---
title: "An Algorithm that Infers Theories from Facts"
authors:
  - "Ehud Shapiro"
doi: ""
url: "https://ijcai.org/proceedings/1981/87"
pdf: "https://ijcai.org/proceedings/1981/0087.pdf"
venue: "Proceedings of the Seventh International Joint Conference on Artificial Intelligence"
year: 1981
pages: "446-450"
track: "Theoretical Foundations"
keywords:
---

# An Algorithm that Infers Theories from Facts

Ehud Shapiro

*Proceedings of the Seventh International Joint Conference on Artificial Intelligence*, pages 446-450.

## Abstract

A framework for inductive inference in logic is presented: a Mode/Inference Problem is defined, and it is shown that problems of machine learning and program synthesis from examples can be formulated naturally as model inference problems. A general. incremental inductive inference algorithm for solving model inference problems is developed. This algorithm is based on Popper's methodology of conjectures and refutations [II]. The algorithm can be shown to identify in the limit [3] any model in a family of complexity classes of models. is most powerful of its kind, and is flexible enough to have been successfully implemented for several concrete domains . The Model Inference System is a Prolog implementation of this algorithm, specialized to infer theories in Horn form . It can infer axiomatizations of concrete models from a small number of facts in a practical amount of time.