论文标题
Fanoos XAI系统中自动操作员选择的基于学习的方法
A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System
论文作者
论文摘要
我们描述了Fanoos Xai系统的扩展[Bayani等2022]，该系统使该系统能够学习适当的操作，以满足用户对描述的要求或多或少抽象。具体而言，分析系统的描述存储在状态中，为了使描述或多或少地抽象，Fanoos从大型库中选择操作员以应用于状态并生成新的描述。主要使用的手写方法用于操作员选择；当前的工作使Fanoos能够利用经验来学习在特定情况下应用的最佳操作员，平衡探索和剥削，并在可用时利用专家见解，并利用当前状态和过去状态之间的相似性。此外，为了引导学习过程（即，就像在课程学习中一样），我们描述了我们实施的模拟用户；该模拟使Fanoos能够获得一般的见解，从而启用合理的行动课程，后来可以通过与真实用户的经验来完善的见解，而不是完全从头开始与人类互动。可以在https：// github/dbay-ani/operator_selection_leartenning_extensions_for_fanoos上找到实施本文中描述的方法的代码。
We describe an extension of the Fanoos XAI system [Bayani et al 2022] which enables the system to learn the appropriate action to take in order to satisfy a user's request for description to be made more or less abstract. Specifically, descriptions of systems under analysis are stored in states, and in order to make a description more or less abstract, Fanoos selects an operator from a large library to apply to the state and generate a new description. Prior work on Fanoos predominately used hand-written methods for operator-selection; this current work allows Fanoos to leverage experience to learn the best operator to apply in a particular situation, balancing exploration and exploitation, leveraging expert insights when available, and utilizing similarity between the current state and past states. Additionally, in order to bootstrap the learning process (i.e., like in curriculum learning), we describe a simulated user which we implemented; this simulation allows Fanoos to gain general insights that enable reasonable courses of action, insights which later can be refined by experience with real users, as opposed to interacting with humans completely from scratch. Code implementing the methods described in the paper can be found at https://github/DBay-ani/Operator_Selection_Learning_Extensions_For_Fanoos.
