Artificial Intelligence: A Modern Approach

Online Demos (Applets) of Artificial Intelligence

This page lists some demos of various artificial intelligence techniques that you can run in your browser. These can help explain how AI algorithms work. Your next step after running these demos would be to download, run, and modify some source code, such as the AIMA online code repository.

Demos by Site

Here are some of the best sites for demos; let peter@norvig.com know of others.

UBC: University of British Columbia's collection of applets that accompany the book Computational Intelligence. A big collection of high-quality applets, but they can be confusing, because they have so many options. However, they all share a similar user interface, so learning one applet will help you understand the others.
MIT: applets for MIT's online course. A good collection of simple applets: they have few controls, but they each do one thing well.
UTA: Univ. of Texas at Austin professor Diane Cook's collection. Somewhat of a work in progress, you need to experiment to see which ones work and which don't.
JARS: A Java Archive of all kinds of applets; this is a search for AI applets, applications, and frameworks.

Demos by Topic (Chapter)

In the following table, demos are organized by chapter in AIMA. For each chapter we list the demos that we know of. When there are several, we make a rough attempt to have the best ones listed first, but this is subjective, and may depend on the particular browser you are using.

Chap Topic Demos
3-4 Search MIT / UBC
3 Search trees RMIT
3 A* Search 8/16-puzzle / 8-puzzle
4 Genetic Algorithms TSP / Plant Eaters
5 CSPs (Map coloring) MIT / UBC
6 Minimax and Alpha-Beta MIT
6 Checkers Chinook
6 Othello Ajax / Cyrano / Chirico
9 Definite Clause Deduction UBC
11 Strips Planning UBC
12 Continuous Planning Koenig
14 Belief Networks UBC / JavaBayes
17 Markov Decision Processes UBC
18 Decision Trees Alberta / UBC
20 Neural Networks UBC
20 Support Vector (Kernel) Machines MIT
21 Reinforcement Learning Blackjack
25 Robotic Control UBC
26 Art Aaron

Chap	Topic	Demos
3-4	Search	MIT / UBC
3	Search trees	RMIT
3	A* Search	8/16-puzzle / 8-puzzle
4	Genetic Algorithms	TSP / Plant Eaters
5	CSPs (Map coloring)	MIT / UBC
6	Minimax and Alpha-Beta	MIT
6	Checkers	Chinook
6	Othello	Ajax / Cyrano / Chirico
9	Definite Clause Deduction	UBC
11	Strips Planning	UBC
12	Continuous Planning	Koenig
14	Belief Networks	UBC / JavaBayes
17	Markov Decision Processes	UBC
18	Decision Trees	Alberta / UBC
20	Neural Networks	UBC
20	Support Vector (Kernel) Machines	MIT
21	Reinforcement Learning	Blackjack
25	Robotic Control	UBC
26	Art	Aaron

AI: A Modern Approach by Stuart Russell and Peter Norvig Modified: Sep 21, 2003