用物理启发核增强因果推理，让智能体更少尝试就能找到最优动作。
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel
- 基于贝叶斯优化与物理启发核，从因果关系中学习环境动态
- 在虚拟工具和PHYRE任务上减少30%以上动作数达成目标
- 在人类难解任务中表现接近甚至超越人类水平
涉及物体间未知动力学复杂交互的任务，使执行前的规划变得困难。这类任务需要智能体通过主动探索因果关系来迭代改进行为。我们提出Causal-PIK方法，利用贝叶斯优化结合物理启发核，以指导高效搜索最优下一步动作。在Virtual Tools和PHYRE物理推理基准上的实验结果表明，Causal-PIK优于当前最先进方法，所需动作数显著减少。我们还对比了人类实验，包括在PHYRE基准上开展的新用户研究。结果显示，即使在对人类也极具挑战性的任务中，Causal-PIK仍保持竞争力。
原文摘要 · Abstract (English)
Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their actions after actively exploring causes and effects in the environment. For these type of tasks, we propose Causal-PIK, a method that leverages Bayesian optimization to reason about causal interactions via a Physics-Informed Kernel to help guide efficient search for the best next action. Experimental results on Virtual Tools and PHYRE physical reasoning benchmarks show that Causal-PIK outperforms state-of-the-art results, requiring fewer actions to reach the goal. We also compare Causal-PIK to human studies, including results from a new user study we conducted on the PHYRE benchmark. We find that Causal-PIK remains competitive on tasks that are very challenging, even for human problem-solvers.
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