: Numerical function approximation over a Boolean domain is a classical problem with wide application to data modeling tasks and various forms of learning. A great many function ap...
Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in whic...
One of the major challenges in the field of neurally driven evolved autonomous agents is deciphering the neural mechanisms underlying their behavior. Aiming at this goal, we have d...
Alon Keinan, Ben Sandbank, Claus C. Hilgetag, Isaa...
We consider the Maximum Integral Flow with Energy Constraints problem: given a directed graph G = (V, E) with edge-weights {w(e) : e E} and node battery capacities {b(v) : v V }...
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...