Evaluating Next Generation Probabilistic Planners

这项研究项目将开发计划算法和一套评估概率规划者的通用方法。概率规划是一种顺序决策领域，涉及在可用操作符具有不确定结果时选择改变世界状态的操作符。这个项目的驱动目标是推动概率规划器的最新发展，以提高效率，改善对问题变化的稳健性，并扩大对现实世界问题的适用性。为了实现其目标，该项目将专注于两项相互关联的任务。首先，它将提出并开发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联系起来。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项目努力，该竞赛不久将在其现有结构中引入一个概率轨道。该项目将组织轨道，并将向社区提供一套软件程序，用于执行和评估概率领域的计划。其次，项目成员将致力于开发他们自己的规划算法，特别强调利用概率规划和强化学习之间的关系的方法。该项目在研究概率规划问题以及如何在实地衡量进展方面将产生一定的研究影响。它还将通过探索基于实例的技术来学习更有效的计划，从而促进计划和强化学习的最新水平。然而，大部分工作的重点将是它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将社区的努力集中在开发算法以解决具有重大科学和经济意义的问题上。

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.