- Professor: Rina Dechter
- Electronic Mail: dechter@ics.uci.edu
- Place: ICS 174
- Time: TuTh 12:30 to 1:50
- Office: ICS 424E
- Office Hours : Mondays, 1:30 to 3:30 pm.
- Textbooks:
*Artificial Intelligence: A Modern Approach*, by Russel and Norvig: http://www.cs.berkeley.edu/~russell/aima.html- Classnotes
- Teaching Assistants
- Stephen Bay sbay@ics.uci.edu
- Dmitry Pavlov pavlovd@ics.uci.edu
- Yuh-Jyh Hu, yhu@ics.uci.edu
- Discussion Sections
- 36371 DIS 1: Monday, 1:00 to 1:50, ICF 101
- 36372 DIS 2: Wednesday, 1:00 to 1:50, ICF 101
- 36373 DIS 3: Friday, 1:00 to 1:50, ICF 101

- Search for problem solving,
- Knowledge-representation and reasoning, logic approach,
- Knowledge-representation and reasoning under uncertainty,
- Learning.

Academic honesty is taken seriously. It is the responsibility of each student to be familiar with UCI's current academic honesty policies. Please take the time to read the current UCI Senate Academic Honesty Policies.

- Bi-weakly homework assignment distributed in class each week: the homework will be given on Tuesdays and should be turned in the following Tuesday.
- 15-20 min. quiz every Thursday (almost).
- One programming project.
- Final exam.

Some handouts will be distributed during the quarter by the Distribution Center, others will be available to buy in the Engineering Copy Center.

Homeworks plus project will account for 40%, quizzes - for 20% and final - 40% of the course grade.

- Lecture 1.
*Introduction and overview:*Goals, history, intelligent agents. Ch. 1, 2. - Lecture 2.
*Problem solving*: Examples (n-queen, 8-puzzle, The road map problem, traveling salesman), State-spaces, search graphs, AND-OR graphs, problem spaces, problem types. Ch. 3 . - Lecture 3.
*Uninformed search*: greedy search, breadth-first, depth-first, iterative deepening, bidirectional search. Ch. 3. - Lecture 4.
*Uninformed search:*continued. quiz 1. Ch. 3. - Lecture 5.
*Informed Heuristic search*: Best-First, A*, Properties of A*. Branch and bound. Ch. 4. - Lecture 6.
*Informed Heuristic search*: Branch and bound, IDA*, Inventing heuristics automatically. Ch. 4. - Lecture 7.
*Game playing*: Minimax search, Alpha-Beta pruning. Ch. 5. - Lecture 8.
*Constraint networks*: The Constraint Satisfaction problem formulation, constraint-graphs, Consistency algorithms. Class-notes. - Lecture 9.
*Search in CSPs*: Backtracking, forward-checking, processing special classes (trees). Class-notes.
> - Lecture 10.
*Iterative improvement*: Hill climbing, stochastic search. Ch. 4.4. - Lecture 11.
*Representation and Reasoning:*Propositional logic, inference, resolution, satisfiability. Ch. 6. - Lecture 12.
*Predicate logic*: Syntax, Quantifiers, variables. Ch. 7. - Lecture 13.
*Inference in logic*: Forward and backward inference, unification. Ch. 9. - Lecture 14.
*Inference- continued*: Ch. 9. - Lecture 15.
*Review: application to planning:*The block world, full vs partial order planning, STRIP. - Lecture 16
*Representation and reasoning under uncertainty*: Ch. 14. - Lecture 17.
*Belief networks*: Ch. 15. - Lecture 18.
*Learning from observations*: Learning decision trees. Ch. 18. - Lecture 19.
*Learning in Neural Networks*: The perceptron. Ch. 19. - Lecture 20.
*Summary and Overview*

A list of Web resources about AI .