Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability
Path planning under partial observability remains challenging because an agent must make long-horizon navigation decisions from only locally bounded observations. Nevertheless, historical trajectories contain reusable experience-guided directional preferences. Classical planners, however, typically solve each instance from scratch and lack an explicit mechanism to exploit such transferable decision knowledge, often leading to redundant node expansions and locally myopic search behaviors. Motivat
Record details
Published: 24 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 27 July 2026
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ethics.ai (24 July 2026), “Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability,” evidence record 13706, https://ethics.ai/record/13706 (originally published by arXiv cs.AI).
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