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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566586
• 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、排雷以及为保护个人、资源或基础设施而进行的许多监视行动。搜索人员（人类和狗）或自主无人驾驶车辆（机器人）可以搜索幸存者，陆地或水下地雷，或非法活动和异常行为。但是，如何以及在哪里搜索呢？答案在于良好而有效的搜索计划，以确保最好地利用稀缺和受限的搜索资源，同时将搜索团队的风险降至最低。因此，搜索代理必须定义搜索区域并规划搜索路径，以便在退化和快速变化的条件下最大化其操作成功的机会。这是一个特别重要的阶段，特别是在下落、可探测性和幸存者、威胁或搜索对象的状况不确定的情况下。在本研究计划中，我们对存在多个冲突目标的不确定性下的路径规划感兴趣。我们努力为规划者（决策者）提供严谨有效的方法，帮助他们应对在不确定和不友好的环境中所面临的挑战。为了做到这一点，我们打算开发基于代表现实生活中遇到的问题的严格数学模型的决策支持工具。这是为了帮助决策者（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、能见度限制、威胁暴露、时间限制、探测能力、可用资源类型、环境条件等。在存在多个相互冲突的目标时，研究不确定性下路径规划的项目很少。大多数已发布的结果适用于目标是从给定点到达已知目的地的情况，并且通常基于单一目标优化。因此，在解决这一难题方面，有必要推进研究并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的方法，提供有效的解决方案，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观。此外，决策者了解拟议的搜索计划是如何产生的以及为什么产生的，这一点非常重要；这些不应被视为黑盒的结果。由于这个原因，向决策者提供解释模块会带来很大的额外好处，这些模块会介绍所建议的搜索计划背后的基本原理。在本提案中，我们将设计、开发、验证和评估多智能体（如自动驾驶汽车）在多目标环境下的路径规划算法，这些算法对搜索对象的位置和检测能力具有不确定性。随后，我们将开发基于离散多标准决策分析的模型和方法，指导决策者对路径计划的选择，明确地整合他/她的偏好和价值观，并允许他/她探索各种备选方案，并通过地理信息系统将其可视化。最后，我们将探讨如何应用计算论证来为提出的解决方案提供解释和论证。除了对决策者有价值之外，在多标准决策框架中引入计算论证将是前沿研究。