Lecture Notes

Instructors

[BW] = Prof. Brian Williams
[EF] = Prof. Emilio Frazzoli
[SK] = Sertac Karaman

LEC # TOPICS LECTURE NOTES
1 Introduction [BW, EF]

( PDF - 1.0 MB)

( PDF)

2 Foundations I: state space search [BW] ( PDF)
3 Foundations II: complexity of state space search [BW] ( PDF)
4 Foundations III: soundness and completeness of search [SK] ( PDF) (Courtesy of Sertac Karaman. Used with permission.)
5 Constraints I: constraint programming [BW] ( PDF)
6 Constraints II: constraint satisfaction [BW] ( PDF)
7

Constraints III: conflict-directed back jumping

Introduction to operator-based planning [BW]

( PDF) (With contributions from Patrick Prosser. Used with permission.)

( PDF) (With contributions from David Smith. Used with permission.)

8

Planning I: operator-based planning and plan graphs

Planning II: plan extraction and analysis [BW]

( PDF) (With contributions from Maria Fox. Used with permission.)

( PDF) (With contributions from Maria Fox. Used with permission.)

9 Planning III: robust execution of temporal plans [BW] ( PDF) (With contributions from Andreas Hofmann and Julie Shah. Used with permission.)
10 Model-based reasoning I: propositional logic and satisfiability [BW] ( PDF)
11

Model-based programming of robotic space explorers [BW]

Encoding planning problems as propositional logic satisfiability [SK]

DARPA Urban Challenge videos

( PDF - 1.1 MB)

( PDF) (Courtesy of Sertac Karaman. Used with permission.)

  Midterm exam  
12 Model-based reasoning II: diagnosis and mode estimation [BW] ( PDF 1 - 1.6MB) ( PDF 2 - 2.0MB)
13 Model-based reasoning III: OpSat and conflict-directed A* [BW] ( PDF)
14 Global path planning I: informed search [EF] ( PDF)
15 Global path planning II: sampling-based algorithms for motion planning [EF] ( PDF - 1.3MB)
16 Mathematical programming I [EF] ( PDF)
17 Mathematical programming II: the simplex method [EF] ( PDF)
18 Mathematical programming III: (mixed-integer) linear programming for vehicle routing and motion planning [EF] ( PDF)
19 Reasoning in an uncertain world [BW] ( PDF 1) ( PDF 2)
20 Inferring state in an uncertain world I: introduction to hidden Markov models [EF] ( PDF)
21 Inferring state in an uncertain world II: hidden Markov models, the Baum-Welch algorithm [EF] ( PDF)
22 Dynamic programming and machine learning I: Markov decision processes [EF] ( PDF)
23 Dynamic programming and machine learning II: Markov decision processes, policy iteration [EF] ( PDF)
24 Game theory I: sequential games [EF] ( PDF)
25 Game theory II: differential games [SK] ( PDF - 1.7MB) (Courtesy of Sertac Karaman. Used with permission.)

Course Info

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