In sequential decision-making problems formulated as Markov decision processes, state-value function approximation using domain features is a critical technique for scaling up the...
We present a probabilistic method for path planning that considers trajectories constrained by both the environment and an ensemble of restrictions or preferences on preferred mot...
The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...
Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal langu...
Noah Goodman, Vikash K. Mansinghka, Daniel M. Roy,...