We present a probabilistic framework for learning with heterogeneous multiview data where some views are given as ordinal, binary, or real-valued feature matrices, and some views ...
In this paper we describe asprin1 , a general, flexible, and extensible framework for handling preferences among the stable models of a logic program. We show how complex prefere...
Gerhard Brewka, James P. Delgrande, Javier Romero ...
Spectral clustering, a graph partitioning technique, has gained immense popularity in machine learning in the context of unsupervised learning. This is due to convincing empirical...
Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Policy gradient algorithms, which directl...
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, pl...
Christian J. Muise, Vaishak Belle, Paolo Felli, Sh...
This paper describes an end-to-end learning framework that allows a novice to create a model from data easily by helping structure the model building process and capturing extende...
In a recent position paper in Artificial Intelligence, we argued that the automated planning research literature has underestimated the importance and difficulty of deliberative...
Markov Logic is a powerful representation that unifies first-order logic and probabilistic graphical models. However, scaling-up inference in Markov Logic Networks (MLNs) is extr...
Planning in large partially observable Markov decision processes (POMDPs) is challenging especially when a long planning horizon is required. A few recent algorithms successfully ...