paper contains 8 sections, 1 equation, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Human learning in curricula with explanations
  4. Teaching between the machine and humans
  5. Operational measures of human understanding in AI
  6. Theoretical framework
  7. Meta-interpretive learning
  8. MIL for learning sorting algorithm

Figures (1)

  • Figure 1: An illustration of teaching materials of merging and sorting that can be learned in incremental order (left to right). All fruits have different weights which can be pairwise compared using the balance scale at the corner. On the left-hand side, two sets of apples should be merged into a larger collection in increasing weights from left to right. On the right-hand side, a set of bananas needs to be arranged in increasing weights from left to right.