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

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.[PUBLICATION ABSTRACT]