Automated Intelligent Decision Making in Domains With Large Action Spaces

人工智能（AI）研究涉及开发新技术，以便在各种更复杂的现实世界问题中做出智能决策。这个自动化决策制定过程需要对给定的领域进行建模，然后从给定的场景中推断未来可能采取的行动，最终决定采取哪些行动，并考虑到某些目标。人工智能研究人员面临的主要挑战之一是，如何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选择采取的行动。据说这些领域有很大的行动空间，而且其中许多领域需要在几分之一秒内做出实时决策，这个问题很快就变得棘手了。

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.