Automatic Sampling of Geographic objects

Today, one's disposes of large datasets composed of thousands of geographic objects. However, for many processes, which require the appraisal of an expert or much computational time, only a small part of these objects can be taken into account. In this context, robust sampling methods become necessary. In this paper, we propose a sampling method based on clustering techniques. Our method consists in dividing the objects in clusters, then in selecting in each cluster, the most representative objects. A case-study in the context of a process dedicated to knowledge revision for geographic data generalisation is presented. This case-study shows that our method allows to select relevant samples of objects

Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation., Paris : France (2008

The Kalai-Smorodinski solution for many-objective Bayesian optimization

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on four problems with respectively four, six, eight, and nine objectives. The method is available in the Rpackage GPGame available on CRAN at https://cran.r-project.org/package=GPGame

Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots

International audienceThe exploration of an unknown environment by a robot system (an individual robot or a team of robots) is a well-studied problem in robotics. This problem has many applications and, among them, the post-disaster search of victims in an urban space. Most of proposed exploration algorithms are based on the use of specific criteria to define the quality of the possible movements. In this paper, we propose an exploration approach based on the combination of several criteria thanks to the PROMETHEE II multi-criteria decision making method. The PROMETHEE II method allows one to establish a complete ranking between possible movements based on outranking relations. Experimental results show that this approach can be used to effectively combine different criteria and outperforms several classic exploration strategies

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

International audienceBuilding design involves many challenges and requires to take into account the interaction between the building and the users. Different occupant behaviour models implemented with building simulation tools (thermal, air quality, lighting) have been proposed. Among these, models based on the agent approach seem to be the most promising. However, existing models poorly describe human cognition and the social dimension. Moreover, they are often oriented towards a specific use (thermal simulation, waste management) without being transposable to another field, and they require a significant instantiation effort for each new case, making their use difficult. This article proposes an agent-based model called Li-BIM that simulates the behaviour of the occupants in a building and their indoor comfort. Li-BIM model is structured around the numerical modelling of the building-BIM-(with standard exchange format IFC), a high-resolution cognitive model, and the coupling with various physical models. Li-BIM simulates the reactive, deliberative and social behaviour of occupants in residential dwellings based on the Belief-Desire-Intention architecture. This model, thanks its ease of use and flexibility, is an operational and relevant tool to support building design process with a human-centred approach. An application of the model is presented, focusing on energy consumption and the inhabitants' comfort. In-situ data obtained from the instrumented house that served as case study have been compared with simulation results from Li-BIM and a standard energy simulation software, demonstrating the reliability of the proposed model

An interactive decision support method for real estate management in a multi-criteria framework – REMIND

Managing a housing stock involves complex decision making such as the design of a multiyear action plan pertaining to the maintenance and upgrading of the properties. In order to address this problem, we developed a novel interactive decision support method (REMIND) to assist a housing stock manager in the progressive design and choice of a multiyear action plan based on multiple criteria. It uses a filtering approach both at the individual action level and at the global scenario level where the housing stock manager can gradually express preferences and conduct what-if analyses. An optimization component based on Tabu search allows the decision-maker to obtain a set of good plans from which he can choose the one to implement. The quality of a plan is defined in terms of how well it meets the goals on each criterion. The application of the method was tested in a leading French property management company

Une architecture d'agent BDI basée sur la théorie des fonctions de croyance : application à la simulation du comportement des agriculteurs

National audienceLa simulation à base d'agents est maintenant largement utilisée pour étudier les systèmes complexes. Cependant, le problème de la définition des agents est toujours posé. Définir des agents complexes capables d'agir de manière réaliste est une tâche difficile. Un paradigme couramment utilisé pour formaliser le comportement de tels agents est le paradigme BDI (Belief-Desire-Intention). Cependant, ce formalisme est peu utilisé en simulation. Une raison est que la plupart des architectures basées sur celui-ci sont très complexes à comprendre pour des non informaticiens. De plus, elles sont en générales très lourdes en termes de temps de calcul. Dans cet article, nous proposons ici une architecture agent basée sur le paradigme BDI et sur la théorie des fonctions de croyance qui permet de répondre aux difficultés précitées. Nous présentons une application de celle-ci pour la simulation du choix et de la conduite de systèmes de culture par des agriculteurs. Cette application montre que notre architecture permet de faire tourner plusieurs milliers d'agents simultanément