Automated game design (AGD), the study of automatically generating game
rules, has a long history in technical games research. AGD approaches generally
rely on approximations of human play, either objective functions or AI agents.
Despite this, the majority of these approximators are static, meaning they do
not reflect human player's ability to learn and improve in a game. In this
paper, we investigate the application of Reinforcement Learning (RL) as an
approximator for human play for rule generation. We recreate the classic AGD
environment Mechanic Maker in Unity as a new, open-source rule generation
framework. Our results demonstrate that RL produces distinct sets of rules from
an A* agent baseline, which may be more usable by humans.

研究利用强化学习作为近似函数来生成游戏规则，通过在 Unity 中重新创建了一个新的开源规则生成框架，实验结果表明强化学习可以生成与 A * 代理基线不同的规则，可能更适合人类使用。

机械化创造者 2.0：用于评估生成规则的强化学习

Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated  Rules

Creating and collecting labeled data is one of the major bottlenecks in
machine learning pipelines and the emergence of automated feature generation
techniques such as deep learning, which typically requires a lot of training
data, has further exacerbated the problem. While weak-supervision techniques
have circumvented this bottleneck, existing frameworks either require users to
write a set of diverse, high-quality rules to label data (e.g., Snorkel), or
require a labeled subset of the data to automatically mine rules (e.g., Snuba).
The process of manually writing rules can be tedious and time consuming. At the
same time, creating a labeled subset of the data can be costly and even
infeasible in imbalanced settings. This is due to the fact that a random sample
in imbalanced settings often contains only a few positive instances.
To address these shortcomings, we present Darwin, an interactive system
designed to alleviate the task of writing rules for labeling text data in
weakly-supervised settings. Given an initial labeling rule, Darwin
automatically generates a set of candidate rules for the labeling task at hand,
and utilizes the annotator's feedback to adapt the candidate rules. We describe
how Darwin is scalable and versatile. It can operate over large text corpora
(i.e., more than 1 million sentences) and supports a wide range of labeling
functions (i.e., any function that can be specified using a context free
grammar). Finally, we demonstrate with a suite of experiments over five
real-world datasets that Darwin enables annotators to generate
weakly-supervised labels efficiently and with a small cost. In fact, our
experiments show that rules discovered by Darwin on average identify 40% more
positive instances compared to Snuba even when it is provided with 1000 labeled
instances.

介绍了一种交互式系统 Darwin，可以协助 weakly-supervised learning 文本数据的标注规则生成。通过候选规则的自动生成和标注者的反馈机制，Darwin 可以高效地生成规则，实验结果表明 Darwin 能够比已有的方法更有效地识别出正例。

文本数据标记的自适应规则发现

Adaptive Rule Discovery for Labeling Text Data

We describe the Inspire system which participated in the first competition on
Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming
(ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis
space generation: given a set of facts representing the mode bias, and a set of
cost configuration parameters, each answer set of this encoding represents a
single rule that is considered for finding a hypothesis that entails the given
examples. Compared with state-of-the-art methods that use the length of the
rule body as a metric for rule complexity, our approach permits a much more
fine-grained specification of the shape of hypothesis candidate rules. The
Inspire system iteratively increases the rule cost limit and thereby increases
the search space until it finds a suitable hypothesis. The system searches for
a hypothesis that entails a single example at a time, utilizing an ASP encoding
derived from the encoding used in XHAIL. We perform experiments with the
development and test set of the ILP competition. For comparison we also adapted
the ILASP system to process competition instances. Experimental results show
that the cost parameters for the hypothesis search space are an important
factor for finding hypotheses to competition instances within tight resource
bounds.

本文描述了基于 Answer-Set Programming 的 Inspire 系统，着重介绍了其利用 ASP 编码进行假设空间生成和利用费用参数进行规则搜索的方法，通过与其他机器学习方法进行对比，结果表明假设搜索空间的成本参数对结果有重要影响。