This paper develops a novel methodology for using symbolic knowledge in deep learning.
From first principles, we derive a semantic loss function that bridges between neural output …

Abstract We propose the Probabilistic Sentential Decision Diagram (PSDD): A complete and
canonical representation of probability distributions defined over the models of a given …

The paper focuses on developing effective importance sampling algorithms for mixed
probabilistic and deterministic graphical models. The use of importance sampling in such …

Probabilistic sentential decision diagrams (PSDDs) are a tractable representation of
structured probability spaces, which are characterized by complex logical constraints on …

We present SGDPLL (T), an algorithm that solves (among many other problems)
probabilistic inference modulo theories, that is, inference problems over probabilistic models …

We present Saul, a new probabilistic programming language designed to address some of
the shortcomings of programming languages that aim at advancing and simplifying the …

This paper describes and discusses the K-COPMAN (Knowledge-enabled Cognitive
Perception for Manipulation) system, which enables autonomous robots to generate …

We show that a number of problems in Artificial Intelligence can be seen as Stochastic
Constraint Optimization Problems (SCOPs): problems that have both a stochastic and a …

Spatial evolutionary games are used to model large systems of interacting agents. In earlier
work, a method was developed using Bayesian Networks to approximate the population …

This article investigates fundamental issues in scaling autonomous personal robots towards
open-ended sets of everyday manipulation tasks which involve high complexity and vague …