Preference Reasoning in Constraint-based Systems

基于约束的系统中的偏好推理

• 批准号: RGPIN-2016-05673
• 负责人: Mouhoub, Malek
• 金额: $ 1.6万
• 依托单位: University of Regina
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=660348
• 关键词: Preference; Reasoning; Constraint; based; Systems

Constraints and preferences co-exist in a wide variety of real world problems, including scheduling, planning, vehicle routing, resource allocation and Geographic Information Systems (GIS) applications. Constraints refer to problem requirements that must be met, while preferences can be either qualitative or quantitative and reflect desires and choices that need to be satisfied as much as possible. In some applications such as urban planning and robot motion planning, these constraints and preferences can be temporal, spatial or both. In this latter case, we will have to deal with entities occupying a given position in time and space. Spatio-temporal data can be symbolic or numeric and may correspond to multiple levels of detail.***The main goal of the proposed research program is to extend the features of the current constraint solving systems by developing new techniques leading to a technology that faithfully represents real world industrial applications. In this regard, we plan to develop a unique framework and its related algorithms for managing the above types of constraints and preferences in an evolving environment.***Given a real world application under constraints and preferences, the main task of this framework is to return, in an efficient way, the best outcomes satisfying all the constraints and optimizing all the preferences. The current solving systems work based on idealized assumptions of environment stability. Our proposed framework will be able to maintain, in an incremental way, this set of optimal solutions anytime a constraint or a preference is added or removed. This feature will overcome the limitations of the current solving systems when tackling applications operating under highly dynamic and unpredictable environmental conditions. Through this dynamic feature, as well as the constraint and preference learning algorithms we propose, our framework will have the ability to interact with the user and allows him or her to model a given problem under constraints and preferences. This will address the challenge that users have to face when modelling these problems using the current solvers. Indeed, the current systems require logic, traditional programming skills, as well as a significant background in constraint programming for the end-user to model even simple problems.***Our framework will have the ability to handle constraints and preferences with uncertainty due to lack of knowledge, missing information or variability caused by events which are under nature's control. This will be achieved by extending the probability and the possibility theories to general as well as spatio-temporal constraints and preferences.***Finally, by achieving the proposed research program we will successfully address diverse complex industrial problems, including reactive scheduling, urban planning, timetabling, robotics, transportation, configuration and E-commerce. **

约束和偏好共存于各种现实世界问题中，包括调度、规划、车辆路线、资源分配和地理信息系统 (GIS) 应用。约束是指必须满足的问题要求，而偏好可以是定性的，也可以是定量的，反映需要尽可能满足的愿望和选择。在城市规划和机器人运动规划等一些应用中，这些约束和偏好可以是时间的、空间的或两者兼而有之。在后一种情况下，我们将不得不处理在时间和空间上占据给定位置的实体。时空数据可以是符号的或数字的，并且可能对应于多个细节级别。***所提议的研究计划的主要目标是通过开发新技术来扩展当前约束求解系统的功能，从而形成忠实代表现实世界工业应用的技术。在这方面，我们计划开发一个独特的框架及其相关算法，用于在不断发展的环境中管理上述类型的约束和偏好。***给定约束和偏好下的现实世界应用程序，该框架的主要任务是以有效的方式返回满足所有约束并优化所有偏好的最佳结果。当前的求解系统基于环境稳定性的理想化假设。我们提出的框架将能够在添加或删除约束或偏好时以增量方式维护这组最优解决方案。当处理在高度动态和不可预测的环境条件下运行的应用程序时，此功能将克服当前求解系统的局限性。通过这种动态功能，以及我们提出的约束和偏好学习算法，我们的框架将能够与用户交互，并允许他或她在约束和偏好下对给定问题进行建模。这将解决用户在使用当前求解器对这些问题进行建模时必须面临的挑战。事实上，当前的系统需要逻辑、传统编程技能以及约束编程的重要背景，以便最终用户能够对简单的问题进行建模。***我们的框架将有能力处理由于缺乏知识、信息缺失或自然控制下的事件引起的可变性而带来的不确定性约束和偏好。这将通过将概率和可能性理论扩展到一般以及时空约束和偏好来实现。***最后，通过实现拟议的研究计划，我们将成功解决各种复杂的工业问题，包括反应性调度、城市规划、时间表、机器人、交通、配置和电子商务。 **