---
title: "Learning Short-Term Weights for GSAT"
authors:
  - "Jeremy Frank"
doi: ""
url: "https://ijcai.org/proceedings/1997/57"
pdf: "https://ijcai.org/proceedings/1997/0057.pdf"
venue: "Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence"
year: 1997
pages: "384-390"
track: "Constraint Satisfaction"
keywords:
---

# Learning Short-Term Weights for GSAT

Jeremy Frank

*Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence*, pages 384-390.

## Abstract

We investigate an improvement to GSAT which associates a weight with each clause. We change the objective function so that GSAT moves to assignments maximizing the weight of satisfied clauses, and each clause's weight is changed when GSAT moves to an assignment in which this clause is unsatisfied. We present results showing that this version of GSAT has good performance when clause weights are reduced geometrically throughout the course of a single try. We conclude that clause weights are best interpreted as short-term, context sensitive indicators of how hard different clauses are to satisfy.