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

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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.

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Sturgill, D., Segre, A.M. Nagging: A Distributed, Adversarial Search-Pruning Technique Applied to First-Order Inference. Journal of Automated Reasoning 19, 347–376 (1997). https://doi.org/10.1023/A:1005885725562

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