关键词: 归纳逻辑程序设计, 反事实, 多任务学习

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