The growing utilization of planning tools in practical scenarios has sparked
an interest in generating multiple high-quality plans. Consequently, a range of
computational problems under the general umbrella of top-quality planning were
introduced over a short time period, each with its own definition. In this
work, we show that the existing definitions can be unified into one, based on a
dominance relation. The different computational problems, therefore, simply
correspond to different dominance relations. Given the unified definition, we
can now certify the top-quality of the solutions, leveraging existing
certification of unsolvability and optimality. We show that task
transformations found in the existing literature can be employed for the
efficient certification of various top-quality planning problems and propose a
novel transformation to efficiently certify loopless top-quality planning.

这篇论文将现有的定义统一为一种基于支配关系的定义，证明了不同的计算问题实际上对应于不同的支配关系，提出了一种有效证明各种高质量规划问题的方法，并提出了一种新的转化方法以有效证明无循环的高质量规划。

统一和认证高质量规划

Unifying and Certifying Top-Quality Planning

Recent years have seen an increasing number of applications that have a
natural language interface, either in the form of chatbots or via personal
assistants such as Alexa (Amazon), Google Assistant, Siri (Apple), and Cortana
(Microsoft). To use these applications, a basic dialog between the robot and
the human is required.
While this kind of dialog exists today mainly within "static" robots that do
not make any movement in the household space, the challenge of reasoning about
the information conveyed by the environment increases significantly when
dealing with robots that can move and manipulate objects in our home
environment.
In this paper, we focus on cognitive robots, which have some knowledge-based
models of the world and operate by reasoning and planning with this model.
Thus, when the robot and the human communicate, there is already some formalism
they can use - the robot's knowledge representation formalism.
Our goal in this research is to translate natural language utterances into
this robot's formalism, allowing much more complicated household tasks to be
completed. We do so by combining off-the-shelf SOTA language models, planning
tools, and the robot's knowledge-base for better communication. In addition, we
analyze different directive types and illustrate the contribution of the
world's context to the translation process.

本文旨在研究自然语言交互与认知机器人、知识表达形式、计划工具和翻译过程的结合应用，以提高其在日常生活中的任务完成效率。