Artificial Intelligence (AI) can be defined as the branch of computer science that is concerned with the automation of intelligent behavior. Intelligent behavior encompasses a wide range of abilities, and as a result AI has become a very broad field that includes game playing, automated reasoning, natural language processing, modeling human performance, robotics, and machine learning. This course will explore a subset of these topics. In the first half of the semester we will focus on how to formulate problems in terms of search. We will create programs that use search to solve puzzles and to play games against an opponent. In the second half of the semester we will focus on machine learning. We will create programs using genetic algorithms, artificial neural networks, and reinforcement learning that automatically improve with experience. We will also discuss robotics and create programs to control simulated robots to navigate a series of increasingly more challenging mazes to find a goal. Throughout the course we will explore how AI relates to the interdisciplinary field of Cognitive Science.
There is no textbook for this course. We will be reading excerpts from several textbooks as well as articles and chapters from the primary literature. All readings are available as links off the schedule below. To prepare for class do the readings for the week prior to the first class meeting of the week.
5% | Class Participation |
45% | Labs |
25% | Midterm Exam Oct. 6, in class |
25% | Final Exam Dec. 6, in class |
If you believe that you need accommodations for a disability, please contact Leslie Hempling in the Office of Student Disability Services, located in Parrish 130, or e-mail lhempli1 to set up an appointment to discuss your needs and the process for requesting accommodations. Leslie Hempling is responsible for reviewing and approving disability-related accommodation requests and, as appropriate, she will issue students with documented disabilities an Accommodation Authorization Letter. Since accommodations may require early planning and are not retroactive, please contact her as soon as possible.
Labs will be assigned on Fridays, during the scheduled lab time, and will be due the following Thursday by midnight. You will turn in your labs using the handin63 program.
Late labs will only be accepted if you contact me at least a day before the deadline with a legitimate reason for needing extra time (such as an illness or needing to leave campus).
Even if you do not fully complete an assignment, you should submit what you have done to receive partial credit.
WEEK | DAY | ANNOUNCEMENTS | TOPIC & READING | LABS |
1 | Aug 30 | Foundations of AI
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None | |
Sep 01 | ||||
2 | Sep 06 | Search
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1: State space search | |
Sep 08 | Drop/Add ends (Sep 09) | |||
3 | Sep 13 | Informed search
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2: A* search | |
Sep 15 | ||||
4 | Sep 20 | Game playing
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3: Minimax search | |
Sep 22 | ||||
5 | Sep 27 | Local and parallel search
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Game tournament | |
Sep 29 | ||||
6 | Oct 04 | Review | None | |
Oct 06 |
Midterm Exam |
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Oct 11 |
Fall Break |
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Oct 13 |
||||
7 | Oct 18 | Machine learning
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None | |
Oct 20 | ||||
8 | Oct 25 | Evolutionary search
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4: Genetic algorithm | |
Oct 27 | ||||
9 | Nov 01 | Neural networks
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5: Classification | |
Nov 03 | Last day to declare CR/NC or W (Nov 04) | |||
10 | Nov 08 | Reinforcement learning
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6: Learning Tic-Tac-Toe | |
Nov 10 | ||||
11 | Nov 15 | Embodiment and Robotics
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7: Subsumption | |
Nov 17 | ||||
12 | Nov 22 | Natural intelligence vs Artificial intelligence
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None | |
Nov 24 |
Thanksgiving Break |
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13 | Nov 29 | Successes of AI
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Subsumption Challenge Results | |
Dec 01 | ||||
14 | Dec 06 |
Final Exam |