Recent applications of autonomous agents and robots, such as self-driving
cars, scenario-based trainers, exploration robots, and service robots have
brought attention to crucial trust-related challenges associated with the
current generation of artificial intelligence (AI) systems. AI systems based on
the connectionist deep learning neural network approach lack capabilities of
explaining their decisions and actions to others, despite their great
successes. Without symbolic interpretation capabilities, they are black boxes,
which renders their decisions or actions opaque, making it difficult to trust
them in safety-critical applications. The recent stance on the explainability
of AI systems has witnessed several approaches on eXplainable Artificial
Intelligence (XAI); however, most of the studies have focused on data-driven
XAI systems applied in computational sciences. Studies addressing the
increasingly pervasive goal-driven agents and robots are still missing. This
paper reviews approaches on explainable goal-driven intelligent agents and
robots, focusing on techniques for explaining and communicating agents
perceptual functions (example, senses, and vision) and cognitive reasoning
(example, beliefs, desires, intention, plans, and goals) with humans in the
loop. The review highlights key strategies that emphasize transparency,
understandability, and continual learning for explainability. Finally, the
paper presents requirements for explainability and suggests a roadmap for the
possible realization of effective goal-driven explainable agents and robots.

该文章重点讨论解释目标驱动型智能代理人和机器人的方法，强调透明度、可理解性和持续学习的策略。它提出了可行的目标驱动可解释代理人和机器人的实现路径和要求。

可解释的目标导向智能体和机器人 —— 综合评述

Explainable Goal-Driven Agents and Robots -- A Comprehensive Review

Learning to navigate in complex environments with dynamic elements is an
important milestone in developing AI agents. In this work we formulate the
navigation question as a reinforcement learning problem and show that data
efficiency and task performance can be dramatically improved by relying on
additional auxiliary tasks leveraging multimodal sensory inputs. In particular
we consider jointly learning the goal-driven reinforcement learning problem
with auxiliary depth prediction and loop closure classification tasks. This
approach can learn to navigate from raw sensory input in complicated 3D mazes,
approaching human-level performance even under conditions where the goal
location changes frequently. We provide detailed analysis of the agent
behaviour, its ability to localise, and its network activity dynamics, showing
that the agent implicitly learns key navigation abilities.

利用增强学习问题和多模态感应输入的辅助深度预测和循环闭环分类任务，可以学习从复杂 3D 迷宫中进行导航并接近人类级别表现

学习在复杂环境中导航

Learning to Navigate in Complex Environments

In this paper, we present a probabilistic framework for goal-driven spoken
dialog systems. A new dynamic stochastic state (DS-state) is then defined to
characterize the goal set of a dialog state at different stages of the dialog
process. Furthermore, an entropy minimization dialog management(EMDM) strategy
is also proposed to combine with the DS-states to facilitate a robust and
efficient solution in reaching a user's goals. A Song-On-Demand task, with a
total of 38117 songs and 12 attributes corresponding to each song, is used to
test the performance of the proposed approach. In an ideal simulation, assuming
no errors, the EMDM strategy is the most efficient goal-seeking method among
all tested approaches, returning the correct song within 3.3 dialog turns on
average. Furthermore, in a practical scenario, with top five candidates to
handle the unavoidable automatic speech recognition (ASR) and natural language
understanding (NLU) errors, the results show that only 61.7\% of the dialog
goals can be successfully obtained in 6.23 dialog turns on average when random
questions are asked by the system, whereas if the proposed DS-states are
updated with the top 5 candidates from the SLU output using the proposed EMDM
strategy executed at every DS-state, then a 86.7\% dialog success rate can be
accomplished effectively within 5.17 dialog turns on average. We also
demonstrate that entropy-based DM strategies are more efficient than
non-entropy based DM. Moreover, using the goal set distributions in EMDM, the
results are better than those without them, such as in sate-of-the-art database
summary DM.

本文提出了一个概率框架，用于目标驱动的口语对话系统，并定义了新的动态随机状态（DS-state）来描述不同阶段对话状态的目标集。同时，提出一种熵最小化对话管理（EMDM）策略，结合 DS-states 以实现在达到用户目标方面的强健且高效的解决方案。测试表明，在 Si-On-Demand 任务中，提议的 DS-states 与 EMDM 策略在实现功能方面的表现明显优于其他方法。