CAREER: Decentralized Constraint-Based Optimization for Multi-Agent Planning and Coordination

职业：用于多智能体规划和协调的分散式基于约束的优化

• 批准号: 1550662
• 负责人: William Yeoh
• 金额: $ 50万
• 依托单位: New Mexico State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1550662&HistoricalAwards=false
• 关键词: CAREER; Decentralized; Constraint; Based; Optimization; CAREER; Decentralized; Constraint; Based; Optimization

There is a growing need for optimization methods to support decentralized decision-making in complex multi-agent systems including target tracking in sensor networks, mission planning of unmanned autonomous vehicles, coordination of rescue robots in disaster scenarios, and scheduling of intelligent devices in smart homes within smart grids. This class of problems is particularly challenging to solve due to a combination of the following requirements: There is a high degree of uncertainty that must be taken into account during planning; the planning process must be done in a decentralized fashion; and the resulting plan must be executed in a decentralized way as well. The objective of this project is to respond to the crucial challenge of developing an integrated approach that captures all these requirements within a single framework in order to improve the scope and applicability of multi-agent techniques in real-world applications. The long-term broader impacts of this project include the potential for the research findings to improve decentralized decision-making in real-world problems. In the short term, high-school students will benefit from the education modules developed by the PI, which will be disseminated through collaborations with local outreach programs as well as local teachers and summer camp organizers. The students will develop better computational thinking skills and be exposed to computational concepts applied to relevant applications of interest. The significance of these efforts is made more crucial by the fact that a majority of the student body at local high-schools as well as at NMSU is Hispanic. This project will make the necessary foundational contributions to the field of multi-agent systems to improve the scope and applicability of such systems, especially those that utilize automated planning and constraint optimization techniques, in the real world. More specifically, this project will result in (i) novel ways to more accurately model a large class of multi-agent planning problems using decentralized constraint-based models; (ii) new scalable algorithms with theoretical guarantees suitable for solving large-scale decentralized planning problems; and (iii) effective ways of improving computational thinking in high-school students via the use of constraint-based representations.

越来越多的需要优化方法来支持复杂的多机构系统中的分散决策，包括传感器网络中的目标跟踪，无人自动驾驶汽车的任务计划，在灾难场景中协调救援机器人的协调以及在智能网格中安排智能设备的智能设备。由于以下要求的组合，这类问题尤其具有挑战性：在计划期间必须考虑高度的不确定性；计划过程必须以分散的方式完成；而且结果计划也必须以分散的方式执行。该项目的目的是应对开发综合方法的关键挑战，该方法在单个框架内捕获所有这些要求，以提高在现实世界应用程序中多机构技术的范围和适用性。该项目的长期更广泛的影响包括研究结果的潜力，可以改善现实世界中的分散决策。在短期内，高中生将受益于PI开发的教育模块，该模块将通过与当地外展计划以及当地教师和夏令营组织者的合作进行传播。学生将发展更好的计算思维能力，并接触到应用于关注的相关应用的计算概念。这些努力的意义变得更加至关重要，因为当地高中和NMSU的大多数学生团体都是西班牙裔。该项目将为多机构系统的领域做出必要的基础贡献，以提高此类系统的范围和适用性，尤其是那些在现实世界中利用自动化计划和约束优化技术的系统的范围和适用性。更具体地说，该项目将导致（i）使用基于分散的约束模型更准确地对大量的多代理计划问题进行更准确地建模的新颖方法； （ii）具有理论保证的新型可扩展算法适合解决大规模分散计划问题； （iii）通过使用基于约束的表示，改善高中生计算思维的有效方法。