Symbolic Learning and Reasoning with Noisy Data for Probabilistic
  Anchoring
  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.
