ADVANCED
TOPICS IN ARTIFICIAL INTELLIGENCE
Intelligent agents (knowbots, softbots, and
robots) are software or hardware entities that perform a set of tasks
on behalf of a user with some degree of autonomy. They find
applications in a variety of domains including: Internet-based
information systems, adaptive (customizable) software systems,
autonomous mobile and immobile robots, data mining and knowledge
discovery, smart systems (smart homes, smart automobiles, etc.),
decision support systems, and intelligent design and manufacturing
systems. Current research on intelligent agents and multi-agent
systems builds on developments in several areas of computer science
including: artificial intelligence (especially agent architectures,
machine learning, planning, distributed problem-solving), information
retrieval, database and knowledge-base systems, and distributed
computing.
COMPUTATIONAL
MODELS OF LEARNING
Computational Models of Learning -- ComS 672 -- is
a 3-credit, graduate course offered by Professor Vasant Honavar in the
Department of Computer Science at Iowa State University in alternate
spring semesters.
EVONET
IN SPAIN
THE ON-LINE TUTORIAL ON EVOLUTIONARY COMPUTATION
ELEMENTS
OF NEURAL COMPUTATION
Elements of Neural Computation - ComS 474 - is a
3-credit, senior undergraduate elective course offered by the
Department of Computer Science at Iowa State University every fall.
EVOLUTION
STRATEGIES
Bionique and Evolution Technique; TU Berlin
GENETIC
ALGORITHM QUESTIONS AND ANSWERS FAQ FROM KYOTO DAIGAKU
FAQ
GENETIC
ALGORITHMS ; AN INTRODUCTION
INDEX
OF MACHINE LEARNING COURSES
INTRODUCTION
TO GENETIC ALGORITHMS FROM MIT
This is an introduction to genetic algorithm
methods for optimization. Genetic algorithms were formally introduced
in the United States in the 1970s by John Holland at University of
Michigan. The continuing price/performance improvements of
computational systems has made them attractive for some types of
optimization. In particular, genetic algorithms work very well on
mixed (continuous and discrete), combinatorial problems. They are less
susceptible to getting 'stuck' at local optima than gradient search
methods. But they tend to be computationally expensive.
PRINCIPLES
OF ARTIFICIAL INTELLIGENCE
Principles of Artificial Intellignece - ComS 472
(572 DL) - is a 3-credit, senior undergraduate elective / introductory
graduate course on Artificicial Intelligence offered by the Department
of Computer Science at Iowa State University every fall.
WELCOME
TO THE GENETIC ALGORITHMS MODULE
The Genetic Algorithms Module was created for the
purpose of giving beginning students a brief introduction to genetic
algorithms. It is divided into five sections: Introduction, Advanced
Topics, Research, Applications, and Resources.
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