---
title: "Efficient Consequence Finding"
authors:
  - "Laurent Simon"
  - "Alvaro del Val"
doi: ""
url: "https://ijcai.org/proceedings/2001/51"
pdf: "https://ijcai.org/proceedings/2001/0051.pdf"
venue: "Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence"
year: 2001
pages: "359-369"
track: ""
keywords:
---

# Efficient Consequence Finding

Laurent Simon, Alvaro del Val

*Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence*, pages 359-369.

## Abstract

We present an extensive experimental study of consequence-finding algorithms based on kernel resolution, using both a trie-based and a novel ZBDD-based implementation, which uses ZeroSuppressed Binary Decision Diagrams to concisely store and process very large clause sets. Our study considers both the full prime implicate task and applications of consequence-finding for restricted target languages in abduction, model-based and faulttree diagnosis, and polynomially-bounded knowledge compilation. We show that the ZBDD implementation can push consequence-finding to a new limit, solving problems which generate over 1070 clauses.