Problem Decomposition and the Learning of Skills
Abstract
One dimension of “divide and conquer” in problem solving concerns the domain and its subdomains. Humans learn the general structure of a domain while solving particular learning problems in it. Another dimension concerns the solver's goals and subgoals. Finding good decompositions is a major AI tactic both for defusing the combinatorial explosion and for ensuring a transparent end-product. In machine learning, pre-occupation with free-standing performance has led to comparative neglect of this resource, illustrated under the following headings. 1. Automatic manufacture of new attributes from primitives (“constructive induction”). 2. Machine learning within goal-subgoal hierarchies (“structured induction”). 3. Reconstruction of skills from human performance data (“behavioural cloning”).
Cite
Text
Michie. "Problem Decomposition and the Learning of Skills." European Conference on Machine Learning, 1995. doi:10.1007/3-540-59286-5_46Markdown
[Michie. "Problem Decomposition and the Learning of Skills." European Conference on Machine Learning, 1995.](https://mlanthology.org/ecmlpkdd/1995/michie1995ecml-problem/) doi:10.1007/3-540-59286-5_46BibTeX
@inproceedings{michie1995ecml-problem,
title = {{Problem Decomposition and the Learning of Skills}},
author = {Michie, Donald},
booktitle = {European Conference on Machine Learning},
year = {1995},
pages = {17-31},
doi = {10.1007/3-540-59286-5_46},
url = {https://mlanthology.org/ecmlpkdd/1995/michie1995ecml-problem/}
}