arXiv cs.AI· Jonas Reiter, Jakob Elias Gebler, Hector Geffner·· 10 小时前AI 评分26
面向经典规划的可微提升动作模式学习
Differentiable Learning of Lifted Action Schemas for Classical Planning
AI 导读
研究者提出一种新型神经网络架构，可在状态完全观测但动作参数未观测的轨迹中学习提升动作模式，同时从状态变化中识别动作参数。该可微组件可集成进更大的神经符号模型，在多个规划域上学习到的动作模式需恢复真实结构，并报告了观测噪声鲁棒性、组件消融及槽位动态模型变体实验。
正文 · 原文
Abstract:Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and relations, and lifted action schemas add or delete these atoms. This compact representation yields strong search heuristics and provides an ideal setting for structural generalization, since lifted relations and action schemas give rise to infinitely many domain instances. A central challenge is to learn these relations and action schemas from data, and recent approaches have addressed this problem using different types of observations. In this work, we develop a novel neural network architecture for learning action schemas from traces where states are fully observed but action arguments are unobserved. The problem is a simplification but an important step towards learning planning domains from sequences of images and action labels, and we aim to solve this simplification in a nearly perfect manner. The challenge lies in learning the action schemas while simultaneously identifying the action arguments from observed state changes. Our approach yields a robust differentiable component that can then be integrated into larger neuro-symbolic models. We evaluate the architecture on various planning domains, where the learned lifted action schemas must recover the ground-truth structure. Additionally, we report experiments on robustness to observation noise, on component ablations, and on a variation related to slot-based dynamics models.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.13282 [cs.AI] |
| (or arXiv:2605.13282v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13282 arXiv-issued DOI via DataCite |
Submission history
From: Jonas Reiter [view email]
[v1]
Wed, 13 May 2026 09:59:49 UTC (374 KB)
[v2]
Sun, 24 May 2026 13:53:18 UTC (374 KB)
[v3]
Mon, 9 Nov 2026 13:07:01 UTC (97 KB)
[v4]
Tue, 10 Nov 2026 08:57:59 UTC (89 KB)
来源：arXiv cs.AI · arxiv.org