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Readings

In the readings column, "AIMA" refers to the course textbook, Artificial Intelligence: A Modern Approach, 2nd Edition, by Stuart Russell and Peter Norvig, Prentice Hall PTR, 2002.

LEC # TOPICS READINGS
1 Introduction to Principles of Autonomy and Decision Making
Jump Starting With Scheme
AIMA. Chapters 1 and 2.
2 Some Scheme "Teach Yourself Scheme in Fixnum Days." (PDF)
3 Rule-based Systems AIMA. Chapters 9.2-9.4.
4 Problem Solving as State Space Search AIMA. Chapter 3.
5 Analysis of Uninformed Search Methods
Some More Scheme
AIMA. Chapter 3.
6 Even More Scheme "Teach Yourself Scheme in Fixnum Days." (PDF)
7 Constraint Satisfaction Problems: Formulations
Solving Constraint Satisfaction Problems: Arc Consistency and Constraint Propagation
AIMA. Chapters 5 and 24.4 (pp. 881-884).
8 Solving Constraint Satisfaction Problems: Forward Checking
Propositional Logic and Satisfiability
AIMA. Chapters 5 and 6.
9 Propositional Logic and Satisfiability (cont.)
10 Graph-based Planning AIMA. Chapter 11.
11 Partial Order Planning and Execution AIMA. Chapter 12.
12 Mid-term Exam
13 Programming SATPlan AIMA. Chapter 7.
14 Roadmap Path Planning
Shortest Path and Informed Search
AIMA. Chapter 25.4.
AIMA. Chapter 4.1-2.
Introduction to Algorithms, 2nd Edition, by Cormen, Leiserson, Rivest, and Stein. Chapters 25.1-2.
15 Shortest Path and Informed Search (cont.)
16 Model-based Diagnosis AIMA. Chapters 4.1-2 and 25.4.
17 Model-based Diagnosis (new slides) AIMA. Chapter 6.
18 Introduction to Linear Programming Handouts:
AMPL (PDF)
Steel (PDF)
19 Spacecraft Rendezvous and Rover Path Planning Using Linear Programs Handout:
Linear Programming Example
20 Basis Theory
21 Integer Programming and Branch and Bound
22 16.413 Project Group Meetings
23 Particle Filters for Fun and Profit AIMA. Chapter 6.
24 Student Presentations
25 Learning to Act Optimally: Reinforcement Learning
Planning to Maximize Reward: Markov Decision Processes
AIMA. Chapter 6.
26 Principles of Autonomy and Decision Making
27 Final Exam