Automated Intelligent Decision Making in Domains With Large Action Spaces

大行动空间领域的自动化智能决策

• 批准号: RGPIN-2017-05041
• 负责人: Churchill, David
• 金额: $ 1.46万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635030
• 关键词: Automated; Intelligent; Decision; Making; Domains

Artificial Intelligence (AI) research involves the development of new techniques for making intelligent decisions in a wide variety of ever more complex real-world problems. This process of automated decision making requires modelling a given domain, and then reasoning about future actions which are possible from a given scenario, eventually deciding on which action to take with some goal in mind. One of the major challenges faced by AI researchers is how to choose an action to take in a domain where the number of possible future scenarios and actions is so large that there is no way to explore them all. These domains are said to have large action spaces – and with many of them requiring real-time decisions to be made in fractions of a second, the problem quickly becomes intractable.

人工智能（AI）研究涉及开发新技术，用于在各种各样的越来越复杂的现实世界问题中做出智能决策。这个自动决策的过程需要对给定的领域进行建模，然后推理给定场景中可能采取的未来行动，最终决定在心中有某个目标的情况下采取哪些行动。人工智能研究人员面临的主要挑战之一是如何选择在一个可能的未来场景和行动数量如此之大以至于无法全部探索的领域中采取的行动。据说这些领域有很大的行动空间，其中许多领域需要在几分之一秒内做出实时决策，问题很快就变得棘手。