Learning-Based Planning:. Jiménez Celorrio, S. & De La Rosa Turbides, T. In Rabuñal Dopico, J. R., Dorado, J., & Pazos, A., editors, Encyclopedia of Artificial Intelligence, pages 1024–1028. IGI Global, 2009. 🏷️ /unread
Learning-Based Planning: [link]Paper  abstract   bibtex   
Automated Planning (AP) studies the generation of action sequences for problem solving. A problem in AP is defined by a state-transition function describing the dynamics of the world, the initial state of the world and the goals to be achieved. According to this definition, AP problems seem to be easily tackled by searching for a path in a graph, which is a well-studied problem. However, the graphs resulting from AP problems are so large that explicitly specifying them is not feasible. Thus, different approaches have been tried to address AP problems. Since the mid 90’s, new planning algorithms have enabled the solution of practical-size AP problems. Nevertheless, domain-independent planners still fail in solving complex AP problems, as solving planning tasks is a PSPACE-Complete problem (Bylander, 94). How do humans cope with this planning-inherent complexity? One answer is that our experience allows us to solve problems more quickly; we are endowed with learning skills that help us plan when problems are selected from a stable population. Inspire by this idea, the field of learning-based planning studies the development of AP systems able to modify their performance according to previous experiences. Since the first days, Artificial Intelligence (AI) has been concerned with the problem of Machine Learning (ML). As early as 1959, Arthur L. Samuel developed a prominent program that learned to improve its play in the game of checkers (Samuel, 1959). It is hardly surprising that ML has often been used to make changes in systems that perform tasks associated with AI, such as perception, robot control or AP. This article analyses the diverse ways ML can be used to improve AP processes. First, we review the major AP concepts and summarize the main research done in learning-based planning. Second, we describe current trends in applying ML to AP. Finally, we comment on the next avenues for combining AP and ML and conclude. 【摘要翻译】自动规划（Automated Planning，AP）研究为解决问题而生成行动序列。自动规划中的问题是由描述世界动态、世界初始状态和要实现的目标的状态转换函数定义的。根据这一定义，AP 问题似乎很容易通过搜索图中的路径来解决，而这是一个研究得很透彻的问题。然而，AP 问题产生的图非常大，明确指定这些图并不可行。因此，人们尝试了不同的方法来解决 AP 问题。自上世纪 90 年代中期以来，新的规划算法已经能够解决实际规模的 AP 问题。然而，独立于领域的规划算法在解决复杂的 AP 问题时仍然会失败，因为解决规划任务是一个 PSPACE-Complete 问题（Bylander，94 年）。人类如何应对这种规划固有的复杂性？一个答案是，我们的经验能让我们更快地解决问题；我们拥有学习技能，当问题从一个稳定的群体中挑选出来时，这些技能能帮助我们进行规划。受这一思想的启发，基于学习的规划领域研究了能够根据以往经验修正其性能的人工智能系统的发展。人工智能（AI）从诞生之初就一直关注机器学习（ML）问题。早在 1959 年，阿瑟-塞缪尔（Arthur L. Samuel）就开发了一个著名的程序，它能在跳棋游戏中学会改进自己的下法（塞缪尔，1959 年）。毫不奇怪，ML 经常被用于改变执行与人工智能相关任务的系统，如感知、机器人控制或 AP。本文分析了利用 ML 改进 AP 流程的各种方法。首先，我们回顾了主要的人工智能概念，并总结了基于学习的规划方面的主要研究。其次，我们介绍了将 ML 应用于 AP 的当前趋势。最后，我们评论了将 AP 与 ML 相结合的下一个途径，并得出结论。
@incollection{jimenezcelorrio2009,
	title = {Learning-{Based} {Planning}:},
	isbn = {978-1-59904-849-9 978-1-59904-850-5},
	shorttitle = {基于学习的规划：},
	url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-59904-849-9.ch151},
	abstract = {Automated Planning (AP) studies the generation of action sequences for problem solving. A problem in AP is defined by a state-transition function describing the dynamics of the world, the initial state of the world and the goals to be achieved. According to this definition, AP problems seem to be easily tackled by searching for a path in a graph, which is a well-studied problem. However, the graphs resulting from AP problems are so large that explicitly specifying them is not feasible. Thus, different approaches have been tried to address AP problems. Since the mid 90’s, new planning algorithms have enabled the solution of practical-size AP problems. Nevertheless, domain-independent planners still fail in solving complex AP problems, as solving planning tasks is a PSPACE-Complete problem (Bylander, 94). How do humans cope with this planning-inherent complexity? One answer is that our experience allows us to solve problems more quickly; we are endowed with learning skills that help us plan when problems are selected from a stable population. Inspire by this idea, the field of learning-based planning studies the development of AP systems able to modify their performance according to previous experiences. Since the first days, Artificial Intelligence (AI) has been concerned with the problem of Machine Learning (ML). As early as 1959, Arthur L. Samuel developed a prominent program that learned to improve its play in the game of checkers (Samuel, 1959). It is hardly surprising that ML has often been used to make changes in systems that perform tasks associated with AI, such as perception, robot control or AP. This article analyses the diverse ways ML can be used to improve AP processes. First, we review the major AP concepts and summarize the main research done in learning-based planning. Second, we describe current trends in applying ML to AP. Finally, we comment on the next avenues for combining AP and ML and conclude.

【摘要翻译】自动规划（Automated Planning，AP）研究为解决问题而生成行动序列。自动规划中的问题是由描述世界动态、世界初始状态和要实现的目标的状态转换函数定义的。根据这一定义，AP 问题似乎很容易通过搜索图中的路径来解决，而这是一个研究得很透彻的问题。然而，AP 问题产生的图非常大，明确指定这些图并不可行。因此，人们尝试了不同的方法来解决 AP 问题。自上世纪 90 年代中期以来，新的规划算法已经能够解决实际规模的 AP 问题。然而，独立于领域的规划算法在解决复杂的 AP 问题时仍然会失败，因为解决规划任务是一个 PSPACE-Complete 问题（Bylander，94 年）。人类如何应对这种规划固有的复杂性？一个答案是，我们的经验能让我们更快地解决问题；我们拥有学习技能，当问题从一个稳定的群体中挑选出来时，这些技能能帮助我们进行规划。受这一思想的启发，基于学习的规划领域研究了能够根据以往经验修正其性能的人工智能系统的发展。人工智能（AI）从诞生之初就一直关注机器学习（ML）问题。早在 1959 年，阿瑟-塞缪尔（Arthur L. Samuel）就开发了一个著名的程序，它能在跳棋游戏中学会改进自己的下法（塞缪尔，1959 年）。毫不奇怪，ML 经常被用于改变执行与人工智能相关任务的系统，如感知、机器人控制或 AP。本文分析了利用 ML 改进 AP 流程的各种方法。首先，我们回顾了主要的人工智能概念，并总结了基于学习的规划方面的主要研究。其次，我们介绍了将 ML 应用于 AP 的当前趋势。最后，我们评论了将 AP 与 ML 相结合的下一个途径，并得出结论。},
	language = {en},
	urldate = {2023-09-27},
	booktitle = {Encyclopedia of {Artificial} {Intelligence}},
	publisher = {IGI Global},
	author = {Jiménez Celorrio, Sergio and De La Rosa Turbides, Tomás},
	editor = {Rabuñal Dopico, Juan Ramón and Dorado, Julian and Pazos, Alejandro},
	year = {2009},
	note = {🏷️ /unread},
	keywords = {/unread},
	pages = {1024--1028},
}
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