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.

翻译：我们描述Fanoos XAI系统[Bayani等人2022]的延伸,该系统使该系统能够学习应采取何种适当行动,以满足用户的描述请求,使描述或多或少具有抽象性。具体地说,所分析的系统描述储存在各州,为了使描述更加或少具有抽象性,Fanoos从一个大型图书馆中选择一个操作员,以适用于州并产生新的描述。以前在Fanoos上的工作主要使用手写方法进行操作员选择;目前的工作使Fanoos能够利用经验,学习最佳操作员在特定情况下应用,平衡探索和利用,利用现有专家的见解,并利用当前状态和过去状态之间的相似性。此外,为了绑紧学习过程(例如课程学习中),我们描述了一个模拟用户;这种模拟可以让Fanoos获得一般的洞察力,从而能够采取合理的行动方针,后来可以通过与实际用户的经验来完善这些洞察力,而不是与人类完全从头进行互动。在https://Labar_Obaros/Oayoments。执行文件中描述了Starment_Ostarefard_Ostaperator_Arrationorestormodestration