人工智慧與序列式決策
AI in Sequential Decision Making
| 節 | 週二 | 週三 |
|---|---|---|
8 16:30–17:20 | 人工智慧與序列式決策 MB415 2 節連堂 | |
9 17:30–18:20 | 人工智慧與序列式決策 MB415 |
* 根據陽明交大上課時間表所列
This course introduces artificial intelligence methodologies for sequential decision-making problems, where decisions must be made repeatedly over time and each action influences future system states and outcomes. The course emphasizes decision modeling, learning-based heuristics, and empirical evaluation, rather than low-level algorithmic engineering or neural network design. Reinforcement learning, approximate dynamic programming, hybrid heuristics, and emerging LLM-assisted decision frameworks are presented as black-box decision engines for solving complex optimization and control problems in manufacturing, logistics, energy systems, and other engineered systems.
Programming
All the materials can be downloaded from the online course registration system.
Participation (5%) Homework (25%) Midterm paper presentation (25%) Term project (45%)
- Learning-Based Decision Methods
- Advanced Topics and Applications
- Foundations of Sequential Decision Making
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course overview and motivation: sequential decision making |
| 第 2 週 | Decision modeling: state, action, and reward design |
| 第 3 週 | Dynamic programming (DP) intuition |
| 第 4 週 | Approximate dynamic programming (ADP) |
| 第 5 週 | Reinforcement learning (RL) for decision problems |
| 第 6 週 | Deep reinforcement learning (DRL) as function approximation |
| 第 7 週 | Adaptive metaheuristics and heuristic selection |
| 第 8 週 | Midterm paper presentation |
| 第 9 週 | Hybrid learning-based decision systems |
| 第 10 週 | Constraint-aware sequential decision making |
| 第 11 週 | Learning-augmented and online decisions |
| 第 12 週 | Multi-stage and decentralized decision problems |
| 第 13 週 | LLM-assisted decision-making frameworks |
| 第 14 週 | Term project |
| 第 15 週 | Term project |
| 第 16 週 | Term project |
The lecture is given based on handouts. Parts of the handouts are referred to the following books and articles: Bertsekas, D. P. Dynamic Programming and Optimal Control, Vol. I & II. Athena Scientific, 4th Edition, 2017. Powell, W. B. Approximate Dynamic Programming: Solving the Curses of Dimensionality. Wiley, 2nd Edition, 2011. Sutton, R. S., & Barto, A. G. Reinforcement Learning: An Introduction. MIT Press, 2nd Edition, 2018. Rao, A., & Jelvis, T. Foundations of Reinforcement Learning with Applications in Finance. Chapman & Hall/CRC Press, 2023. Supplementary Reading : Selected journal articles from International Journal of Production Research, Computers & Industrial Engineering, Applied Soft Computing, Robotics and Computer-Integrated Manufacturing, IEEE Transactions on Industrial Informatics, European Journal of Operational Research, and related journals (assigned during the semester).
- 地點
- MB501
- 時間
- Tue. 12:00-13:00 (e-mail contact in advance)
- 聯絡方式
- cclin321@nycu.edu.tw