We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Finite-state and memoryless controllers are simple action selection mechanisms widely used in domains such as videogames and mobile robotics. Memoryless controllers stand for func...
In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic...
Assume a network (V, E) where a subset of the nodes in V are active. We consider the problem of selecting a set of k active nodes that best explain the observed activation state, ...