While Poker, as a family of games, has been studied extensively in the last
decades, collectible card games have seen relatively little attention. Only
recently have we seen an agent that can compete with professional human players
in Hearthstone, one of the most popular collectible card games. Although
artificial agents must be able to work with imperfect information in both of
these genres, collectible card games pose another set of distinct challenges.
Unlike in many poker variants, agents must deal with state space so vast that
even enumerating all states consistent with the agent's beliefs is intractable,
rendering the current search methods unusable and requiring the agents to opt
for other techniques. In this paper, we investigate the strength of such
techniques for this class of games. Namely, we present preliminary analysis
results of ByteRL, the state-of-the-art agent in Legends of Code and Magic and
Hearthstone. Although ByteRL beat a top-10 Hearthstone player from China, we
show that its play in Legends of Code and Magic is highly exploitable.

通过对 ByteRL 在《法典传说》和《炉石传说》中的表现进行初步分析，我们发现这种技术在这类游戏中的可利用性很高。

学习击败 ByteRL：可收集卡牌游戏智能体的可利用性

Learning to Beat ByteRL: Exploitability of Collectible Card Game Agents

Strong foundations in basic AI techniques are key to understanding more
advanced concepts. We believe that introducing AI techniques, such as search
methods, early in higher education helps create a deeper understanding of the
concepts seen later in more advanced AI and algorithms courses. We present a
project-based and competition-based bachelor course that gives second-year
students an introduction to search methods applied to board games. In groups of
two, students have to use network programming and AI methods to build an AI
agent to compete in a board game tournament-othello was this year's game.
Students are evaluated based on the quality of their projects and on their
performance during the final tournament. We believe that the introduction of
gamification, in the form of competition-based learning, allows for a better
learning experience for the students.

本研究介绍了一种基于项目和竞赛的本科生课程，旨在为第二年级学生提供搜索方法在棋盘游戏中的应用基础，并通过竞赛形式应用于构建 AI 代理以参加比赛。通过引入竞赛化学习和游戏化学习，以更好的方式促进学生学习的质量和体验。

利用人工智能棋类比赛刺激学生参与度

Stimulating student engagement with an AI board game tournament

While rigid origami has shown potential in a large diversity of engineering
applications, current rigid origami crease pattern designs mostly rely on known
tessellations. This leaves a potential gap in performance as the space of
rigidly foldable crease patterns is far larger than these tessellations would
suggest. In this work, we build upon the recently developed principle of three
units method to formulate rigid origami design as a discrete optimization
problem. Our implementation allows for a simple definition of diverse
objectives and thereby expands the potential of rigid origami further to
optimized, application-specific crease patterns. We benchmark a diverse set of
search methods in several shape approximation tasks to validate our model and
showcase the flexibility of our formulation through four illustrative case
studies. Results show that using our proposed problem formulation one can
successfully approximate a variety of target shapes. Moreover, by specifying
custom reward functions, we can find patterns, which result in novel, foldable
designs for everyday objects.

本文提出了一种基于三元件原理的离散优化框架，用于构建刚性折纸的特定应用折痕图案设计。实现的搜索方法可以成功地逼近各种目标形状，并生成新颖可折叠设计。

自动化刚性折纸设计

Automating Rigid Origami Design

We have designed three search methods for producing the task trees for the
provided goal nodes using the Functional Object-Oriented Network. This paper
details the strategy, the procedure, and the outcomes.

本文介绍了使用功能面向对象网络设计任务树的三种搜索方法，包括策略、程序和结果。

Foon Creation

This paper proposes a new algorithm for solving MAX2SAT problems based on
combining search methods with semidefinite programming approaches. Semidefinite
programming techniques are well-known as a theoretical tool for approximating
maximum satisfiability problems, but their application has traditionally been
very limited by their speed and randomized nature. Our approach overcomes this
difficult by using a recent approach to low-rank semidefinite programming,
specialized to work in an incremental fashion suitable for use in an exact
search algorithm. The method can be used both within complete or incomplete
solver, and we demonstrate on a variety of problems from recent competitions.
Our experiments show that the approach is faster (sometimes by orders of
magnitude) than existing state-of-the-art complete and incomplete solvers,
representing a substantial advance in search methods specialized for MAX2SAT
problems.

本文提出了一种将搜索方法与半定规划方法相结合的算法，其用于解决 MAX2SAT 问题。文中称，使用这种方法能够显著提高问题的解决速度，并在一些问题上取得了 order of magnitude 级别的速度提升。