A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has possible states, and a traveling salesperson problem with as little as 82 cities already has more than different tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving.

中文翻译：

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心理学研究什么时候对人工智能有用？减少问题解决中计算复杂性的一个案例

问题是代理在不知道如何实现的情况下寻求实现给定目标的情况。人类问题解决通常被研究为在由状态（有关环境的信息）和运算符（在状态之间移动）组成的问题空间中的搜索。像下象棋这样的问题有可能的状态，而一个只有 82 个城市的旅行商问题已经有超过不同的旅行（类似于国际象棋）。生物神经元比计算机中的数字开关慢。对问题空间的穷举搜索超出了当前计算机对大多数有趣问题的能力，而且很明显，人类在有生之年无法穷举搜索这些问题空间的一小部分。然而，人类每天都会下棋并解决类似复杂性的后勤问题。即使对于简单的问题，人类通常也不会参与探索问题空间的一小部分。这就引出了一个问题：人类如何以快速有效的方式解决日常问题？最近的研究表明，人类构建了一个问题表示并解决了所表示的问题——而不是存在的问题。所构建的问题表示和用于解决它的过程受到认知能力的限制和成本效益分析的限制，其中不包括努力和回报。在本文中，我们认为更好地理解人类使用启发式表示和解决问题的方式有助于了解如何在人工智能中使用更简单的算法和表示，以降低计算复杂度、减少计算时间并促进复杂问题中的实时计算解决。