摘要翻译：
类脑随机搜索（BLiSS）指的是这样的任务：给定一族效用函数U(U，a)，其中U是参数或任务描述符的向量，使用网络（选项网）将U相对于U最大化或最小化，网络输入a并学习随机生成好的选项U。本文讨论了为什么这对于类脑智能（一个由NSF资助的领域）和许多应用是至关重要的，并讨论了网络设计和训练的各种可能性。附录讨论了最近的研究，运筹学中的随机优化工作的关系，以及理解新皮层的基于工程的方法的关系。
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英文标题：
《Brain-Like Stochastic Search: A Research Challenge and Funding
  Opportunity》
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作者：
Paul J. Werbos
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/1006.0385