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,), where is a vector of parameters or task descriptors, maximize or minimize U with respect to u, using networks (Option Nets) which input 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. Of course, there are many forms of evolutionary computing. There are also classical methods, like Gibbs search and the sophisticated trust region approaches recently developed by Barhen et al and used on the Desert Storm tank routing problem. There are a few neural net designs (like Kohonen nets, but not Hopfield nets) which have had competitive performance on some specialized large-scale optimization problems.