CAREER: Compute Intensive Methods for Artificial Intelligence

这项研究和教育计划的目标是开发新的形式主义 和方法，人工智能相结合，健全的理论方法， 原则性的实验成分，提供一个实际的评价建议 模型，并将它们与现实世界的应用程序和挑战联系起来，并教育下一个 一代计算机科学学生关于雄心勃勃的和具有挑战性的目标， 人工智能 为了开发成功的应用程序，一般搜索和推理 近年来，通过明确纳入大量特定领域的 知识 例如，这种知识密集型方法在以下方面取得了成功： 专家系统;但在一般规划或推理等领域， 令人失望的是，获得具体的知识往往困难和昂贵。 然而，一般方法的最新进展与更快的硬件和更好的实现相结合，提供了强有力的证据，表明计算密集型方法不仅 适用于处理许多AI形式主义的组合性质，但也可能是 需要补充特定领域的知识。 本研究将快速通道 推理和搜索方法，重点是随机过程，这是一个 解决计算困难问题的最新发展。 它还调查了各种来源的复杂性在困难的问题，使用理论和 实验方法，探索计算机科学之间有趣的联系， 人工智能和统计物理学此外，它正在研究问题中的问题， 表示，包括编码、抽象、编译和 近似方法 其结果预计将是新的通用方法，人工智能，适用于各种各样的问题，如规划， 运筹学和计算生物学。 http://simon.cs.cornell.edu/home/selman/

The objectives of this research and educational program are to develop new formalisms and methods for artificial intelligence by combining a sound theoretical approach with a principled experimental component, to provide a practical evaluation of the proposed models and relate them to real-world applications and challenges, and to educate the next generation of computer science students about the ambitious and challenging goals of artificial intelligence. To develop successful applications, general search and reasoning has been avoided in recent years by explicitly incorporating large amounts of domain-specific knowledge. Such a knowledge-intensive approach has been successful in, for example, expert systems; but in areas such as general planning or reasoning, progress has been disappointing, and specific knowledge acquisition is often difficult and expensive. However, recent advances in general methods combined with faster hardware and better implementations provide strong evidence that a compute-intensive approach is not only suitable for dealing with the combinatorial nature of many AI formalisms, but may also be required to supplement domain-specific knowledge. This research studies fast general reasoning and search methods, with an emphasis on stochastic procedures, which are a promising recent development for solving computationally hard problems. It also investigates the various sources of complexity in hard problems, using both theoretical and experimental methods, exploring interesting connections between computer science, artificial intelligence, and statistical physics. In addition, it is studying issues in problem representation, including robustness of encodings, abstraction, compilation, and approximation methods. The result is expected to be new general approaches to artificial intelligence that are applicable to a wide variety of problems in such areas as planning, operations research, and computational biology. http://simon.cs.cornell.edu/home/selman/