---
title: "Inference without Chaining"
authors:
  - "Alan M. Frisch"
doi: ""
url: "https://ijcai.org/proceedings/1987/102"
pdf: "https://ijcai.org/proceedings/1987/0102.pdf"
venue: "Proceedings of the Tenth International Joint Conference on Artificial Intelligence"
year: 1987
pages: "515-518"
track: "Knowledge Representation"
keywords:
---

# Inference without Chaining

Alan M. Frisch

*Proceedings of the Tenth International Joint Conference on Artificial Intelligence*, pages 515-518.

## Abstract

The problem of specifying, constructing , and understanding specialized, limited inference systems arises in many areas of AI. As a first step towards solving this problem this paper recommends the development of an inference engine that is limited by its inability to chain together two pieces of a representation in order to derive a third. A method for using model theory to specify limited inference is introduced and then used to specify an inference engine via a t hree-valued logic. This inference engine is proved to be the strongest one that does no chaining, modulo the way that it divides the representation into pieces. Thus , the specification captures the set of all inferences that require no chaining. This paper also surveys and compares a number of systems that do no chaining as well as some that allow only selected forms of chaining.