Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
he more abstract term "relational morphology" in place of tile usual "two-level morphology" in order to emphasize an aspect of Koskenniemi's work which ha...
Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given...
We propose a theory for reasoning about actions based on order-sorted predicate logic where one can consider an elaborate taxonomy of objects. We are interested in the projection ...
The question of whether there is a logic that captures polynomial time is the central open problem in descriptive complexity theory. In my talk, I will review the question and the...