Brain-Like Stochastic Search: A Research Challenge and Funding
  Opportunity
  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. The appendix discusses recent research, relations
to work on stochastic optimization in operations research, and relations to
engineering-based approaches to understanding neocortex.
