ProbAnch: a Modular Probabilistic Anchoring Framework

Persson, Andreas; Martires, Pedro Zuidberg Dos; Raedt, Luc De; Loufti, Amy

doi:10.24963/ijcai.2020/771

Cited by 1 publication

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“…This would for example allow us to replace the GoogLeNet-based classifier by more recent and memory efficient architecture such as residual neural networks (He et al, 2016). The modularity of the anchoring framework is presented in a separate demo paper (Persson et al, 2020a). Figure 2-3. .…”

Section: Requirements For Anchoring and Semantic Object Trackingmentioning

confidence: 99%

Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring

et al. 2020

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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.

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Section: Requirements For Anchoring and Semantic Object Trackingmentioning

confidence: 99%