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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587869
• 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.

搜索行动是许多人道主义行动的重要组成部分，如搜索和救援、排雷以及许多旨在保护个人、资源或基础设施的监视行动。搜索队（人和狗）或自动无人驾驶车辆（机器人）可以搜索幸存者，土地或水下地雷，或非法活动和异常行为。但是，如何以及在哪里搜索呢？答案在于良好和有效的搜索规划，确保最佳利用稀缺和受限的搜索资源，同时最大限度地减少搜索团队的风险。因此，搜索代理必须界定搜索区域和规划搜索路径，以便最大限度地提高其业务成功的机会，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，特别是在幸存者、威胁或搜索对象的下落、可探测性和状况不确定的情况下。 在这个研究项目中，我们感兴趣的是在不确定性下的路径规划存在多个和相互冲突的目标。我们致力于为规划者（决策者）提供严格有效的方法，帮助他们应对在不确定和不友好的环境中面临的挑战。为了做到这一点，我们打算开发决策支持工具的基础上，严格的数学模型代表的现实生活中遇到的问题。这是为了协助决策者（搜索规划者）制定充分和现实的搜索计划，考虑到搜索行动的多个方面，如地形限制、物理限制、可见度限制、威胁暴露、时间限制、探测能力、可用资源类型、环境条件等。 研究项目解决路径规划下的不确定性存在多个相互冲突的目标是稀缺的。大多数已发表的结果适用于目标是从给定点到达已知目的地的情况，并且通常基于单目标优化。因此，有必要推进研究，并为解决这一难题提供充分的创新机会。我们的目标是开发集成多目标优化技术，提供有效的解决方案，与多准则决策分析技术，明确考虑到决策者的偏好和价值观的循环方法。此外，决策者必须了解拟议的搜索计划是如何以及为什么制定的，这一点非常重要;这些不应被视为黑匣子的结果。因此，向决策者提供解释模块，说明拟议搜索计划背后的理由，有很大的额外好处。 在这项提案中，我们将设计，开发，验证和评估算法的路径规划的多智能体，如自动驾驶汽车在多个目标的背景下，与不确定性的搜索对象的下落，并在检测能力。随后，我们将开发模型和方法，基于离散多准则决策分析，指导决策者的选择的路径规划，明确地整合他/她的偏好和价值观，并允许他/她探索各种替代方案，并将其可视化与地理信息系统。最后，我们将探讨如何计算论证可以应用于提供解释和论证所提出的解决方案。除了对决策者有价值之外，在多准则决策框架中引入计算论证将是前沿研究。