A hallmark of a good XAI system is explanations that users can understand and
act on. In many cases, this requires a system to offer causal or counterfactual
explanations that are intelligible. Cognitive science can help us understand
what kinds of explanations users might expect, and in which format to frame
these explanations. We briefly review relevant literature from the cognitive
science of explanation, particularly as it concerns teleology, the tendency to
explain a decision in terms of the purpose it was meant to achieve. We then
report empirical data on how people generate explanations for the behavior of
autonomous vehicles, and how they evaluate these explanations. In a first
survey, participants (n=54) were shown videos of a road scene and asked to
generate either mechanistic, counterfactual, or teleological verbal
explanations for a vehicle's actions. In the second survey, a different set of
participants (n=356) rated these explanations along various metrics including
quality, trustworthiness, and how much each explanatory mode was emphasized in
the explanation. Participants deemed mechanistic and teleological explanations
as significantly higher quality than counterfactual explanations. In addition,
perceived teleology was the best predictor of perceived quality and
trustworthiness. Neither perceived teleology nor quality ratings were affected
by whether the car whose actions were being explained was an autonomous vehicle
or was being driven by a person. The results show people use and value
teleological concepts to evaluate information about both other people and
autonomous vehicles, indicating they find the 'intentional stance' a convenient
abstraction. We make our dataset of annotated video situations with
explanations, called Human Explanations for Autonomous Driving Decisions
(HEADD), publicly available, which we hope will prompt further research.

AI 系统的关键是提供用户可以理解和采取行动的解释。本论文通过对认知科学和解释相关的文献研究，以及调查人们对自主驾驶车辆行为解释的生成和评价，发现人们倾向于用目的论解释决策和行为，无论是对于其他人还是自主驾驶车辆都表现出这一倾向。研究结果促进了对于自主驾驶决策的人工注释视频情况和解释数据集 (HEADD) 的进一步研究。

自动驾驶车辆的行为解释中涉及人的属性和目的

People Attribute Purpose to Autonomous Vehicles When Explaining Their  Behavior

This paper presented an XAI system specification and an interactive dialogue
model to facilitate the creation of Explanation Experiences (EE). Such
specifications combine the knowledge of XAI, domain and system experts of a use
case to formalise target user groups and their explanation needs and to
implement explanation strategies to address those needs. Formalising the XAI
system promotes the reuse of existing explainers and known explanation needs
that can be refined and evolved over time using user evaluation feedback. The
abstract EE dialogue model formalised the interactions between a user and an
XAI system. The resulting EE conversational chatbot is personalised to an XAI
system at run-time using the knowledge captured in its XAI system
specification. This seamless integration is enabled by using Behaviour Trees
(BT) to conceptualise both the EE dialogue model and the explanation
strategies. In the evaluation, we discussed several desirable properties of
using BTs over traditionally used STMs or FSMs. BTs promote the reusability of
dialogue components through the hierarchical nature of the design. Sub-trees
are modular, i.e. a sub-tree is responsible for a specific behaviour, which can
be designed in different levels of granularity to improve human
interpretability. The EE dialogue model consists of abstract behaviours needed
to capture EE, accordingly, it can be implemented as a conversational,
graphical or text-based interface which caters to different domains and users.
There is a significant computational cost when using BTs for modelling
dialogue, which we mitigate by using memory. Overall, we find that the ability
to create robust conversational pathways dynamically makes BTs a good candidate
for designing and implementing conversation for creating explanation
experiences.

该研究提出了一种 XAI 系统规范和交互式对话模型，以促进 Explanation Experiences（EE）的创建，并使用行为树（BT）实现对 EE 用户交互接口的设计与实现，这种方法能够通过用户反馈不断改进和推进，同时也使用 BTs 解决了计算成本方面的问题。

用行为树创建对话式解释体验

Behaviour Trees for Creating Conversational Explanation Experiences

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.

本文介绍了 Fanoos XAI 系统的扩展，该扩展使该系统能够学习适当的操作以满足用户的请求，包括抽象与非抽象描述，并使用了一些计算机模拟的方法使得其具有足够的泛化能力。