Multicriteria/multiobjective Decision Making in Humanitarian Operations - Path planning under Uncertainty

人道主义行动中的多标准/多目标决策——不确定性下的路径规划

• 批准号: RGPIN-2014-04540
• 负责人: AbiZeid, Irène
• 金额: $ 1.46万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611097
• 关键词: Multicriteria; multiobjective; Decision; Making; Humanitarian

The act of searching is an important part of many humanitarian operations, such as search and rescue, demining, and of many surveillance operations for the purpose of protecting individuals, resources or infrastructures. Teams of searchers (humans and dogs) or autonomous unmanned vehicles (robots) may search for survivors, land or underwater mines, or illicit activities and abnormal behaviors. But, how and where to search? The answer lies in good and efficient search planning that ensures the best use of scarce and constrained search resources, while minimizing the risks to the search teams. Search agents must therefore define search areas and plan search paths in order to maximize the chances of their operations success, often in degraded and rapidly changing conditions. This is a particularly important phase, especially in the presence of uncertainty on the whereabouts, the detectability, and the conditions of the survivors, the threats, or the search objects. In this research program, we are interested in path planning under uncertainty in the presence of multiple and conflicting objectives. We strive to provide the planners (decision makers) with rigorous and efficient methods to help them address the challenges they face in uncertain and unfriendly environments. In order to do so, we intend to develop decision support tools based on rigorous mathematical models representative of the real-life problems encountered. This is needed in order to assist decision makers (search planners) in defining adequate and realistic search plans that take into account the multiple dimensions of search operations such as terrain constraints, physical constraints, visibility constraints, threat exposure, time constraints, detection capabilities, type of resources available, environmental conditions, etc. Research projects addressing path planning under uncertainty in the presence of multiple conflicting objectives are scarce. Most of the published results apply to situations where the goal is to get from a given point to a known destination, and are often based on a single objective optimization. Therefore, there is a need to advance research and ample opportunity for innovation in tackling this difficult problem. Our goal is to develop approaches integrating multiobjective optimization techniques that provide efficient solutions, with multicriteria decision analysis techniques that explicitly take into account the preferences and values of the decision maker in the loop. In addition, it is very important that decision makers understand how and why the proposed search plans were produced; these should not be perceived as the outcomes of a black box. For this reason, there is a great added benefit in providing the decision makers with explanation modules that present the rationale behind the proposed search plans. In this proposal, we will design, develop, validate and evaluate algorithms for path planning of multi-agents such as autonomous vehicles in the context of multiple objectives, with uncertainty on the whereabouts of the search objects, and on the detection capabilities. Subsequently, we will develop models and methods, based on discrete multicriteria decision analysis, that guide a decision maker’s choice of a path plan, that explicitly integrate his/her preferences and values, and that allow him/her to explore the various alternatives and visualize them with a geographic information system. Finally, we will explore how computational argumentation can be applied to provide explanations and argue for the proposed solutions. In addition to it being valuable for the decision maker, the introduction of computational argumentation in the multicriteria decision making framework would be leading edge research.

搜索行为是许多人道主义行动的重要组成部分，例如搜索和救援，降解以及许多监视行动，以保护个人，资源或基础设施。搜索者（人类和狗）或自动驾驶汽车（机器人）的团队可能会寻找生存，土地或水下矿山，或者非法活动和异常行为。但是，如何以及在哪里搜索？答案在于良好而有效的搜索计划，可确保最佳利用稀缺和限制的搜索资源，同时最大程度地减少搜索团队的风险。因此，搜索代理必须定义搜索区域并计划搜索路径，以最大程度地利用其运营成功的机会，通常是在退化和快速变化的条件下。这是一个特别重要的阶段，尤其是在存在下落，可检测性以及生存，威胁或搜索对象的条件的情况下。 在该研究计划中，我们对在有多个目标存在的不确定性下的路径规划感兴趣。我们努力为计划者（决策者）提供严格而有效的方法，以帮助他们解决他们在不确定和不友好的环境中面临的挑战。为此，我们打算根据代表遇到的现实生活问题的严格数学模型开发决策支持工具。这是为了帮助决策者（搜索计划者）定义足够且现实的搜索计划，以考虑搜索操作的多个维度，例如地形约束，物理约束，可见性约束，威胁敞口，时间限制，时间限制，检测能力，可用的资源类型，可用环境条件等， 在存在多个冲突目标的情况下，在不确定性下解决路径计划的研究项目很少。大多数已发布的结果都适用于目标是从给定点到达已知目的地的目标，并且通常基于单个目标优化。因此，有必要促进研究和足够的创新机会来解决这个困难问题。我们的目标是开发整合提供有效解决方案的多目标优化技术的方法，并与多标准决策分析技术明确考虑了循环中决策者的偏好和价值。此外，决策者了解如何以及为什么制定拟议的搜索计划，这一点非常重要。这些不应被视为黑匣子的结果。因此，在为决策者提供拟议搜索计划背后的理由的解释模块方面有一个巨大的好处。 在此提案中，我们将在多个目标的背景下设计，开发，验证和评估算法，例如自动驾驶汽车等多机车的路径规划，对搜索对象的下落以及检测功能的不确定性存在不确定性。随后，我们将基于离散的多准则决策分析开发模型和方法，以指导决策者选择路径计划，从而明确整合他/她的偏好和价值观，并使他/她能够探索各种替代方案并将其与地理信息系统形象化。最后，我们将探讨如何应用计算论证来提供解释并为所提出的解决方案争论。除了对决策者的价值之外，在多准则决策框架中引入计算论证将是领先的研究。