تقرير
Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization
| العنوان: | Domain-Independent Dynamic Programming: Generic State Space Search for Combinatorial Optimization |
|---|---|
| المؤلفون: | Kuroiwa, Ryo, Beck, J. Christopher |
| المصدر: | Proceedings of the International Conference on Automated Planning and Scheduling, 33(1), 2023, 236-244 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence |
| الوصف: | For combinatorial optimization problems, model-based approaches such as mixed-integer programming (MIP) and constraint programming (CP) aim to decouple modeling and solving a problem: the 'holy grail' of declarative problem solving. We propose domain-independent dynamic programming (DIDP), a new model-based paradigm based on dynamic programming (DP). While DP is not new, it has typically been implemented as a problem-specific method. We propose Dynamic Programming Description Language (DyPDL), a formalism to define DP models, and develop Cost-Algebraic A* Solver for DyPDL (CAASDy), a generic solver for DyPDL using state space search. We formalize existing problem-specific DP and state space search methods for combinatorial optimization problems as DP models in DyPDL. Using CAASDy and commercial MIP and CP solvers, we experimentally compare the DP models with existing MIP and CP models, showing that, despite its nascent nature, CAASDy outperforms MIP and CP on a number of common problem classes. Comment: This paper was accepted at the 33rd International Conference on Automated Planning and Scheduling (ICAPS) 2023 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1609/icaps.v33i1.27200 |
| URL الوصول: | http://arxiv.org/abs/2211.14409 |
| رقم الأكسشن: | edsarx.2211.14409 |
| قاعدة البيانات: | arXiv |
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