This project aims to motivate research in competitive human-robot interaction
by creating a robot competitor that can challenge human users in certain
scenarios such as physical exercise and games. With this goal in mind, we
introduce the Fencing Game, a human-robot competition used to evaluate both the
capabilities of the robot competitor and user experience. We develop the robot
competitor through iterative multi-agent reinforcement learning and show that
it can perform well against human competitors. Our user study additionally
found that our system was able to continuously create challenging and enjoyable
interactions that significantly increased human subjects' heart rates. The
majority of human subjects considered the system to be entertaining and
desirable for improving the quality of their exercise.

本项目通过引入多智能体强化学习，创造了一款竞争人类的机器人选手 - Fencing Game，评估了机器人选手的能力和用户体验，用户研究发现它能够持续创造具有挑战性和愉悦性的交互，能有效增加人体心率，被绝大多数受试者认为是有趣且能提升锻炼质量的系统。

通过竞争性人机交互促进身体活动

Motivating Physical Activity via Competitive Human-Robot Interaction

We provide nearly optimal algorithms for online facility location (OFL) with
predictions. In OFL, $n$ demand points arrive in order and the algorithm must
irrevocably assign each demand point to an open facility upon its arrival. The
objective is to minimize the total connection costs from demand points to
assigned facilities plus the facility opening cost. We further assume the
algorithm is additionally given for each demand point $x_i$ a natural
prediction $f_{x_i}^{\mathrm{pred}}$ which is supposed to be the facility
$f_{x_i}^{\mathrm{opt}}$ that serves $x_i$ in the offline optimal solution.
Our main result is an $O(\min\{\log {\frac{n\eta_\infty}{\mathrm{OPT}}},
\log{n} \})$-competitive algorithm where $\eta_\infty$ is the maximum
prediction error (i.e., the distance between $f_{x_i}^{\mathrm{pred}}$ and
$f_{x_i}^{\mathrm{opt}}$). Our algorithm overcomes the fundamental
$\Omega(\frac{\log n}{\log \log n})$ lower bound of OFL (without predictions)
when $\eta_\infty$ is small, and it still maintains $O(\log n)$ ratio even when
$\eta_\infty$ is unbounded. Furthermore, our theoretical analysis is supported
by empirical evaluations for the tradeoffs between $\eta_\infty$ and the
competitive ratio on various real datasets of different types.

我们提供了几乎最优算法，对具有预测固定需求点的在线设施位置问题进行求解，理论分析和实证评估均表明该算法在不同的位置预测误差下均能够维持较好的竞争比率。

在线设施位置问题及其预测

Online Facility Location with Predictions

We present a novel approach to sentence simplification which departs from
previous work in two main ways. First, it requires neither hand written rules
nor a training corpus of aligned standard and simplified sentences. Second,
sentence splitting operates on deep semantic structure. We show (i) that the
unsupervised framework we propose is competitive with four state-of-the-art
supervised systems and (ii) that our semantic based approach allows for a
principled and effective handling of sentence splitting.

本研究提出了一种新颖的句子简化方法，使用基于语义的方法进行句子拆分，不需要手动编写规则或训练语料库，研究表明这个无监督框架与四个最先进的监督系统竞争力相当，并且我们提出的基于语义的方法能够有效地处理句子拆分。