Artificial Intelligence Books

Reeves and Rowe provide a very readable overview of what is currently known about evolutionary algorithms. While the book provides very good coverage of several recent models of GA dynamics, it would be worth the money if only for the chapter on the dynamical systems treatment of GAs.

Vose's "The Simple Genetic Algorithm" is a brilliant piece of work, but it also holds the distinction of being the earliest in a book that I have ever become utterly, completely, hopelessly lost. I refer to this book as "the one I use to figure out what Vose was talking about."

This was both a good introduction to genetic algorithms in general and to their adaptation to grouping problems in particular.

More than that the style is concise, engaging and consistently interesting. The explanations are practical and intuitively appealing.

I found myself jumping around in the book somewhat (I would recommend reading chapter 14 before chapter 1), but everything was explained somewhere. At the end, I felt I had a good basis not just from which to implement a grouping GA, but to consider many other variations or developments from the classic model.

The key strength of this book for me was its practical, common-sense approach that stresses understanding the problem and if it works use it, if not adapt it so it does.

Rather the spirit of GAs themselves.

Genetic Programming helps computers to write their own software, make discoveries, and no doubt will help robots interact with humans in some not too distant future. This book is more interesting than most because each chapter discusses GP from a different perspective. Yet, chapters seem inter-related. By reading the whole book I have gotten more than the sum of all of the individual chapters. It is definitely a good source of information about the state of the art in GP.

Part I Advances in Geometric Algebra


Chapter 1
Old Wine in New Bottles: A New Algebraic Framework for Computational Geometry,
David Hestenes
1.1 Introduction
1.2 Minkowski Algebra
1.3 Conformal Split
1.4 Models of Euclidean Space
1.5 Lines and Planes
1.6 Spheres and Hyperplanes
1.7 Conformal and Euclidean Groups
1.8 Screw Mechanics
1.9 Conclusions

Chapter 2
Universal Geometric Algebra
Garret Sobczyk
2.1 Introduction
2.2 The Universal Geometric Algebra
2.3 Matrices of Geometric Numbers
2.4 Linear Transformations
2.5 Pseudo-Euclidean Geometries
2.6 Affine and Projective Geometries
2.7 Conformal Transformations

Chapter 3
Realizations of the Conformal Group
Jose Maria Pozo and Garret Sobczyk
3.1 Introduction
3.2 Projective Geometry
3.3 The Conformal Representant and Stereographic Projection
3.4 Conformal Transformations and Isometries
3.5 Isometries in No
3.6 Compactification
3.7 Mobius Transformations

Chapter 4
Hyperbolic Geometry
Hongbo Li
4.1 Introduction
4.2 Hyperbolic Plane Geometry with Clifford Algebra
4.3 Hyperbolic Conformal Geometry with Clifford Algebra
4.4 A Universal Model for the Conformal Geometries of the Euclidean, Spherica and Double-Hyperbolic Spaces
4.5 Conclusion

Part II Theorem Proving

Chapter 5
Geometric Reasoning With Geometric Algebra
Dongming Wang
5.1 Introduction
5.2 Clifford Algebra for Euclidean Geometry
5.4 Proving Identities in Clifford Algebra

Chapter 6
Automated Theorem Proving
Hongbo Li
6.1 Introduction
6.2 A general Framework for Clifford algebra and Wu's Method
6.3 Automated Theorem Proving in Euclidean Geometry and Other
Classical Geometries
6.4 Automated Theorem Proving in Differential Geometry}{116}
6.5 Conclusion

Part III Computer Vision

Chapter 7
The Geometry Algebra of Computer Vision
Eduardo Bayro Corrochano and Joan Lasenby
7.1 Introduction
7.2 The Geometric Algebras of 3-D and 4-D Spaces
7.3 The Algebra of Incidence
7.4 Algebra in Projective Space
7.5 Visual Geometry of $n$ Uncalibrated Camera
7.6 Conclusions

Chapter 8
Using Geometric Algebra for Optical Motion Capture
Joan Lasenby and Adam Stevenson
8.1 Introduction
8.2 External and Internal Calibration
8.3 Estimating the External Parameters
8.4 Examples and Results
8.5 Extending to Include Internal Calibration
8.6 Conclusions

Chapter 9
Bayesian Inference and Geometric Algebra: An Application to Camera Localization
Chris Doran
9.1 Introduction
9.2 Geometric Algebra in Three Dimensions
9.3 Rotors and Rotations
9.4 Rotor Calculus
9.5 Computer Vision
9.6 Unknown range data
9.7 Extension to three cameras
9.8 Conclusions

Chapter 10
Projective Reconstruction of Shape and Motion Using Invariant Theory
Eduardo Bayro Corrochano and Vladimir Banarer
10.1 Introduction
10.2 3-D Projective Invariants from Multiple Views
10.3 Projective Depth
10.4 Shape and Motion
10.5 Conclusions

Part IV Robotics

Chapter 11
Robot Kinematics and Flags

11.1 Introduction
11.2 The Clifford Algebra
11.3 Flags
11.4 Robots
11.5 Concluding Remarks

Chapter 12
The Clifford Algebra and the Optimization of Robot Design
Shawn G. Ahlers and John Michael McCarthy
12.1 Introduction
12.2 Literature Review
12.3 Overview of the Design Algorithm
12.4 Double Quaternions
12.5 The Task Trajectory
12.6 The Design of the TS Robot
12.7 The Optimum TS Robot
12.8 Conclusion

Chapter 13
Eduardo Bayro Corrochano and Garret Sobczyk
13.1 Introduction
13.2 The General Linear Group
13.3 Algebra of Incidence
13.4 Rigid Motion in the Affine Plane
13.5 Application to Robotics
13.6 Application II:
The design of an image filter
Recognition of hand gestures
The meet filter
13.7 Conclusion

Part V Quantum and Neural Computing, and Wavelets

Chapter 14
Geometric Algebra in Quantum Information Processing
by Nuclear Magnetic Resonance
Timothy F. Havel, David G. Cory, Shyamal S. Somaroo, and Ching-Hua Tseng
14.1 Introduction
14.2 Multiparticle Geometric Algebra
14.3 Algorithms for Quantum Computers
14.4 NMR and the Product Operator Formalism
14.5 Quantum Computing by Liquid-State NMR
14.6 States and Gates by NMR
14.7 Quantum Simulation by NMR
14.8 Remarks on Foundational Issues

Chapter 15
Geometric Feedforward Neural Networks and Support Multivector Machines
Eduardo Bayro Corrochano and Refugio Vallejo

15.1 Introduction
15.2 Real Valued Neural Networks
15.3 Complex MLP and Quaternionic MLP
15.4 Geometric Algebra Neural Networks
15.5 Learning Rule
15.6 Experiments Using Geometric Feedforward Neural Networks
15.7 Support Vector Machines in Geometric Algebra
15.8 Experimental Analysis of Support Multivector Machines
15.9 Conclusions

Chapter 16
Image Analysis Using Quaternion Wavelets
Leonardo Traversoni
16.1 Introduction
16.2 The Static Approach
16.3 Clifford Multiresolution Analyses
16.4 Haar Quaternionic Wavelets
16.5 A Dynamic Interpretation
16.6 Global Interpolation
16.7 Dealing with Trajectories
16.8 Conclusions

Part VI Applications to Engineering and Physics

Chapte 17
Objects in Contact: Boundary Collisions as Geometric Wave Propagation
Leo Dorst
17.1 Introduction
17.2 Boundary Geometry
17.3 The Boundary as a Geometric Object
17.4 Wave Propagation of Boundaries
17.5 Conclusions

Chapter 18 Modern Geometric Calculations in Crystallography
G. Aragon, J.L. Aragon, F. Davila, A. Gomez and M.A. Rodriguez
18.1 Introduction
18.2 Quasicrystals
18.3 The Morphology of Icosahedral Quasicrystals
18.4 Coincidence Site Lattice Theory
18.5 Conclusions

Chapter 19 Quaternion Optimization Problems in Engineering
Ljudmila Meister
19.1 Introduction
19.2 Properties of Quaternions
19.3 Extremal Problems for Quaternions
19.4 Determination of Rotations
19.5 The Main Problem of Orientation
19.6 Optimal Filtering and Prediction
19.7 Summary

Chapter 20
Clifford Algebras in Electrical Engineering
William Baylis
20.1 Introduction
20.2 Structure of Cl_3
20.3 Paravector Model of Spacetime
20.4 Using Relativity at Low Speeds
20.5 Relativity at High Speeds
20.6 Conclusions

Chapter 21
Applications of Geometric Algebra in Physics and Links With Engineering
Anthony Lasenby and Joan Lasenby

21.1 Introduction
21.2 The Spacetime Algebra
21.3 Quantum Mechanics
21.4 Gravity as a Gauge Theory
21.5 A New Representation of 6-d Conformal Space
21.6 Summary and Conclusions

Part VII Computational Methods in Clifford Algebras

Chapter 22
Clifford Algebras as Projections of Group Algebras
Vladimir M. Chernov
22.1 Introduction
22.2 Group Algebras and Their Projection
22.3 Applications
22.4 Conclusion

Chapter 23
Counterexamples for Validation and Discovering of New Theorems
Pertti Lounesto
23.1 Introduction
23.2 The Role of Counterexamples in Mathematics
23.3 Clifford Algebras: An Outline
23.4 Preliminary Counterexamples in Clifford Algebras
23.5 Counterexamples About Spin Groups
23.6 Counterexamples on the Internet


Chapter 24
The Making of GABLE: A Geometric Algebra Learning Environment in Matlab
Stephen Mann, Leo Dorst, and Tim Bouma
24.1 Introduction
24.2 Representation of Geometric Algebra
24.3 Inverses
24.4 Meet and Join
24.5 Graphics
24.6 Example: Pappus's Theorem
24.7 Conclusions

Chapter 25
Helmstetter Formula and Rigid Motions with CLIFFORD
Rafal Ablamowicz
25.1 Introduction
25.2 Verification of the Helmstetter Formula
25.3 Rigid Motions with Clifford Algebras
25.4 Summary



References



Index

This is a beautiful and unique book. The style is lively and the explanations are remarkably clear. The author makes a significant effort to demistify concepts before defining them rigorously and proving facts about them. It is impressive to see the variety of the topics covered in the book. For example, there is a nice and easy introduction to Lie groups and Lie algebras, and an exquisite treatment of the elementary differential geometry of curves and surfaces. As the author states, this material is a write-up of lectures given by the famous geometer Eugene Calabi (of the Calabi-Yau manifolds!), and this is quite a treat. Gallier's presentation definitely rivals do Carmo, one of the best. It is also refreshing and illuminating to see topics such as QR-decomposition and decomposition in terms of Householder matrices, treated from a geometric point of view. For that matter, the treatment of polar forms and SVD is superior, although perhaps a bit abstract for my taste. There are lots of problems, including programming projects. At first glance, the problems are on the hard side. This could turn off some students. In conclusion, this is a great book. Even though it is very well written, this is not an "easy book". However, perseverant readers will find its reading very rewarding. It is a great preparation for more advanced books on Lie groups, differential geometry, and projective and algebraic geometry. The sections on applications are very nice, but there should be more and they should be more extensive. Oh, I was forgetting, the internet supplement is great. For example, there is a wonderful treatment of rational curves and surfaces. Every geometry lover should have this book!

The name Thomas J. Sanders must be included in any list of the best Science Fiction writers of our day.

His newest offering is God's Fire, following closely on the heels of The God Chip Conspiracy, his brilliant debut novel of last year. God's Fire's cast of zany but brilliant characters are actually the ancestors of the heroes of The God Chip Conspiracy, and this story is set in the same universe, but 120 years earlier. No matter though, since time in a Sanders novel sometimes turns around and meets itself coming, and both books can be read as complete stories, and in the order you choose.

In God's Fire, we learn more about the development of the God Chip and its first recipients: Joy, a high-tech love doll and CHESTER, the Complete Human Environment Simulation for Tacticel Emergency Response. Our hero, Galileo Newton Goddard, destined from the moment his name was typed on a birth certificate to become the world's greatest scientist, is the creator of the God Chip. Caught in a squeeze play between the forces or irrationality and love, he confronts the ultimate technological dilemna: Can a man find true love and happiness with a machine?

Sander's penchant for mixing complex scientific thought, philosophical allusions and illusions, with a folksy way of storytelling and an almost bizarre but impressive imagination, make every page of God's Fire a work of science fiction art. You'll be guessing all the way through where it will go next.

If you could merge, or clone perhaps, Asimov and Vonnegut, and sprinkle in some cellular matter from Heinlein, you could aptly name the offspring of that mix Thomas J. Sanders.

I enthusiastically recommend God's Fire to all readers, and especially to science fiction lovers.

This book is a collection of papers that were presented at the 1998 International Colloquium on Grammatical Inference. Grammatical inference is a subfield of machine learning, inductive inference, and pattern classification. Applications of grammatical inference include speech recognition and speech production, computational molecular biology, information extraction, and music composition, among others. The articles included in the book are representative of some of the best current work in grammatical inference. I enthusiastically recommend the book to anyone interested in learning about the current advances in this subfield of machine learning.

A belief function is a step beyond traditional probability distribution functions. The latter describe uncertainty. But, by definition, you somehow know precisely that a given pdf is a correct description of a process. A belief function tries to express the imprecision in knowledge about a pdf.

The book is an advanced treatment of how graphical methods can be used to aid in the construction of belief models. Be aware though that the book has complicated ideas drawn from set theory and graph theory. With rigourous derivations of theorems.

Usages are also stressed. Examples are given of how to model failure rates in systems of many parts. Where a traditional probabilistic analysis may be far too difficult or labourious.

This book is an edited collection of chapters written by knowledgeable researchers in the field of evolutionary computation. The collection is very well organized, and it covers in adequate depth many topics of evolutionary algorithms. This is an expensive book, but it is worth the price if you are really interested in a good reference book of this area. I recommend it highly.

This book has an excellent coverage of all topics of HCI and Usability for Medicine and Healthcare. Each chapter illustrates the related work in details. Each chapter also illustates different methodologies and implementation details. It also discusses the future directions. It will be very helpful for any people in HCI and Usability for Medicine and Healthcare. It is a must book for researchers, practitioners and software developers in medicine and health care.