Nagging: A Distributed, Adversarial Search-Pruning Technique Applied to First-Order Inference

Authors:
David Sturgill;Alberto Maria Segre
Affiliations:
Baylor University, Waco, Tx 76798, U.S.A. e-mail: sturgill@cs.baylor.edu;the University of Iowa, Iowa City, Ia 52242, U.S.A. e-mail: segre@cs.uiowa.edu
Venue:
Journal of Automated Reasoning
Year:
1997

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Abstract

This article introduces a parallel search-pruning technique callednagging. Nagging is sufficiently general to be effective in a number ofdomains; here we focus on an implementation for first-order theorem proving,a domain both responsive to a very simple nagging model and amenable to manyrefinements of this model. Nagging’s scalability and intrinsic faulttolerance make it particularly suitable for application in commonlyavailable, low-bandwidth, high-latency distributed environments. We presentseveral nagging models of increasing sophistication, demonstrate theireffectiveness empirically, and compare nagging with related work in parallelsearch.