Abstract
This paper presents preliminary work on using deep neural networks to guide general-purpose heuristic algorithms for performing utilitarian combinatorial assignment. In more detail, we use deep learning in an attempt to produce heuristics that can be used together with e.g., search algorithms to generate feasible solutions of higher quality more quickly. Our results indicate that our approach could be a promising future method for constructing such heuristics.
This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by grants from the National Graduate School in Computer Science (CUGS), Sweden, Excellence Center at Linköping-Lund for Information Technology (ELLIIT), TAILOR funded by EU Horizon 2020 research and innovation programme (GA 952215), and Knut and Alice Wallenberg Foundation (KAW 2019.0350).
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Präntare, F., Tiger, M., Bergström, D., Appelgren, H., Heintz, F. (2021). Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms. In: Heintz, F., Milano, M., O'Sullivan, B. (eds) Trustworthy AI - Integrating Learning, Optimization and Reasoning. TAILOR 2020. Lecture Notes in Computer Science(), vol 12641. Springer, Cham. https://doi.org/10.1007/978-3-030-73959-1_10
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