In this paper we give instructions on how to write a minimalist grammar (MG).
In order to present the instructions as an algorithm, we use a variant of
context free grammars (CFG) as an input format. We can exclude overgeneration,
if the CFG has no recursion, i.e. no non-terminal can (indirectly) derive to a
right-hand side containing itself. The constructed MGs utilize licensors/-ees
as a special way of exception handling. A CFG format for a derivation
$A\_eats\_B\mapsto^* peter\_eats\_apples$, where $A$ and $B$ generate noun
phrases, normally leads to overgeneration, e.\,g., $i\_eats\_apples$. In order
to avoid overgeneration, a CFG would need many non-terminal symbols and rules,
that mainly produce the same word, just to handle exceptions. In our MGs
however, we can summarize CFG rules that produce the same word in one item and
handle exceptions by a proper distribution of licensees/-ors. The difficulty
with this technique is that in most generations the majority of licensees/-ors
is not needed, but still has to be triggered somehow. We solve this problem
with $\epsilon$-items called \emph{adapters}.

本研究论文介绍了如何编写一种简约语法（MG），通过使用一种上下文无关文法（CFG）的变体作为输入格式，以算法的形式给出编写指南。该方法通过利用许可者和被许可者来处理异常情况，避免 CFG 中的递归导致过度生成，并通过适当分配许可者和被许可者来处理例外情况。同时，还通过引入所谓的适配器解决大多数许可者和被许可者不需要触发的问题。

极简语法：无过度生成的构建

Minimalist Grammar: Construction without Overgeneration

Robot control using reinforcement learning has become popular, but its
learning process generally terminates halfway through an episode for safety and
time-saving reasons. This study addresses the problem of the most popular
exception handling that temporal-difference (TD) learning performs at such
termination. That is, by forcibly assuming zero value after termination,
unintentionally implicit underestimation or overestimation occurs, depending on
the reward design in the normal states. When the episode is terminated due to
task failure, the failure may be highly valued with the unintentional
overestimation, and the wrong policy may be acquired. Although this problem can
be avoided by paying attention to the reward design, it is essential in
practical use of TD learning to review the exception handling at termination.
This paper therefore proposes a method to intentionally underestimate the value
after termination to avoid learning failures due to the unintentional
overestimation. In addition, the degree of underestimation is adjusted
according to the degree of stationarity at termination, thereby preventing
excessive exploration due to the intentional underestimation. Simulations and
real robot experiments showed that the proposed method can stably obtain the
optimal policies for various tasks and reward designs.
this https URL

本文提出了一种在终止后故意低估值以避免由于无意中的高估引起的学习失败的方法，并根据终止时的稳定度调整低估程度，从而防止由于故意低估引起的过度探索。通过模拟和真实机器人实验表明，所提出的方法能够稳定地获取各种任务和奖励设计的最优策略。

时间差分学习中，针对错误设计的奖励的终止状态的故意低估价值函数

Intentionally-underestimated Value Function at Terminal State for  Temporal-difference Learning with Mis-designed Reward

We propose a novel approach to logic-based learning which generates
assumption-based argumentation (ABA) frameworks from positive and negative
examples, using a given background knowledge. These ABA frameworks can be
mapped onto logic programs with negation as failure that may be non-stratified.
Whereas existing argumentation-based methods learn exceptions to general rules
by interpreting the exceptions as rebuttal attacks, our approach interprets
them as undercutting attacks. Our learning technique is based on the use of
transformation rules, including some adapted from logic program transformation
rules (notably folding) as well as others, such as rote learning and assumption
introduction. We present a general strategy that applies the transformation
rules in a suitable order to learn stratified frameworks, and we also propose a
variant that handles the non-stratified case. We illustrate the benefits of our
approach with a number of examples, which show that, on one hand, we are able
to easily reconstruct other logic-based learning approaches and, on the other
hand, we can work out in a very simple and natural way problems that seem to be
hard for existing techniques.

本文提出了一种基于逻辑的学习方法，通过利用给定的背景知识从正负样例中生成基于假设的辩证框架，并将这些框架映射到可能是非分层的失败否定的逻辑程序。该方法通过采用转换规则来处理常规规则的例外，其中包括一些从逻辑程序转换规则改编的规则，例如折叠，以及类似于背诵学习和假设引入的其他规则。我们提出了一个应用这些转换规则学习分层框架的通用策略，并还提出了一种处理非分层情况的变体。通过多个示例，我们展示了此方法的优点。