CAREER: Compute Intensive Methods for Artificial Intelligence

这项研究和教育计划的目标是通过将合理的理论方法与有原则的实验部分相结合，为人工智能开发新的形式主义和方法，对建议的模型进行实用评估，并将它们与现实世界的应用和挑战联系起来，并教育下一代计算机科学学生关于人工智能雄心勃勃和具有挑战性的目标。为了开发成功的应用程序，近年来通过显式地结合大量特定于领域的知识来避免一般的搜索和推理。例如，这种知识密集型方法在专家系统方面取得了成功；但在一般规划或推理等领域，进展令人失望，具体知识的获取往往既困难又昂贵。然而，最近在一般方法方面的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人工智能形式的组合性质，而且可能还需要补充特定领域的知识。这项研究研究了快速的一般推理和搜索方法，重点是随机过程，这是解决计算困难问题的一个有前途的最近发展。它还调查了困难问题中复杂的各种来源，使用理论和实验方法，探索计算机科学、人工智能和统计物理之间有趣的联系。此外，它还研究了问题表征中的问题，包括编码、抽象、编译和近似方法的健壮性。预计其结果将是人工智能的新的通用方法，适用于规划、运筹学和计算生物学等领域的各种问题。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/