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 relevance 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.

Key words: inductive logic programming, counterfactual, multi-task learning

摘要： 在多任务学习中，学习器会将已学到的知识添加到背景知识库中，并利用这些背景知识来辅助其他任务的学习。然而，随着背景知识的不断积累，知识库可能会变得庞大，给学习系统带来负担。因此，有必要对不相关的背景知识进行遗忘。现有的遗忘策略往往未充分考虑背景知识与学习任务之间的关联性，而是为不同的归纳任务提供相同的背景知识。针对这一问题，提出了一种基于反事实思维的相关性识别方法，即反事实获取法。该方法通过相关性函数评估每个假设对学习任务的具体贡献，仅保留那些相关性函数值超过设定阈值的假设。此外，该方法应用于归纳逻辑编程领域，设计出一个名为Countergol的多任务归纳逻辑编程学习器。理论分析显示，Countergol能够有效地缩减假设空间及样本复杂度。实验结果表明，通过与其他遗忘方法的对比，Countergol在大量任务学习中的优越性得到了进一步验证。

关键词: 归纳逻辑程序设计, 反事实, 多任务学习