---
title: "Heuristics for Inductive Learning"
authors:
  - "Steven Salzberg"
doi: ""
url: "https://ijcai.org/proceedings/1985/116"
pdf: "https://ijcai.org/proceedings/1985/0116.pdf"
venue: "Proceedings of the Ninth International Joint Conference on Artificial Intelligence"
year: 1985
pages: "603-608"
track: "Learning and Acquisition"
keywords:
---

# Heuristics for Inductive Learning

Steven Salzberg

*Proceedings of the Ninth International Joint Conference on Artificial Intelligence*, pages 603-608.

## Abstract

A number of heuristics have been developed which greatly reduce the search space a learning program must consider in its attempt to construct hypotheses about why a failure occurred. These heuristics have been implemented in the HANDICAPPER system [Salzberg 1983, Atk inson & Salzberg 1984], in which they si gnif icantly improved predicti ve ability while demonstrating a remarkable learning curve. The rationalization process has been developed as a verification system for the hypotheses suggested by the heuristics. Rationalization uses the causal knowledge of the system to ascertain whether or not a hypothesis is reasonable . If the hypothesis is not supported by causal knowledge, it is discarded and another hypothesis must be generated by the heuristics. The resulting learning system , by integrating causal knowledge with heuristic search, has quickly gone from essentially random predictive accuracy to a system which consistently outperforms the experts at predicting events in its problem domain .