We study temporally extended goals expressed in Pure-Past LTL (PPLTL). PPLTL
is particularly interesting for expressing goals since it allows to express
sophisticated tasks as in the Formal Methods literature, while the worst-case
computational complexity of Planning in both deterministic and nondeterministic
domains (FOND) remains the same as for classical reachability goals. However,
while the theory of planning for PPLTL goals is well understood, practical
tools have not been specifically investigated. In this paper, we make a
significant leap forward in the construction of actual tools to handle PPLTL
goals. We devise a technique to polynomially translate planning for PPLTL goals
into standard planning. We show the formal correctness of the translation, its
complexity, and its practical effectiveness through some comparative
experiments. As a result, our translation enables state-of-the-art tools, such
as FD or MyND, to handle PPLTL goals seamlessly, maintaining the impressive
performances they have for classical reachability goals.

研究纯过去时线性时态逻辑（PPLTL）中表达的时间上延长的目标，这对于表达目标特别有趣，因为它允许表达形式方法文献中的复杂任务，同时在确定性和非确定性领域的规划的最坏情况的计算复杂性仍然与经典可达性目标相同。本文提出了一种将 PPLTL 目标的计划多项式转化为标准计划的技术，并展示了翻译的形式正确性、复杂性和实际效果。

在纯过去时线性时态逻辑中规划具有时间延伸目标：多项式约减为标准规划

Planning for Temporally Extended Goals in Pure-Past Linear Temporal Logic: A Polynomial Reduction to Standard Planning

A robot working in a physical environment (like home or factory) needs to
learn to use various available tools for accomplishing different tasks, for
instance, a mop for cleaning and a tray for carrying objects. The number of
possible tools is large and it may not be feasible to demonstrate usage of each
individual tool during training. Can a robot learn commonsense knowledge and
adapt to novel settings where some known tools are missing, but alternative
unseen tools are present? We present a neural model that predicts the best tool
from the available objects for achieving a given declarative goal. This model
is trained by user demonstrations, which we crowd-source through humans
instructing a robot in a physics simulator. This dataset maintains user plans
involving multi-step object interactions along with symbolic state changes. Our
neural model, ToolNet, combines a graph neural network to encode the current
environment state, and goal-conditioned spatial attention to predict the
appropriate tool. We find that providing metric and semantic properties of
objects, and pre-trained object embeddings derived from a commonsense knowledge
repository such as ConceptNet, significantly improves the model's ability to
generalize to unseen tools. The model makes accurate and generalizable tool
predictions. When compared to a graph neural network baseline, it achieves
14-27% accuracy improvement for predicting known tools from new world scenes,
and 44-67% improvement in generalization for novel objects not encountered
during training.

提出一种基于神经网络的模型 ToolNet，可通过人类示范学习常识知识，使机器人具有了适应新场景使用未知工具的能力，并结合常识知识仓库来预测最佳工具，结果表明该模型的正确率和普适性较传统图神经网络有明显提高。

ToolNet: 利用常识泛化预测机器人计划合成中的工具使用

ToolNet: Using Commonsense Generalization for Predicting Tool Use for  Robot Plan Synthesis

Summary: Counting all k-mers in a given dataset is a standard procedure in
many bioinformatics applications. We introduce KMC3, a significant improvement
of the former KMC2 algorithm together with KMC tools for manipulating k-mer
databases. Usefulness of the tools is shown on a few real problems.
Availability: Program is freely available at
this http URL Contact: sebastian.deorowicz@polsl.pl

介绍了 KMC2 算法的显著改进 ——KMC3 以及用于操作 k-mer 数据库的 KMC 工具，这些工具的有用性在一些真实问题上得到了验证。

KMC 3: k-mer 统计及操作

KMC 3: counting and manipulating k-mer statistics

In this paper we introduce the methodology used and the basic phases we
followed to develop the Catalan WordNet, and shich lexical resources have been
employed in its building. This methodology, as well as the tools we made use
of, have been thought in a general way so that they could be applied to any
other language.

介绍了开发加泰罗尼亚词网所采用的方法，使用的词汇资源以及旨在应用于任何其他语言的通用工具。