[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.

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

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

Course Info

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