Ebook: Challenges for Computational Intelligence

In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set -- identify the most important problems and focus on them
-- is still actual.

Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.

The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.

---

In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set -- identify the most important problems and focus on them
-- is still actual.

Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.

The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.

---

In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set -- identify the most important problems and focus on them
-- is still actual.

Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.

The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.

Content:
Front Matter....Pages I-XII
What Is Computational Intelligence and Where Is It Going?....Pages 1-13
New Millennium AI and the Convergence of History....Pages 15-35
The Challenges of Building Computational Cognitive Architectures....Pages 37-60
Programming a Parallel Computer: The Ersatz Brain Project....Pages 61-98
The Human Brain as a Hierarchical Intelligent Control System....Pages 99-122
Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism....Pages 123-143
Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour....Pages 145-161
Computational Scene Analysis....Pages 163-191
Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities....Pages 193-219
The Science of Pattern Recognition. Achievements and Perspectives....Pages 221-259
Towards Comprehensive Foundations of Computational Intelligence....Pages 261-316
Knowledge-Based Clustering in Computational Intelligence....Pages 317-341
Generalization in Learning from Examples....Pages 343-363
A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective ....Pages 365-406
Computational Intelligence in Mind Games....Pages 407-442
Computer Go: A Grand Challenge to AI....Pages 443-465
Noisy Chaotic Neural Networks for Combinatorial Optimization....Pages 467-487

---

In the year 1900 at the International Congress of Mathematicians in Paris David Hilbert delivered what is now considered the most important talk ever given in the history of mathematics, proposing 23 major problems worth working at in the future. One hundred years later the impact of this talk is still strong: some problems have been solved, new problems have been added, but the direction once set -- identify the most important problems and focus on them
-- is still actual.

Computational Intelligence (CI) is used as a name to cover many existing branches of science, with artificial neural networks, fuzzy systems and evolutionary computation forming its core. In recent years CI has been extended by adding many other subdisciplines and it became quite obvious that this new field also requires a series of challenging problems that will give it a sense of direction. Without setting up clear goals and yardsticks to measure progress on the way many research efforts are wasted.

The book written by top experts in CI provides such clear directions and the much-needed focus on the most important and challenging research issues, showing a roadmap how to achieve ambitious goals.

Content:
Front Matter....Pages I-XII
What Is Computational Intelligence and Where Is It Going?....Pages 1-13
New Millennium AI and the Convergence of History....Pages 15-35
The Challenges of Building Computational Cognitive Architectures....Pages 37-60
Programming a Parallel Computer: The Ersatz Brain Project....Pages 61-98
The Human Brain as a Hierarchical Intelligent Control System....Pages 99-122
Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism....Pages 123-143
Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour....Pages 145-161
Computational Scene Analysis....Pages 163-191
Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities....Pages 193-219
The Science of Pattern Recognition. Achievements and Perspectives....Pages 221-259
Towards Comprehensive Foundations of Computational Intelligence....Pages 261-316
Knowledge-Based Clustering in Computational Intelligence....Pages 317-341
Generalization in Learning from Examples....Pages 343-363
A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective ....Pages 365-406
Computational Intelligence in Mind Games....Pages 407-442
Computer Go: A Grand Challenge to AI....Pages 443-465
Noisy Chaotic Neural Networks for Combinatorial Optimization....Pages 467-487
....