Introduction to Artificial Intelligence

(Spring 2013)

**Meets:** Mondays and Wednesdays 3:05-4:25pm in ES&T L1205

**Instructor: **Prof. Mark Riedl (riedl@cc.gatech.edu)

Office hours: Tuesdays: 3:00-5:00pm, TSRB 234

Home | Schedule | Projects | Grading | Resources | Piazza |

- Project 0: Introduction to Python (0% of grade)
**[No due date]** - Project 1: Search (10% of grade)
**[Due Feb. 10, 2013, 11:55pm]**[Submit by Feb. 7, 11:55pm for autograder pass/fail check] - Project 2: Adversarial Search (10% of grade)
**[Due March 3, 2013, 11:55pm]**[Submit by Feb. 28, 11:55pm for autograder pass/fail check] - Project 3: Dynamic Bayesian Networks (10% of grade)
**[Due April 7, 11:55pm]**[Submit by April 4, 11:55pm for autograder pass/fail check] - Project 4: Decision Tree Learning (10% of grade)
**[Due April 28, 11:55pm]**

**Codebases for each project can be downloaded from T-square.**

- Missionaries and cannibals (problem | solution)
- Search (problem | solution)
- Search 2 (problem | solution)
- Rook Jumping Maze Generation. See this for more information on how the puzzles work. As a thought exercise, consider different ways to represent states using the complete-state formulation. Consider different operations for moving from possible puzzle to possible puzzle. Consider different factors that you might incorporate into an evaluation function. For example, you might want a "hard" puzzle or an "easy" puzzle; how can you write a formal definition of "easy" and "hard". Think about different algorithms that might perform well on this problem. Solution: none given: but see evaluation function designs and different possible algorithms. If you are feeling ambition, implement a puzzle generator.
- Expectiminimax (problem | solution)
- Constraint satisfaction (problem | solution)
- Resolution with propositional logic: wumpus world (problem | solution)
- Bayes net inference (problem | solution)
- Bayes net inference 2 (problem | solution)
- Markov decision process (problem | solution)
- MDP Vvalue iteration problem from class (problem | solution)
- Decision tree learning (problem | solution)