Demonstrations
From 6.034 Wiki
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- | Much of the material in 6.034 is reinforced by on-line demonstrations develop by us or | + | Much of the material in 6.034 is reinforced by on-line artificial-intelligence demonstrations develop by us or |
- | otherwise available on the web. | + | otherwise available on the web. Those demonstrations developed by us are provided via the easy-to-use |
- | Java Web Start mechanism. | + | Java Web Start mechanism, which comes with the Java Runtime Environment, the so-called JRE. |
- | So, if you don't have | + | The demonstrations illustrate the following ideas: |
- | can | + | |
- | demonstrations. | + | * Blocks world manipulation (after Winograd) |
+ | * Search: depth-first, breadth-first, hill-climbing, beam, branch and bound, A* | ||
+ | * Games: mini-max, alpha-beta | ||
+ | * Genetic algorithms: crossover, mutation, fitness | ||
+ | * Constraint satisfaction: drawing analysis (after Waltz, using Huffman labels) | ||
+ | * Domain reduction: map coloring, resource allocation | ||
+ | * Biological mimetics: genetic algorithms, self-organizing maps, cross-modal clustering | ||
+ | * Learning: nearest neighbors, support vector machines, lattice learning, boosting | ||
+ | |||
+ | So, if you don't have the Java Runtime Enviornment installed, | ||
+ | you should [http://java.sun.com/products/javawebstart/ install it first]. Then, you | ||
+ | can run the [http://courses.csail.mit.edu/6.034f/demonstrate/demonstrate.jnlp demonstrations]. |
Revision as of 15:03, 22 August 2010
Much of the material in 6.034 is reinforced by on-line artificial-intelligence demonstrations develop by us or otherwise available on the web. Those demonstrations developed by us are provided via the easy-to-use Java Web Start mechanism, which comes with the Java Runtime Environment, the so-called JRE.
The demonstrations illustrate the following ideas:
- Blocks world manipulation (after Winograd)
- Search: depth-first, breadth-first, hill-climbing, beam, branch and bound, A*
- Games: mini-max, alpha-beta
- Genetic algorithms: crossover, mutation, fitness
- Constraint satisfaction: drawing analysis (after Waltz, using Huffman labels)
- Domain reduction: map coloring, resource allocation
- Biological mimetics: genetic algorithms, self-organizing maps, cross-modal clustering
- Learning: nearest neighbors, support vector machines, lattice learning, boosting
So, if you don't have the Java Runtime Enviornment installed, you should install it first. Then, you can run the demonstrations.