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Principles of Autonomy and Decision Making >> Content Detail



Study Materials



Readings

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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] Amazon logo 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] Amazon logo 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] Amazon logo Flanagan, David. Java® in a Nutshell. Sebastopol, CA: O'Reilly, 2005. ISBN: 0596007736.


LEC #TOPICSREADINGS
1Principles of Autonomy and Decision MakingAIMA. Chapters 1 and 2.
2A 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
3Formulating Problem Solving as State Space Search

Optional Lecture: More Fun with Java®
AIMA. Chapter 3.
4Problem Solving with Java®JINS. Chapters 1-3 and 5 (cont.)

AIMA. Chapter 3 (cont.)
5Asymptotic Analysis of Uninformed Search MethodsAIMA. Chapter 3 (cont.)
6Global Path-Planning via Optimal Search and Shortest PathsAIMA. Chapter 4.
7Roadmaps and Adversarial GamesAIMA. Chapter 25, (except section 25.3)
8Solving Linear Programs using SimplexIOR. Chapter 3.
9Kinodymanic Path-Planning using Linear ProgramsIOR. Chapters 4 and 5.
10Formulating Visual Interpretation using Constraint ProgrammingAIMA. Chapter 24, (except section 2)

AIMA. Chapter 5.

Handout (PDF)
11Solving Constraint Programs using Inference and SearchAIMA. Chapter 5 (cont.)
12Activity Planning and Plan GraphsAIMA. Chapter 11.
13Plan Extraction in Graph PlanAIMA. Chapter 12.

Blum, Avrim L., and Merrick L. Furst. "Fast Planning Through Planning Graph Analysis." Artificial Intelligence 90 (1997): 281-300.
Mid-term Examination
14Planning and Execution in a Changing WorldAIMA. Chapter 13.
15Modelling using Propositional LogicAIMA. Chapter 14.
16Propositional SatisfiabilityAIMA. Chapter 25, section 3.

AIMA. Chapter 15, (except section 5)
17Entailment and Inference in Propositional LogicAIMA. Chapter 6.
18Model-Based Diagnosis and Conflict-directed SearchAIMA. Chapter 6 (cont.)
19Introduction to Probabilistic Reasoning
20Probabilistic State Estimation and Robot LocalizationIOR. Chapter 13.
21Formulating Utility-based Agents using Markov Decision ProcessesIOR. Chapter 3 (cont.)
2216.413 Student Project PresentationsAIMA. Chapter 17, (except section 5)

AIMA. Chapter 21.
23Learning from Observations through Inductive MethodsAIMA. Chapter 18.
24Learning from Observations through Statistical Methods
25Making Decisions through Finite Domain Constraint OptimizationAIMA. Chapter 20.
26Final Exam Review
Final Exam

 








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