Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of
utility functions U(u,A), where u is a vector of parameters or task
descriptors, maximize or minimize U with respect to u, using networks (Option
Nets) which input A and learn to generate good options u stochastically. This
paper discusses why this is crucial to brain-like intelligence (an area funded
by NSF) and to many applications, and discusses various possibilities for
network design and training.