Markov decisionprocesses(MDPs) haveproven to be popular models for decision-theoretic planning, but standard dynamic programming algorithms for solving MDPs rely on explicit, stat...
Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application anal...
We develop an exact dynamic programming algorithm for partially observable stochastic games (POSGs). The algorithm is a synthesis of dynamic programming for partially observable M...
Eric A. Hansen, Daniel S. Bernstein, Shlomo Zilber...
One of the main problems in probabilistic grammatical inference consists in inferring a stochastic language, i.e. a probability distribution, in some class of probabilistic models...
The stochastic Lotka-Volterra model is an infinite Markov population model that has applications in various life science domains. Its analysis is challenging since, besides an infi...