标题： 对比稀疏自编码器在棋类博弈代理规划解释中的应用 显示英文标题

标题： Contrastive Sparse Autoencoders for Interpreting Planning of Chess-Playing Agents

摘要： 人工智能引领棋类系统达到超人类水平，但这些系统严重依赖黑箱算法。 这在确保最终用户透明度方面是不可持续的，尤其是在这些系统负责敏感决策时。 最近的可解释性研究显示，深度神经网络（DNN）的内部表示是可以理解的，并且包含了人类可理解的概念。 然而，这些方法很少被上下文化，通常基于单一隐藏状态，这使得它们无法解释多步骤推理，例如规划。 在这方面，我们提出了对比稀疏自编码器（CSAE），一个用于研究游戏轨迹对的新框架。 使用CSAE，我们能够提取并解释对棋类代理计划有意义的概念。 在提出自动特征分类法之前，我们主要关注CSAE特征的定性分析。 此外，为了评估我们训练的CSAE的质量，我们设计了合理性检查以消除结果中的虚假相关性。 显示英文摘要

摘要： AI led chess systems to a superhuman level, yet these systems heavily rely on black-box algorithms. This is unsustainable in ensuring transparency to the end-user, particularly when these systems are responsible for sensitive decision-making. Recent interpretability work has shown that the inner representations of Deep Neural Networks (DNNs) were fathomable and contained human-understandable concepts. Yet, these methods are seldom contextualised and are often based on a single hidden state, which makes them unable to interpret multi-step reasoning, e.g. planning. In this respect, we propose contrastive sparse autoencoders (CSAE), a novel framework for studying pairs of game trajectories. Using CSAE, we are able to extract and interpret concepts that are meaningful to the chess-agent plans. We primarily focused on a qualitative analysis of the CSAE features before proposing an automated feature taxonomy. Furthermore, to evaluate the quality of our trained CSAE, we devise sanity checks to wave spurious correlations in our results.