— This paper proposes a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, whe...
We investigate the decidability of observational equivalence and approximation in “Syntactic Control of Interference” (SCI). By asg denotations of terms in an inequationally fu...
— This work is concerned with the problem of characterizing and computing probabilistic bisimulations of diffusion processes. A probabilistic bisimulation relation between two su...
We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...