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New planning method achieves state-of-the-art performance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for generalized planning that significantly improves the efficiency and performance of learning policies in classical planning domains. This approach enhances the Iterated Width (IW) policy by introducing a holistic encoding of the search tree, allowing Relational Graph Neural Networks (R-GNNs) to score all transitions in a single pass. Additionally, Abstracted IW(1) is proposed to improve scalability through relational abstraction during novelty checks. Evaluations on the IPC 2023 benchmark demonstrate state-of-the-art performance, outperforming previous methods and the LAMA planner. AI

IMPACT Establishes new state-of-the-art in classical planning, potentially accelerating research in generalized AI planning capabilities.

RANK_REASON The cluster contains an academic paper detailing a new method for classical planning with benchmark results.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New planning method achieves state-of-the-art performance

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Hector Geffner ·

    Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning

    Generalized planning aims to learn policies that generalize across collections of instances within a classical planning domain. Recent Graph Neural Network (GNN) approaches have learned nearly perfect policies for several domains. This work improves on the recently published idea…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning

    Generalized planning aims to learn policies that generalize across collections of instances within a classical planning domain. Recent Graph Neural Network (GNN) approaches have learned nearly perfect policies for several domains. This work improves on the recently published idea…

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