RI-Small: Combinatorial Search Algorithms as Rational Agents

RI-Small：作为理性智能体的组合搜索算法

• 批准号: 0812141
• 负责人: Wheeler Ruml
• 金额: $ 44.82万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812141&HistoricalAwards=false
• 关键词: RI; Small; Combinatorial; Search; Algorithms

This project pursues three concurrent activities: 1) analysis of common search spaces and previously proposed search strategies to broadly characterize the relevant regularities that can be exploited during search, 2) development of search algorithms that can efficiently learn, update, and exploit models of those parameters on-line during problem-solving, and 3) comprehensive evaluation of the new algorithms across many common benchmarks in terms of actual CPU time, taking into account their increased overhead. Algorithms will be developed for the two most common types of search problems: 1) shortest-path problems, such as task planning or robot navigation, where the depth of the search tree is not usefully bounded, and 2) bounded-depth problems, such as constraint satisfaction or combinatorial optimization problems, where the number of decision variables is known.The rational search approach yields a form of hybrid metareasoning, in which the problem-solver reasons statistically about which combinatorial reasoning to do next. This combination promises significant gains in robustness and performance over the current paradigm in which search algorithms use the numerical information available to them only in simple ways, such as allowing it to directly dictate expansion order or using it only to prune. Rational search will provide a sound basis for moving beyond search strategies motivated by intuition to algorithms that adapt their behavior in unanticipated ways to suit precisely the problem at hand. By focusingattention on optimal use of information, this project will help the field of heuristic search address the question of problem formulation: what problem-specific heuristic information is most useful to guide search, and how can it best be conveyed to the search algorithm? It will also strengthen the nascent links between machine learning and heuristic search, particularly around the issues of exploration vs exploitation and the value of information. Because they form the engines of most AI systems, improvements in heuristic search algorithms yield social benefits wherever such systems are used. Increasing the robustness and generality of search methods also makes industrial adoption of AI technology easier and faster,widening its applicability.

This project pursues three concurrent activities: 1) analysis of common search spaces and previously proposed search strategies to broadly characterize the relevant regularities that can be exploited during search, 2) development of search algorithms that can efficiently learn, update, and exploit models of those parameters on-line during problem-solving, and 3) comprehensive evaluation of the new algorithms across many common benchmarks in terms of actual CPU time, taking into account their increased开销。 Algorithms will be developed for the two most common types of search problems: 1) shortest-path problems, such as task planning or robot navigation, where the depth of the search tree is not usefully bounded, and 2) bounded-depth problems, such as constraint satisfaction or combinatorial optimization problems, where the number of decision variables is known.The rational search approach yields a form of hybrid metareasoning, in which the problem-solver reasons statistically about which组合推理下一步。这种组合有望在当前范式上稳健性和性能的显着提高，在该范式中，搜索算法只能以简单的方式使用它们可用的数值信息，例如允许其直接决定扩展顺序或仅用于修剪。理性的搜索将为超越直觉促进的搜索策略提供一个合理的基础，这些算法会以意外的方式适应其行为，以精确地适应手头的问题。通过专注于最佳信息使用，该项目将有助于启发式搜索领域解决问题提出问题：哪种特定问题的启发式信息对于指导搜索最有用，以及如何最好地将其传达给搜索算法？它还将加强机器学习与启发式搜索之间的新生联系，尤其是围绕勘探与剥削问题和信息价值的问题。因为它们构成了大多数AI系统的引擎，所以无论使用此类系统，启发式搜索算法的改进都会产生社会利益。提高搜索方法的鲁棒性和一般性也使工业采用AI技术的速度更加容易，更快，从而扩大了其适用性。