 |
Graph Searching
Search is an important part of AI; many problems can be cast as the problem of
finding a path in a graph. This graph-searching tool is designed to help you
learn about different search strategies.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Consistency Based CSP Solver
Constraint satisfaction problems (CSPs) are pervasive in AI problems. A
constraint satisfaction problem is the problem of assigning values to variables
that satisfy some constraints. This tool lets you investigate arc consistency
and domain splitting with backtracking as ways to solve these problems.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Stochastic Local Search Based CSP Solver
This tool is designed to help you learn another strategy for solving CSPs.
This tool demonstrates stochastic local search (various mixes of hill
climbing and random moves) that walks through the space of total assignments
trying to find an assignment with minimal error.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Definite Clause Deduction
Every representation and reasoning system needs a proof procedure in order to
be complete. The purpose of this tool is to illustrate how the process of
answer extraction within a knowledge base can be cast as a search problem. The
deduction tool uses a language similar to Prolog and demonstrates its goal
solving procedures.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Belief and Decision Networks
Belief networks (also called Bayesian networks or causal networks) are a
representation for independence amongst random variables for probabilistic
reasoning under uncertainty. The purpose of this tool is to illustrate how
probabilities are updated given new evidence in a belief network, and shows the
details of how the variable elimination algorithm works.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Decision Trees
Learning is the ability to improve one's behaviour based on experience and
represents an important element of computational intelligence. Decision trees
are a simple yet successful technique for supervised classification learning.
This tool demonstrates how to build a decision tree using a training data set
and then use the tree to classify unseen examples in a test data set.
[ Help ]
[ Bugs & Enhancements ]
|
 |
Neural Networks
Inspired by neurons and their connections in the brain, neural networks are a
representation used in machine learning. After running the back-propagation
learning algorithm on a given set of examples, the neural network can be used
to predict outcomes for any set of input values.
[ Help ]
[ Bugs & Enhancements ]
|
 |
STRIPS To CSP conversion
A STRIPS problem is a planning problem that views the world as a set of variables and
actions upon them. A problem is solved when a series of actions can be found that will
lead to a Goal assignment of values to variables from a Start assignment. The STRIPS
to CSP converter allows for the graphical creation of a STRIPS problem and then the
conversion of said problem into a CSP for solving.
[ Help ]
[ Bugs & Enhancements ]
|
|
