论文标题
使用用户适应性可视化的线性模型树解释深钢筋学习对接代理
Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization
论文作者
论文摘要
深度神经网络（DNN）在海洋机器人技术领域中可能很有用，但是它们的效用价值受其黑盒子性质的限制。可解释的人工智能方法试图了解这种黑盒如何做出决定。在这项工作中，线性模型树（LMT）用于近似模拟环境中控制自主表面容器（ASV）的DNN，然后与DNN并行运行，以实时以特征归因的形式提供解释。一个模型的理解程度不仅取决于解释本身，还取决于其表现效果和适应所述解释的接收者。不同的最终用户可能需要两种类型的解释，也需要对这些解释的不同表示。这项工作的主要贡献是（1）通过引入树木分裂中的特征订购来构建LMT的贪婪方法的准确性和建立时间，（2）概述了海员/操作员的特征，以及开发人员的特征，并作为两个不同的媒介的特征和（3）的特征及（3）的特征（3）的特征（3）的特征（3）概述（3）。 LMT给出的归因于开发人员是系统的最终用户，而何时海员或操作员是最终用户，基于其不同的特征。
Deep neural networks (DNNs) can be useful within the marine robotics field, but their utility value is restricted by their black-box nature. Explainable artificial intelligence methods attempt to understand how such black-boxes make their decisions. In this work, linear model trees (LMTs) are used to approximate the DNN controlling an autonomous surface vessel (ASV) in a simulated environment and then run in parallel with the DNN to give explanations in the form of feature attributions in real-time. How well a model can be understood depends not only on the explanation itself, but also on how well it is presented and adapted to the receiver of said explanation. Different end-users may need both different types of explanations, as well as different representations of these. The main contributions of this work are (1) significantly improving both the accuracy and the build time of a greedy approach for building LMTs by introducing ordering of features in the splitting of the tree, (2) giving an overview of the characteristics of the seafarer/operator and the developer as two different end-users of the agent and receiver of the explanations, and (3) suggesting a visualization of the docking agent, the environment, and the feature attributions given by the LMT for when the developer is the end-user of the system, and another visualization for when the seafarer or operator is the end-user, based on their different characteristics.
