We describe a point-based approximate value iteration algorithm for partially observable Markov decision processes. The algorithm performs value function updates ensuring that in each iteration the new value function is an upper bound to the previous value function, as estimated on a sampled set of belief points. A randomized belief-point selection scheme allows for fast update steps. Results i...

We have recently shown that the probability density S(s) of the position-momentum dot product s=r 3p of a particle can be computed efficiently from its wave function ψ(r). Here, by examining the H atom and LiH molecule, we show that S(s) yields insight into the nature of electronic trajectories, and we argue that electron posmometry provides information that is inaccessible by diffraction or mo...