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。