As advances in artificial intelligence enable increasingly capable
learning-based autonomous agents, it becomes more challenging for human
observers to efficiently construct a mental model of the agent's behaviour. In
order to successfully deploy autonomous agents, humans should not only be able
to understand the individual limitations of the agents but also have insight on
how they compare against one another. To do so, we need effective methods for
generating human interpretable agent behaviour summaries. Single agent
behaviour summarization has been tackled in the past through methods that
generate explanations for why an agent chose to pick a particular action at a
single timestep. However, for complex tasks, a per-action explanation may not
be able to convey an agents global strategy. As a result, researchers have
looked towards multi-timestep summaries which can better help humans assess an
agents overall capability. More recently, multi-step summaries have also been
used for generating contrasting examples to evaluate multiple agents. However,
past approaches have largely relied on unstructured search methods to generate
summaries and require agents to have a discrete action space. In this paper we
present an adaptive search method for efficiently generating contrasting
behaviour summaries with support for continuous state and action spaces. We
perform a user study to evaluate the effectiveness of the summaries for helping
humans discern the superior autonomous agent for a given task. Our results
indicate that adaptive search can efficiently identify informative contrasting
scenarios that enable humans to accurately select the better performing agent
with a limited observation time budget.

本文提出了一种自适应搜索方法，用于有效生成对比行为总结，该方法支持连续状态和操作空间，并通过用户研究证明其可以帮助人们在有限的观察时间预算内准确选择表现更好的自主代理人。

通过对比行为总结展示自主机器人能力

Conveying Autonomous Robot Capabilities through Contrasting Behaviour  Summaries

This paper considers the problem of acquiring an unknown target location
(among a finite number of locations) via a sequence of measurements, where each
measurement consists of simultaneously probing a group of locations. The
resulting observation consists of a sum of an indicator of the target's
presence in the probed region, and a zero mean Gaussian noise term whose
variance is a function of the measurement vector. An equivalence between the
target acquisition problem and channel coding over a binary input additive
white Gaussian noise (BAWGN) channel with state and feedback is established.
Utilizing this information theoretic perspective, a two-stage adaptive target
search strategy based on the sorted Posterior Matching channel coding strategy
is proposed. Furthermore, using information theoretic converses, the
fundamental limits on the target acquisition rate for adaptive and non-adaptive
strategies are characterized. As a corollary to the non-asymptotic upper bound
of the expected number of measurements under the proposed two-stage strategy,
and to non-asymptotic lower bound of the expected number of measurements for
optimal non-adaptive search strategy, a lower bound on the adaptivity gain is
obtained. The adaptivity gain is further investigated in different asymptotic
regimes of interest.

本文研究了通过一系列测量获取未知目标位置（在有限个位置中）的问题，并建立了目标获取问题和二进制输入加性白噪声信道上的信道编码之间的等价性，提出了一种基于排序后的后验匹配信道编码策略的两阶段自适应目标搜索策略。通过信息论会话进一步研究了自适应和非自适应策略的基本限制，提出了适应性增益的下限。

使用反馈编码提高目标采集速率

Improved Target Acquisition Rates with Feedback Codes

Consider a target moving with a constant velocity on a unit-circumference
circle, starting from an arbitrary location. To acquire the target, any region
of the circle can be probed for its presence, but the associated measurement
noise increases with the size of the probed region. We are interested in the
expected time required to find the target to within some given resolution and
error probability. For a known velocity, we characterize the optimal tradeoff
between time and resolution (i.e., maximal rate), and show that in contrast to
the case of constant measurement noise, measurement dependent noise incurs a
multiplicative gap between adaptive search and non-adaptive search. Moreover,
our adaptive scheme attains the optimal rate-reliability tradeoff. We further
show that for optimal non-adaptive search, accounting for an unknown velocity
incurs a factor of two in rate.

通过在单位周长圆的任意位置对其进行侦测，本文考虑了一个以恒定速度运动的目标，但其关联的测量噪声会随着探测区域的扩大而增加，研究了在给定精度和误差概率的前提下，发现目标的预期时间所需。本研究通过采用一种自适应搜索方案实现了速率 - 可靠性权衡的最优解，而针对未知速度的情况下，采用最优非自适应搜索方案所创造的速率却会降低一半。