BSF: 2014012: Robust Solutions for Distributed Constraint Optimization Problems

BSF：2014012：分布式约束优化问题的鲁棒解决方案

基本信息

批准号：
1540168
负责人：
William Yeoh
金额：
$ 5万
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：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2017-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1540168&HistoricalAwards=false
关键词：
BSF 2014012 Robust Solutions Distributed

项目摘要

Distributed constraint optimization problems (DCOPs) have been shown to be useful in modeling various distributed combinatorial optimization problems, including meeting scheduling, sensor network, and power management problems. However, many of these problems are not only distributed in nature but dynamic as well. For example, a disaster rescue scenario can include dynamic events like the collapse of buildings, detection of new survivors, and spread of fires. Previous attempts to cope with dynamism in DCOPs have focused on reactively finding a new solution when an event occurs.In this project, the PI will take a proactive approach by taking possible future events into consideration when searching for solutions. This research will result in (1) a newly designed Robust DCOP (R_DCOP) model that will include a probabilistic scheme representing the likelihood of dynamic events; and (2) R_DCOP algorithms that will address the stochastic elements of the problem. The broader impacts of this research project are two fold: (1) Through this research, the PI will build the foundations for a general robust DCOP model that can be applied in dynamic environments and spur deployment of DCOP algorithms in the real world; and (2) This project will support broadening participation of underrepresented students at NMSU, a minority- and Hispanic-serving institution.

分布式约束优化问题（DCOP）已被证明可用于建模各种分布式组合优化问题，包括满足计划，传感器网络和电源管理问题。但是，其中许多问题不仅分布在自然界，而且是动态的。例如，灾难救援场景可以包括动态事件，例如建筑物的崩溃，对新幸存者的发现和大火传播。以前的DCOP中应付动力学的尝试已重点是在事件发生时反应地找到新的解决方案。在该项目中，PI将通过在搜索解决方案时考虑可能的未来事件来采取主动的方法。这项研究将导致（1）新设计的健壮DCOP（R_DCOP）模型，该模型将包括代表动态事件可能性的概率方案；（2）将解决问题的随机元素的R_DCOP算法。该研究项目的更广泛的影响是两个方面：（1）通过这项研究，PI将为一般强大的DCOP模型建立基础，该模型可以在动态环境中应用，并刺激了现实世界中DCOP算法的部署；（2）该项目将支持扩大代表性不足的学生在NMSU的少数派和西班牙裔服务机构的参与。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Goal Recognition Design with Stochastic Agent Action Outcomes

具有随机代理行为结果的目标识别设计

DOI：
发表时间：
2016
期刊：
IJCAI
影响因子：
0
作者：
Wayllace, Christabel;Hou, Ping;Yeoh, William;Son, Tran Cao
通讯作者：
Son, Tran Cao

Proactive Dynamic Distributed Constraint Optimization

主动动态分布式约束优化

DOI：
发表时间：
2016
期刊：
Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems
影响因子：
0
作者：
Hoang, Khoi;Fioretto, Ferdinando;Hou, Ping;Yokoo, Makoto;Yeoh, William;Zivan, Roie
通讯作者：
Zivan, Roie