Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov l...
Pedro Domingos, Stanley Kok, Hoifung Poon, Matthew...
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...
Abstract. This paper presents a novel technique for counterexample generation in probabilistic model checking of Markov chains and Markov Decision Processes. (Finite) paths in coun...
A standard method for approximating averages in probabilistic models is to construct a Markov chain in the product space of the random variables with the desired equilibrium distr...
We consider a two-player zero-sum game given by a Markov chain over a finite set of states K and a family of zero-sum matrix games (Gk)kK. The sequence of states follows the Marko...