We consider the fundamental problem of monitoring (i.e. tracking) the belief state in a dynamic system, when the model is only approximately correct and when the initial belief st...
A constraint satisfaction problem (CSP) is a general framework that can formalize various application problems in artificial intelligence. However, practical real-world problems t...
We introduce a new algorithm based on linear programming that approximates the differential value function of an average-cost Markov decision process via a linear combination of p...
We present an iterative Markov chain Monte Carlo algorithm for computing reference priors and minimax risk for general parametric families. Our approach uses MCMC techniques based...
We consider a fault-tolerant generalization of the classical uncapacitated facility location problem, where each client j has a requirement that rj distinct facilities serve it, i...