The following is a list of the readings that supplement the lectures. Please read the assigned readings, listed below, before the corresponding lecture is given. Note that the lectures contain material not covered in the texts, and vice-versa. Students are responsible for all materials provided in the lecture notes, and all assigned readings.
The focus of this course is on the design and analysis of methods for reasoning and decision making. The primary, required textbook is:
[AIMA] Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. 2nd ed. Upper Saddle River, NJ: Prentice Hall/Pearson Education, 2003. ISBN: 0137903952.
This text covers most major reasoning, decision making and learning methods presented in the course.
In addition, our optional, recommended text is:
[IOR] Hillier, Frederick S., and Gerald J. Lieberman. Introduction to Operations Research. 8th ed. Boston, MA: McGraw-Hill Higher Education, 2005. ISBN: 0072527447.
This text will only be used to cover the topics of integer and linear programming. However, it offers a good comprehensive text for those who would like to delve further into operations research.
Programming assignments and the course projects will be implemented in the Java® programming language. Java® is a strongly typed language with a syntax similar to C++ and Ada, but simplifies the programming process by offering automatic memory management, and greater machine portability. This is not a programming course; we expect students to have a programming proficiency where they are able to learn programming on their own. As a good basic Java® reference we recommend:
[JINS] Flanagan, David. Java® in a Nutshell. Sebastopol, CA: O'Reilly, 2005. ISBN: 0596007736.
Course readings.
| LEC # |
TOPICS |
READINGS |
| 1 |
Principles of Autonomy and Decision Making |
AIMA. Chapters 1 and 2. |
| 2 |
A Very Brief Introduction to Java® |
JINS. Chapters 1-3 and 5.
Java® Jumpstart (PDF)
Friendly Little Hint About Junit (PDF)
Sun Java® Tutorials
Sun Java® Developer Kit (SDK)
Eclipse Integrated Development Environment
Junit Automated Testing System
Java® Application Programmer Library References |
| 3 |
Formulating Problem Solving as State Space Search
Optional Lecture: More Fun with Java® |
AIMA. Chapter 3. |
| 4 |
Problem Solving with Java® |
JINS. Chapters 1-3 and 5 (cont.)
AIMA. Chapter 3 (cont.) |
| 5 |
Asymptotic Analysis of Uninformed Search Methods |
AIMA. Chapter 3 (cont.) |
| 6 |
Global Path-Planning via Optimal Search and Shortest Paths |
AIMA. Chapter 4. |
| 7 |
Roadmaps and Adversarial Games |
AIMA. Chapter 25, (except section 25.3) |
| 8 |
Solving Linear Programs using Simplex |
IOR. Chapter 3. |
| 9 |
Kinodymanic Path-Planning using Linear Programs |
IOR. Chapters 4 and 5. |
| 10 |
Formulating Visual Interpretation using Constraint Programming |
AIMA. Chapter 24, (except section 2)
AIMA. Chapter 5.
Handout (PDF) |
| 11 |
Solving Constraint Programs using Inference and Search |
AIMA. Chapter 5 (cont.) |
| 12 |
Activity Planning and Plan Graphs |
AIMA. Chapter 11. |
| 13 |
Plan Extraction in Graph Plan |
AIMA. Chapter 12.
Blum, Avrim L., and Merrick L. Furst. "Fast Planning Through Planning Graph Analysis." Artificial Intelligence 90 (1997): 281-300. |
|
Mid-term Examination |
|
| 14 |
Planning and Execution in a Changing World |
AIMA. Chapter 13. |
| 15 |
Modelling using Propositional Logic |
AIMA. Chapter 14. |
| 16 |
Propositional Satisfiability |
AIMA. Chapter 25, section 3.
AIMA. Chapter 15, (except section 5) |
| 17 |
Entailment and Inference in Propositional Logic |
AIMA. Chapter 6. |
| 18 |
Model-Based Diagnosis and Conflict-directed Search |
AIMA. Chapter 6 (cont.) |
| 19 |
Introduction to Probabilistic Reasoning |
|
| 20 |
Probabilistic State Estimation and Robot Localization |
IOR. Chapter 13. |
| 21 |
Formulating Utility-based Agents using Markov Decision Processes |
IOR. Chapter 3 (cont.) |
| 22 |
16.413 Student Project Presentations |
AIMA. Chapter 17, (except section 5)
AIMA. Chapter 21. |
| 23 |
Learning from Observations through Inductive Methods |
AIMA. Chapter 18. |
| 24 |
Learning from Observations through Statistical Methods |
|
| 25 |
Making Decisions through Finite Domain Constraint Optimization |
AIMA. Chapter 20. |
| 26 |
Final Exam Review |
|
|
Final Exam |
|