Computer Science 270:
Artificial Intelligence

Fall 2010

Tuesday/Thursday 8:30 - 9:55 pm
Searles 126

This course provides an introduction to artificial intelligence using the intelligent agent paradigm. An intelligent agent is a software system that can interact with an external environment by perceiving that environment and taking actions to change the environment based on the sequences of percepts received. We will explore the principles and techniques involved in creating intelligent agents that act optimally in complex environments given limited information and computational resources. Topics include search techniques (e.g. heuristic search and local search), knowledge representation and reasoning (e.g. propositional logic, first-order logic, and reasoning under uncertainty), and learning techniques (e.g. decision-tree learning, neural networks, and reinforcement learning).


ADMINISTRATIVE INFORMATION:
Instructor: Stephen Majercik, 222 Searles Hall, 725-3106, smajerci@bowdoin.edu
Office Hours: Monday, 1:30-2:30 pm; Tues, 4:00-5:00 pm; or by appointment; or just stop by!
Class E-mail: csci270@bowdoin.edu


TEXT (required):
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd edition, Prentice Hall, 2010.


COURSE PREREQUISITES:
CS 210 and Math 200

COURSE REQUIREMENTS (percentages are approximate):
6 Programming Assignments 50%
3 Take-Home Exams 50%
Class Participation Tiebreaker










ACADEMIC HONESTY:
You are expected to follow Bowdoin's Computer Use Policy and its Academic Honor Code. All materials you submit must be original and may not be derived from other sources.* You are encouraged to discuss ideas and techniques broadly with other class members, but not specifics of assignments except as part of group projects. Discussions should be limited to questions that can be asked and answered without using any written medium (e.g. pencil and paper or email). If you are looking at someone else's solution you are cheating. If someone simply tells you how they solved the problem that is also cheating. At no time should you read any code written by another student unless you are in the same group and will be turning in the assignment as a group. Sharing of code or intermediate designs is prohibited. Remember that providing help beyond what is allowed here is as much of an infraction as receiving help. Once you have finished the course sharing your work with other students is also a violation.

Any violation of this policy is grounds for me to initiate an action that would come before the Judicial Board. If you have any questions about this policy, PLEASE do not hesitate to ask me. This is a zero-tolerance policy.

*You may use software and materials available from other sources (understanding that you get no credit for this work) as long as: 1) you acknowledge explicitly which aspects of your assignment were taken from other sources and what those sources are, 2) the materials are freely and legally available, and 3) the material was not previously created by a Bowdoin student.

































TENTATIVE COURSE OUTLINE:
Dates Topic Readings
Sep 2 Artificial Intelligence and Intelligent Agents Chs 1, 2
Sep 7, 9 Naive Search Ch 3
Sep 14, 16 Informed Search Ch 3
Sep 21, 23, 28 Constraint Satisfaction Ch 6
Sep 30, Oct 5 Adversarial Search -- Games Ch 5
Oct 7 Local Search Ch 4
Oct 14 Simulated Annealing Ch 4
Oct 15-17 Take-Home Exam 1 Chs 1-3, 5-6
Oct 19, 21, 26, 28 Nature-Inspired Algorithms Ch 4 and Handouts
Nov 2, 4 Propositional Logic Ch 7
Nov 9, 11 Rule-Based Systems and Decision Trees Ch 18
Nov 16, 18 Neural Networks Ch 18
Nov 19-21 Take-Home Exam 2 Chs 4,7, Handouts
Nov 23, 30, Dec 2 Reinforcement Learning Ch 21
Dec 7, 9 Artificial Intelligence Revisited Handouts
Exam Period Take-Home Exam 3 Chs 18, 21















 
TENTATIVE ASSIGNMENT/EXAM SCHEDULE:
Dates Topic
Sep 9 OUT: A1, Part 1 (Naive Search)
Sep 16 OUT: A1, Part 2 (Informed Search)
Sep 23 DUE: A1
OUT: A2 (Constraint Satisfaction)
Sep 30 GRADED: A1
DUE: A2
Oct 14 GRADED: A2
Oct 15-17 Take-Home Exam 1 (up to and including Adversarial Search)
Oct 21 OUT: A3 (Nature-Inspired Algorithms)
Nov 4 DUE: A3
OUT: A4 (Propositional Logic)
Nov 11 GRADED: A3
DUE: A4
Nov 18 GRADED: A4
OUT: A5 (Neural Networks)
Nov 19-21 Take-Home Exam 2 (Local Search through Propositional Logic)
Nov 30 DUE: A5
OUT: A6 (Reinforcement Learning)
Dec 7 GRADED: A5
DUE: A6
Dec 14 GRADED: A6
Exam Period Take-Home Exam 3 (Rule-Based Systems and Decision Trees through Reinforcement Learning)