New SCOPE framework enhances symbolic planning in open-ended environments

Researchers have introduced SCOPE, a novel framework designed to enhance symbolic planning in open-ended environments. SCOPE addresses the issue of incomplete symbolic representations, which often hinder long-horizon planning. The framework integrates a Symbolic Execution Simulator (SESim) for validating and refining action plans, and a Self-Adaptive Symbolic Memory (SASMem) for distilling feedback into evolving symbolic knowledge. Experiments demonstrate that SCOPE significantly improves the completeness of symbolic worlds, plan success rates under environmental perturbations, and adaptability across diverse embodied scenarios. AI

IMPACT Enhances AI planning capabilities in complex, unpredictable environments, potentially improving robot autonomy and task completion.

RANK_REASON The cluster contains a research paper detailing a new framework for AI planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Hugging Face
• SASMem
• SCOPE
• Yundaichuan Zhan

• paper
• other

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