Collaborative Research: Solving Chess with Probabilistic Planning and Control

合作研究：用概率规划和控制解决国际象棋问题

• 批准号: 9024557
• 负责人: Stuart Russell
• 金额: $ 0.95万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1992-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9024557&HistoricalAwards=false
• 关键词: Collaborative; Research; Solving; Chess; Probabilistic

This is collaborative research with Hans Berliner at Carnegie Mellon University (IRI-9105202). Game-playing is an important domain for two reasons: first, it exhibits in a clean form two of the major problems facing AI systems, namely how to pay attention to only a small fraction of the possibilities in complex domains, and how to use a large quantity of evidential information to make decisions; second, it has long been considered a litmus test for ideas about problem solving and for the quality of AI ideas in general, particularly in comparison to human intelligence. Recent work on control of search by Russell and Wefald and on probabilistic inference in problem solving domains by Hansson and Mayer, suggests that the PI's will be able to break the grip of brute-force methods on high-performance systems. The object of the proposed research is to test out these ideas, with a view to demonstrating feasibility of an intelligent chess program with the potential to perform at a high level. Should the effort succeed, the PI's will continue with a concerted attempt to defeat the human world champion. The purely scientific benefits should be significant, in terms of demonstrating the feasibility of large-scale probabilistic inference and metalevel control; the benefits to the field of AI in the larger national context will be immeasurable, since several influential critics will be disarmed, and success will avoid the extremely deleterious conclusion the "all your need for AI is faster chips".

这是我和卡内基的汉斯·柏林合作的研究 梅隆大学（IRI-9105202）。 玩游戏是一个重要的 域有两个原因：第一，它以一种干净的形式显示， 人工智能系统面临的主要问题，即如何支付 只注意到一小部分的可能性， 复杂的领域，以及如何使用大量的证据 第一，决策依据;第二，长期以来， 被认为是解决问题的试金石， AI创意的质量，特别是与 人类智慧 罗素关于搜索控制的最新研究 和Wefald以及问题解决中的概率推理 域由汉森和迈耶，表明PI的将能够 打破暴力方法对高性能 系统. 这项研究的目的是测试 这些想法，以证明可行性， 智能国际象棋程序，具有高性能的潜力， 水平 如果努力成功，PI将继续 齐心协力击败人类世界冠军。 纯 科学效益应该是显著的， 论证了大规模概率 推理和元级控制;对人工智能领域的好处 在更大的国家范围内将是不可估量的，因为 一些有影响力的批评家将被解除武装，成功将 避免极端有害的结论，即“所有你需要的， 人工智能是更快的芯片”。