Summary of Domain-independent Dynamic Programming, by Ryo Kuroiwa et al.
Domain-Independent Dynamic Programming
by Ryo Kuroiwa, J. Christopher Beck
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Domain-Independent Dynamic Programming (DIDP) model-based paradigm aims to decouple modeling and solving combinatorial optimization problems. By introducing the Dynamic Programming Description Language (DyPDL), a formalism inspired by artificial intelligence planning, DIDP models can be defined as state transition systems. This allows for the use of heuristic search algorithms to solve these models. The authors experimentally compare their DIDP solvers with commercial mixed-integer programming (MIP) and constraint programming (CP) solvers on various benchmark instances, showing that DIDP outperforms MIP in nine problem classes, CP in nine problem classes, and both MIP and CP in seven. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DIDP is a new way to solve problems. It’s like a blueprint for solving puzzles. The authors created a special language called DyPDL to make it easy to define these blueprints. They then used computers to try different solutions and found the best one. They compared their method to other popular methods, like MIP and CP, and showed that DIDP is often better. |
Keywords
» Artificial intelligence » Optimization
