Totally and Partially Ordered Hierarchical Planners in PDDL4J Library

从多代人类中学习分层规划器

Learning a Hierarchical Planner from Humans in Multiple Generations

提出自然编程系统，结合程序化学习与分层规划，通过用户课程构建和语言提示引导规划，使机器能稳健组合多用户程序并快速适应新上下文解决复杂任务。

CoCoDA：用于工具增强代理的共进化组合DAG

CoCoDA: Co-evolving Compositional DAG for Tool-Augmented Agents

CoCoDA通过共进化规划器和工具库，利用组合代码DAG结构提升工具库检索效率和规划效果，实现在数学推理和代码任务中的性能提升。

Model-Lite Case-Based Planning

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain mo…

HATP: An HTN Planner for Robotics

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 the…

基于LLM生成启发式的层次任务网络规划

Hierarchical Task Network Planning with LLM-Generated Heuristics

研究利用大语言模型为层次任务网络规划生成搜索启发式，通过Pytrich规划器在六个基准领域评估，结果表明LLM生成的启发式在覆盖度上接近最优HTN规划器，并在83%的共享问题上显著减少搜索开销。