Agent-based models (ABMs) and video games, including those taking advantage of virtual reality (VR), have undergone a remarkable parallel evolution, achieving impressive levels of complexity and sophistication. This paper argues that while ABMs prioritize scientific analysis and understanding and VR aims for immersive entertainment, they both simulate artificial worlds and can benefit from closer integration. Coupling both approaches indeed opens interesting possibilities for research and development in various fields, and in particular education, at the heart of the SIMPLE project, an EU-funded project on the development of digital tools for awareness raising on environmental issues. However, existing tools often present limitations, including technical complexity, limited functionalities, and lack of interoperability. To address these challenges, we introduce a novel framework for linking GAMA, a popular ABM platform, with Unity, a widely used game engine. This framework enables seamless data exchange, real-time visualization, and user interaction within VR environments, allowing researchers to leverage the strengths of both ABMs and VR for more impactful and engaging simulations. We demonstrate the capabilities of our framework through two prototypes built to highlight its potential in representing and interacting with complex socio-environmental system models. We conclude by emphasizing the importance of continued collaboration between the ABM and VR communities to develop robust, user-friendly tools, paving the way for a new era of collaborative research and immersive experiences in simulations.

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 three problems with respectively four, six, and ten objectives. The method is available in the package GPGame available on CRAN at https://cran.r-project.org/package=GPGame.

Many real world problems can be defined as optimisation problems in which the aim is to maximise an objective function. The quality of obtained solution is directly linked to the pertinence of the used objective function. However, designing such function, which has to translate the user needs, is usually fastidious. In this paper, a method to help user objective functions designing is proposed. Our approach, which is highly interactive, is based on man machine dialogue and more particularly on the comparison of problem instance solutions by the user. We propose an experiment in the domain of cartographic generalisation that shows promising results.

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.

Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging.

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.