Admissible Hypotheses and Enhanced Learning

Abstract

This paper discusses strategies for moving through sequences of hypotheses, each one of which is produced in response to an experimental test of the previous member. Previous discussions of this issue have all agreed that hypotheses deductively incompatible with the evidence at stage is cannot appear in the sequence beyond in This paper contends that this conclusion is untenable. The use of oversimplified models has led investigators into overlooking epistemological properties of more complex hypotheses which allow more sophisticated methodologies in testing and hypothesis generation. In particular, it is shown that testing already falsified hypotheses may give more experimental information than other, more traditional strategies. This is shown by considering a popular board game, but a realistic example is introduced to demonstrate the general importance and usefulness of the strategy.

Cite

Text

Hunt. "Admissible Hypotheses and Enhanced Learning." International Joint Conference on Artificial Intelligence, 1983.

Markdown

[Hunt. "Admissible Hypotheses and Enhanced Learning." International Joint Conference on Artificial Intelligence, 1983.](https://mlanthology.org/ijcai/1983/hunt1983ijcai-admissible/)

BibTeX

@inproceedings{hunt1983ijcai-admissible,
  title     = {{Admissible Hypotheses and Enhanced Learning}},
  author    = {Hunt, G. M. K.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {1983},
  pages     = {444-446},
  url       = {https://mlanthology.org/ijcai/1983/hunt1983ijcai-admissible/}
}