[AIMA] = Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd ed. Prentice Hall, 2009. ISBN: 9780136042594.
[IOR] = Hillier, Frederick, and Gerald Lieberman. Introduction to Operations Research. 9th ed. McGraw-Hill, 2009. ISBN: 9780077298340.
[PA] = LaValle, Steven. Planning Algorithms. Cambridge University Press, 2006. ISBN: 9780521862059.
[JINS] = Flanagan, David. Java in a Nutshell. 5th ed. O’Reilly, 2005. ISBN: 9780596007737.
LEC # | TOPICS | READINGS |
---|---|---|
1 | Introduction |
[AIMA] Chapter 2 [JINS] Chapters 1-3 and 5 |
2 | Foundations I: state space search | [AIMA] Chapter 3.1-4 |
3 | Foundations II: complexity of state space search |
6.042J Lecture Notes: Recurrences 6.046 Lecture Notes: Asymptotic analysis (PDF) (Courtesy of Erik Demaine. Used with permission.) |
4 | Foundations III: soundness and completeness of search | 6.042J Lecture Notes: Proofs, induction I |
5 | Constraints I: constraint programming | [AIMA] Chapter 6.1, chapter 24.3-5 |
6 | Constraints II: constraint satisfaction | [AIMA] Chapters 6.2-6.5 |
7 |
Constraints III: conflict-directed back jumping Planning I: activity planning |
[AIMA] Chapter 10 Blum, Avrim, and Merrick Furst. “Fast Planning Through Planning Graph Analysis.” Artificial Intelligence 90 (1997): 281-300. |
8 | Planning II: graph plan | [AIMA] Chapter 11 |
9 | Planning III: plan termination and plan execution | Dechter, Rina, Itay Meiri, and Judea Pearl. “Temporal Constraint Networks.” Artificial Intelligence 49 (1991): 61-95. |
10 | Model-based reasoning I: propositional logic and satisfiability | [AIMA] Chapters 7-8 |
11 | Encoding planning problems as propositional logic satisfiability | |
Midterm exam | ||
12 | Model-based reasoning II: diagnosis and mode estimation | de Kleer, Johan, and Brian Williams. “Diagnosing Multiple Faults.” Artificial Intelligence 32 (1987): 100-117. |
13 | Model-based reasoning III: OpSat and conflict-directed A* | Williams, Brian, and Robert Ragno. “Conflict-directed A* and Its Role in Model-based Embedded Systems.” Discrete Applied Mathematics 155 (2007): 1562-1595. |
14 | Global path planning I: informed search |
[AIMA] Chapter 3.5-6 [PA] Chapter 2.3 |
15 | Global path planning II: sampling-based algorithms for motion planning |
[PA] Chapter 5 Karaman, Sertac, and Emilio Frazzoli. “Sampling-based Algorithms for Optimal Motion Planning.” International Journal of Robotics Research 30, no. 7 (2011): 846-894. |
16 | Mathematical programming I |
[IOR] Chapters 2, 3, and 9.1-3 [PA] Chapter 6.1-2 |
17 | Mathematical programming II: the simplex method | [IOR] Chapter 4 |
18 | Mathematical programming III: (mixed-integer) linear programming for vehicle routing and motion planning | [IOR] Chapter 11 |
19 | Reasoning in an uncertain world | [AIMA] Chapters 13 and 14.1-14.5 |
20 | Inferring state in an uncertain world I: introduction to hidden Markov models |
[AIMA] Chapters 15.1-3 and 20.3 Rabiner, Lawrence. “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition.” Proceedings of the IEEE 77 (1989): 257-286. |
21 | Inferring state in an uncertain world II: hidden Markov models, the Baum-Welch algorithm | |
22 | Dynamic programming and machine learning I: Markov decision processes | [AIMA] Chapter 17.1-3 |
23 | Dynamic programming and machine learning II: Markov decision processes, policy iteration | |
24 | Game theory I: sequential games |
[AIMA] Chapters 5 and 17.5 [PA] Chapters 9.3-4 and 10.5 |
25 | Game theory II: differential games | [AIMA] Chapter 17.6 |