Many machine-learning systems can learn patterns from images or sensor data, but they often struggle when decisions must follow strict rules. Real-world tasks may involve budgets, capacities, safety limits, or actions that are required or forbidden.<\/p>\n\n\n\n
Led by Georg Martius, this project investigated how neural networks can be combined with combinatorial optimization<\/strong>\u2014mathematical methods for choosing the best option from many discrete possibilities. The project developed ways to embed optimization algorithms into learning systems, allowing AI to learn from data while making decisions that respect predefined constraints. It ran from 2021 to 2024.<\/p>\n\n\n\n The project provides a foundation for AI systems that connect perception with structured, constraint-aware decision-making. Such systems could help robots plan actions safely, enable logistics systems to allocate limited resources, and support applications in scheduling, planning, and operations management.<\/p>\n\n\n\n The approach does not replace classical optimization solvers. Instead, it integrates them into larger learning pipelines so that neural networks can interpret data while established algorithms handle rules and constraints. This combination could make AI systems more reliable and adaptable, although their performance still depends on how accurately the relevant constraints and optimization problem are modelled.<\/p>\n\n\n\nKey results<\/h2>\n\n\n\n
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Why it matters<\/h2>\n\n\n\n
This project produced the following peer-reviewed publications: <\/h2>\n\n\n\n
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