Understanding and Improving On-Line Planning Methods

这是一个为期三年的连续奖励的第一年资助。 在人工智能中使用各种在线规划方法，包括例如实时搜索方法（诸如LRTA*）、重复学习方法（诸如Q学习）和机器人导航方法（诸如D*）。 PI旨在大大提高这些和其他在线规划方法的性能，例如，未来的机器人导航方法将能够比现在更快地绘制未知地形，但具有与现有在线规划方法相同的有利特性。 许多在线规划方法，无论是总是或大多数时间，执行行动，移动代理的感知方向的目标，也就是说，移动代理，使其减少估计的目标距离最。 然而，PI的初步理论结果表明，执行行动，移动代理的感知方向的目标通常不是一个好主意。 例如，在最坏情况下，D* 不能以最小行进距离到达未知地形中的目标位置。 提高这些在线规划方法的性能的关键是以与规划或学习目标更直接相关的方式利用它们保持（或可以保持）的距离估计。 研究员将从理论和实验两方面研究在线规划方法的特性，并将开发改进的在线规划方法，这些方法与现有方法具有相同的界面，使这些方法的用户能够轻松地用新方法代替他们目前使用的方法。 拟议研究的副作用包括开发一个测试平台，用于在未知地形中对机器人导航方法进行实验评估，并为理解未知地形中的机器人导航方法（包括D*）奠定坚实的理论基础。

This is the first year funding of a three year continuing award. A variety of on-line planning methods are used in artificial intelligence including, for example, real-time search methods such as LRTA*, reinforcement-learning methods such as Q-learning, and robot-navigation methods such as D*. The PIs intend to improve the performance of these and other on-line planning methods substantially so that, for example, future robot-navigation methods will be able to map unknown terrain significantly faster than is now possible, yet have the same advantageous properties as existing on-line planning methods. Many on-line planning methods, either always or most of the time, execute actions that move the agent in the perceived direction of the goal, that is, move the agent so that it reduces the estimates of the goal distances the most. However, the PIs preliminary theoretical results show that executing actions that move the agent in the perceived direction of the goal is usually not a good idea. For example, D* does not reach a goal location in unknown terrain with a minimal travel distance in the worst case. The key to improving the performance of these on-line planning methods then is to exploit the distance estimates that they maintain (or can maintain) in a way that is more directly related to the planning or learning objective. The PIs will study the properties of on-line planning methods both theoretically and experimentally, and will develop improved on-line planning methods that have the same interface as the existing methods, which allows users of these methods to easily substitute the new methods for the ones they are currently using. Side benefits of the proposed research include developing a test-bed for the experimental evaluation of robot navigation methods in unknown terrain, and creating a solid theoretical foundation for understanding robot-navigation methods in unknown terrain, including D*.