Zuidberg Dos Martires Pedro, Kumar Nitesh, Persson Andreas, Loutfi Amy, De Raedt Luc
Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
Center for Applied Autonomous Sensor Systems (AASS), Department of Science and Technology, Örebro University, Örebro, Sweden.
Front Robot AI. 2020 Jul 31;7:100. doi: 10.3389/frobt.2020.00100. eCollection 2020.
Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.
机器人代理应该能够从亚符号传感器数据中学习，同时能够在符号层面上对物体进行推理并与人类进行交流。这就提出了一个问题，即如何克服符号人工智能和亚符号人工智能之间的差距。我们提出了一种基于自底向上的对象锚定的语义世界建模方法，该方法使用以对象为中心的世界表示。感知锚定处理连续的感知传感器数据，并保持与符号表示的对应关系。我们扩展了锚定的定义以处理多模态概率分布，并将由此产生的符号锚定系统与概率逻辑推理器耦合以进行推理。此外，我们使用统计关系学习使锚定框架能够从嘈杂的亚符号传感器输入中学习世界的一组概率逻辑规则形式的符号知识。由此产生的框架结合了感知锚定和统计关系学习，能够维护随着时间推移所感知到的所有对象的语义世界模型，同时仍然利用逻辑规则的表现力来推理那些没有通过感官输入数据直接观察到的对象的状态。为了验证我们的方法，一方面，我们展示了我们的系统对多模态概率分布进行概率推理的能力，另一方面，展示了从感知观察产生的锚定对象中学习概率逻辑规则的能力。随后，所学习的逻辑规则被用于评估我们提出的概率锚定过程。我们在一个涉及对象交互的场景中展示我们的系统，在这个场景中会出现对象遮挡并且需要概率推理来正确锚定对象。
