Evaluating Next Generation Probabilistic Planners

本研究计画将发展规划演算法及一套评估机率规划者之一般方法。 概率规划是顺序决策的领域，涉及在可用运营商具有不确定结果时选择改变世界状态的运营商。 该项目的驱动目标是推进概率规划器的最新发展，以提高效率，改善对问题变化的鲁棒性，并扩大对现实世界问题的适用性。 为实现其目标，该项目将侧重于两项相互关联的任务。 首先，它将提出并发展一种评估概率规划者的方法。 这将需要研究一套替代方案并进行实验，以将评估指标与日益现实的领域中的理想结果相关联。 该项目的工作将通过一年两次的国际规划竞赛（IPC）与更大的研究界密切协调，该竞赛将很快在其现有结构中引入概率轨道。 该项目将组织跟踪，并将提供一套软件程序的社区执行和评估计划的概率域。 第二，项目成员将致力于开发自己的规划算法，特别强调利用概率规划和强化学习之间关系的方法。该项目将在研究概率规划问题以及如何衡量该领域的进展方面产生一定的研究影响。 它还将通过探索基于实例的技术来提高规划和强化学习的最新水平，以更有效地学习规划。 然而，大部分工作的重点将是其对整个规划界的更广泛的影响，具体的领域描述语言，评估软件和基准问题，将有助于集中社区的努力，开发算法，以解决重大的科学和经济利益的问题。

This research project will develop planning algorithms and a set of general methods for evaluating probabilistic planners. Probabilistic planning is the area of sequential decision making concerned with choosing operators that change the state of the world when the available operators have uncertain outcomes. The driving goal of this project is to advance the state of the art of probabilistic planners toward increased efficiency, improved robustness to problem variations, and broadened applicability to real-world problems. To accomplish its goal, the project will focus on two interrelated tasks. First, it will propose and develop a methodology for evaluating probabilistic planners. This will require studying a set of alternatives and running experiments to correlate evaluation metrics with desirable outcomes in increasingly realistic domains. The project efforts will be coordinated closely with the larger research community through the biannual International Planning Competition (IPC), which will soon introduce a probabilistic track to its existing structure. This project will organize the track and will provide the community with a set of software programs for executing and evaluating plans in probabilistic domains. Second, the project members will pursue the development of their own planning algorithms, with a particular emphasis on approaches that exploit the relationship between probabilistic planning and reinforcement learning.The project will have definite research impacts in its study of the problem of probabilistic planning and how progress should be measured in the field. It will also advance the state of the art in planning and reinforcement learning through the exploration of instance-based techniques for learning to plan more effectively. However, the focus of the majority of the work will be on its broader impacts on the planning community as a whole, with concrete domain description languages, evaluation software, and benchmark problems that will serve to focus the community's efforts toward developing algorithms to solve problems of significant scientific and economic interest.