论文标题
学会推理并在物理级联事件中行动
Learning to reason about and to act on physical cascading events
论文作者
论文摘要
推理和与动态环境的互动是AI的一个基本问题，但是当动作可以触发交叉依赖性事件的级联时，它变得极具挑战性。我们介绍了一种新的监督学习设置，称为{\ em Cascade}，其中显示了一个代理的视频，其中有一个物理模拟的动态场景，并被要求介入并触发一系列事件，以使系统达到“反事实”目标。例如，可以要求特工“通过推绿球来使蓝球击中红色”。代理干预是从连续空间中得出的，级联事件使动力学高度非线性。 我们将语义树搜索与事件驱动的前向模型相结合，并设计了一种算法，该算法学会了在连续空间中的语义树中进行搜索。我们证明，我们的方法学会有效地遵循指示，以干预以前看不见的复杂场景。当提供了一系列的级联事件时，这也可以理解替代结果。
Reasoning and interacting with dynamic environments is a fundamental problem in AI, but it becomes extremely challenging when actions can trigger cascades of cross-dependent events. We introduce a new supervised learning setup called {\em Cascade} where an agent is shown a video of a physically simulated dynamic scene, and is asked to intervene and trigger a cascade of events, such that the system reaches a "counterfactual" goal. For instance, the agent may be asked to "Make the blue ball hit the red one, by pushing the green ball". The agent intervention is drawn from a continuous space, and cascades of events makes the dynamics highly non-linear. We combine semantic tree search with an event-driven forward model and devise an algorithm that learns to search in semantic trees in continuous spaces. We demonstrate that our approach learns to effectively follow instructions to intervene in previously unseen complex scenes. It can also reason about alternative outcomes, when provided an observed cascade of events.
