Situated Structure Learning of a Bayesian Logic Network for Commonsense
  Reasoning
  This paper details the implementation of an algorithm for automatically
generating a high-level knowledge network to perform commonsense reasoning,
specifically with the application of robotic task repair. The network is
represented using a Bayesian Logic Network (BLN) (Jain, Waldherr, and Beetz
2009), which combines a set of directed relations between abstract concepts,
including IsA, AtLocation, HasProperty, and UsedFor, with a corresponding
probability distribution that models the uncertainty inherent in these
relations. Inference over this network enables reasoning over the abstract
concepts in order to perform appropriate object substitution or to locate
missing objects in the robot's environment. The structure of the network is
generated by combining information from two existing knowledge sources:
ConceptNet (Speer and Havasi 2012), and WordNet (Miller 1995). This is done in
a "situated" manner by only including information relevant a given context.
Results show that the generated network is able to accurately predict object
categories, locations, properties, and affordances in three different household
scenarios.
