AI Demo
로봇 앨리스와 채팅하러가기 (하단에 채팅창 있음) : 리처드 월리스 : 인터넷상에서 대화하는 프로그램으로 상대 네티즌이 사람으로 착각할 만큼 발달했다. 앨리스는 4만여가지의 답변을 통해 일상대화의 95%까지 소화할 수 있게 됐다. 리처드 월리스는 2000년 앨리스로 AI 개발자들에게는 가장 권위 있는 뢰브너상에 도전해 1등을 차지했다.
The Age of Intelligent Machines : The Film : download : Raymond Kurzweil
IDA*search 를 이용한 8 / 15 puzzle
Tools for learning : British Columbia : online resources for Computational Intelligence
Every representation and reasoning system needs a proof procedure in order to be complete. The purpose of this applet is to illustrate how the process of answer extraction within a knowledge base can be cast as a search problem. The deduction applet uses a language similar to Prolog and demonstrates its goal solving procedures.
Graph Searching
Search is an important part of CI;
many problems can be cast as the problem of finding a path in a graph. This
graph-searching applet is designed to help you learn about different search
strategies.
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 applet lets you investigate arc consistency and
domain splitting with backtracking as ways to solve these problems.
Stochastic Local Search for CSPs
This applet is
designed to help you learn another strategy for solving CSPs. This applet
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.
Planning
Planning is essential for agents that
act in an environment. To solve a goal intelligently, an agent needs to think
about what it will do now and in the future. This applet demonstrates planning
using the blockworld problem domain and STRIPS representation.
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 applet 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.
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 applet 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.
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.
Robot Control
A robot is an intelligent agent that
perceives, reasons, and acts in time in an environment. It acts to achieve its
assigned goals and at the same time avoids getting into undesired states. The
robot applet provides a simulation of a robot perceiving and acting under the
control of a set of customizable robot controller functions.