Burning fossil fuels is a big part of our heat production. Since this process is both non-renewable and polluting, finding other options is important. A clean and underutilized alternative is geothermal energy. However, it is often not considered due to sheer ignorance or misconceptions. HotPipe is a serious game designed to alleviate these problems, particularly among youth populations. Players control a drill to create geothermal wells solving a variety of puzzles, which introduce relevant cases for geothermal heating and show what geothermal wells are made of. The game focuses primarily on conveying the concepts of water circulation, relation between temperature and depth, androck type proprieties. From our game evaluation, players revealed a solid improvement on their geothermal energy knowledge.@en

Contemporary research explores the possibilities of integrating machine learning (ML) approaches with traditional combinatorial optimisation solvers. Since optimisation hybrid solvers, which combine propositional satisfiability (SAT) and constraint programming (CP), dominate recent benchmarks, it is surprising that the literature has paid limited attention to machine learning approaches for hybrid CP–SAT solvers. We identify the technique of minimal unsatisfiable subsets as promising to improve the performance of the hybrid CP–SAT lazy clause generation solver Chuffed. We leverage a graph convolutional network (GCN) model, trained on an adapted version of the MiniZinc benchmark suite. The GCN predicts which variables belong to an unsatisfiable subset on CP instances; these predictions are used to initialise the activity score of Chuffed’s Variable-State Independent Decaying Sum (VSIDS) heuristic. We benchmark the ML-aided Chuffed on the MiniZinc benchmark suite and find a robust 2.5% gain over baseline Chuffed on MRCPSP instances. This paper thus presents the first, to our knowledge, successful application of machine learning to improve hybrid CP–SAT solvers, a step towards improved automatic solving of CP models.@en

Contemporary research explores the possibilities of integrating machine learning (ML) approaches with traditional combinatorial optimisation solvers. Since optimisation hybrid solvers, which combine propositional satisfiability (SAT) and constraint programming (CP), dominate recent benchmarks, it is surprising that the literature has paid limited attention to machine learning approaches for hybrid CP–SAT solvers. We identify a recent technique in the SAT literature called unsatisfiable core learning as promising to improve the performance of the hybrid CP–SAT lazy clause generation solver Chuffed. We leverage a graph convolutional network (GCN) model, trained on an adapted version of the MiniZinc benchmark suite. The GCN predicts which variables belong to an unsatisfiable cores on CP instances; these predictions are used to initialise the activity score of Chuffed’s Variable-State Independent Decaying Sum (VSIDS) heuristic. We benchmark the ML-aided Chuffed on the MiniZinc benchmark suite and find a robust 2.5% gain over baseline Chuffed on MRCPSP instances. This paper thus presents the first, to our knowledge, successful application of machine learning to improve hybrid CP–SAT solvers, a step towards improved automatic solving of CP models.@en