Hierarchical Task Network (HTN) planning usually requires a domain engineer
to provide manual input about how to decompose a planning problem. Even
HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer
to annotate the tasks with information about what to learn. We introduce
CURRICULAMA, an HTN method learning algorithm that completely automates the
learning process. It uses landmark analysis to compose annotated tasks and
leverages curriculum learning to order the learning of methods from simpler to
more complex. This eliminates the need for manual input, resolving a core issue
with HTN-MAKER. We prove CURRICULAMA's soundness, and show experimentally that
it has a substantially similar convergence rate in learning a complete set of
methods to HTN-MAKER.

CURRICULAMA 是一种完全自动化学习过程的 HTN 方法学习算法，利用地标分析组合注释任务，并利用课程学习从简单到复杂地排序方法的学习，消除了 HTN-MAKER 中需要手动输入的核心问题，并证明了 CURRICULAMA 的合理性，实验证明其在学习完整一套方法上具有与 HTN-MAKER 相当的收敛速度。

从地标自动学习 HTN 方法

Automatically Learning HTN Methods from Landmarks

The ability of an agent to change its objectives in response to unexpected
events is desirable in dynamic environments. In order to provide this
capability to hierarchical task network (HTN) planning, we propose an extension
of the paradigm called task modifiers, which are functions that receive a task
list and a state and produce a new task list. We focus on a particular type of
problems in which planning and execution are interleaved and the ability to
handle exogenous events is crucial. To determine the efficacy of this approach,
we evaluate the performance of our task modifier implementation in two
environments, one of which is a simulation that differs substantially from
traditional HTN domains.

该研究提出了一种基于任务修改器的层级任务网络规划扩展，以实现在动态环境中对代理进行意外事件响应的能力。通过在两个环境中评估性能，证明了该方法的有效性。

HTN 规划和行动的任务修改器

Task Modifiers for HTN Planning and Acting

Hierarchical Task Network (HTN) planning is a popular approach that cuts down
on the classical planning search space by relying on a given hierarchical
library of domain control knowledge. This provides an intuitive methodology for
specifying high-level instructions on how robots and agents should perform
tasks, while also giving the planner enough flexibility to choose the
lower-level steps and their ordering. In this paper we present the HATP
(Hierarchical Agent-based Task Planner) planning framework which extends the
traditional HTN planning domain representation and semantics by making them
more suitable for roboticists, and treating agents as "first class" entities in
the language. The former is achieved by allowing "social rules" to be defined
which specify what behaviour is acceptable/unacceptable by the agents/robots in
the domain, and interleaving planning with geometric reasoning in order to
validate online -with respect to a detailed geometric 3D world- the human/robot
actions currently being pursued by HATP.

本文介绍了一种扩展传统 HTN 计划架构的 HATP 计划框架，该框架将社交规则和几何推理结合起来，将代理人视为一等实体，使其更适合机器人学，并在线验证当前由 HATP 执行的人类 / 机器人行为。

HATP：用于机器人的 HTN 规划器

HATP: An HTN Planner for Robotics

Hierarchies are the most common structure used to understand the world
better. In galaxies, for instance, multiple-star systems are organised in a
hierarchical system. Then, governmental and company organisations are
structured using a hierarchy, while the Internet, which is used on a daily
basis, has a space of domain names arranged hierarchically. Since Artificial
Intelligence (AI) planning portrays information about the world and reasons to
solve some of world's problems, Hierarchical Task Network (HTN) planning has
been introduced almost 40 years ago to represent and deal with hierarchies. Its
requirement for rich domain knowledge to characterise the world enables HTN
planning to be very useful, but also to perform well. However, the history of
almost 40 years obfuscates the current understanding of HTN planning in terms
of accomplishments, planning models, similarities and differences among
hierarchical planners, and its current and objective image. On top of these
issues, attention attracts the ability of hierarchical planning to truly cope
with the requirements of applications from the real world. We propose a
framework-based approach to remedy this situation. First, we provide a basis
for defining different formal models of hierarchical planning, and define two
models that comprise a large portion of HTN planners. Second, we provide a set
of concepts that helps to interpret HTN planners from the aspect of their
search space. Then, we analyse and compare the planners based on a variety of
properties organised in five segments, namely domain authoring, expressiveness,
competence, performance and applicability. Furthermore, we select Web service
composition as a real-world and current application, and classify and compare
the approaches that employ HTN planning to solve the problem of service
composition. Finally, we conclude with our findings and present directions for
future work.

提出了一个基于框架的方法以比较和分析使用 Hierarchical Task Network（HTN）规划解决 Web 服务复合问题的途径，并探讨了 HTN 规划的成就，规划模型及其在真实应用中的适用性。