Preference Reasoning in Constraint-based Systems

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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. **