计算机工程与应用2024，Vol.60Issue(20)：168-179,12.DOI:10.3778/j.issn.1002-8331.2405-0434
基于反事实的相关背景知识获取方法
Towards Related Background Knowledge Acquisition via Counterfactual
摘要
Abstract
In multi-task learning,a learner adds the learned programs into background knowledge(BK)and reuses them to learn other programs.Continually acquiring BK can lead to the problem of excessive BK,which overwhelms a learning system.Hence,it is necessary to forget irrelevant BK.However,existing forgetting approaches rarely consider the rele-vance between BK and learning tasks,commonly providing the same BK for different induction tasks.To address this issue,this paper proposes a relevance identification approach based on counterfactual thinking,termed counterfactual acquisition.This approach first measures each hypothesis's contribution to the learning task using a relevance function.Then,it retains only those hypotheses whose relevance function values exceed a predefined threshold.Moreover,this approach is applied to inductive logic programming(ILP)through the introduction of a multi-task ILP learner named Countergol.Theoretical analysis demonstrates that Countergol can reduce the hypothesis space and sample complexity size.Experimental comparisons against other forgetting approaches show that Countergol outperforms similar methods.关键词
归纳逻辑程序设计/反事实/多任务学习Key words
inductive logic programming/counterfactual/multi-task learning分类
信息技术与安全科学引用本文复制引用
王学敏,包旭光,常亮,郝远静..基于反事实的相关背景知识获取方法[J].计算机工程与应用,2024,60(20):168-179,12.基金项目
国家自然科学基金(U22A2099,61966009) （U22A2099,61966009）
广西研究生教育创新项目(YCBZ2023130). （YCBZ2023130）
