To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision...
Abstract. We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to ...
— In this paper, we consider a motion planning problem for a class of constrained nonlinear systems. In each simplex of a triangulation of the set of states, the nonlinear dynami...
Path-oriented Random Testing (PRT) aims at generating a uniformly spread out sequence of random test data that activate a single control flow path within an imperative program. T...
This paper extends the link between evolutionary game theory and multi-agent reinforcement learning to multistate games. In previous work, we introduced piecewise replicator dynam...