CS 3793: Introduction to Artificial Intelligence

link to book's web site

Syllabus and Assignments


Final Review.


Homework 1

Homework 2

Homework 3

Homework 4

Homework 5

Programming Assignments

picknumber.zip is a simple example of how we will program agents and environments in Java.

Lab 1 (initial download lab1.zip)

Lab 2 (initial download lab2.zip)

Lab 3 (initial download lab3.zip)

Lab 4 (initial download lab4.zip)

Lab 5 (initial download lab5.zip) (jar file of players to test against testplayers.jar)

The download lab5.zip is another attempt to avoid some of the problems running Interact.java (called Interact2.java here) on Windows machines. In this attempt, each out.println has been replaced by an out.print and an out.write(10), which should avoid the two-character newline on Windows, but the single linefeed character should still be interpreted as a newline in Java.

The jar file contains three classes: SimplePlayer2, SimplePlayer3 and BylanderPlayer. SimplePlayer2 and SimplePlayer3 perform minimax to depth 2 and 3, respectively; in addition, they search to depth 6 after 20 moves. BylanderPlayer does alpha-beta pruning to depth 6; in addition, it includes opening moves, has a decent evaluation function, and searches to higher depths as the game proceeds.

To use this jar file:

Handouts (subject to change)

Everything below will probably change quite a bit
Iterative Deepening, A* Search, Alpha-Beta Pruning, Propositional Logic, Wumpus Inference, elim-bel Algorithm, Partial Planning Notes, Partial Planning Example, Learning Theory , Perceptron Mistake Bound, General Loss Bounds.

Notes (subject to change)

Agents (slides)

Search (slides), Search Examples (slides)

Features (slides)

Logic (slides)

Probability (slides), Some Puzzling Probability Problems

Learning (slides), More Learning (slides)

Planning (slides)

Making Decisions (slides)

Multiple Agents (slides)

Unsupervised Learning (slides)

The Rest of AI (slides)

Everything below will probably change quite a bit

Heuristic Search, Game Playing.

Example of Bayesian Network Calculation.

Planning, Planning Example, Learning, Numerical Learning Algorithms, Neural Networks, Natural Language, Vision, Some AI Talks.

Other Stuff

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