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 ...
Pruning techniques have recently been shown to speed up search algorithms by reducing the branching factor of large search spaces. One such technique is sleep sets, which were ori...
Due to the simplicity and efficiency, many hashing methods have recently been developed for large-scale similarity search. Most of the existing hashing methods focus on mapping l...
: Consecutive political crises in Belgium during the periods 2007-2008 and 2010-2011 demonstrated the importance of coalition formation and the necessity for a good understanding o...