Artificial Intelligence Books

This is a great book that was a reading in one of the advanced robotic courses I took at graduate school. It contains a lot of stuff that you want to get your hands dirty on especially for those who are mathematically inclined. A great book from an authoritative source in the field-worth your money.

Comprehensive coverage of motion planning techniques.

If you're interested in robot motion planning, this book is it! It provides incomparable detailed coverage of the major methods for motion planning and their theory, and could serve as a great geometrical perspective to planning in general. It's not a casual read, however and is meant for a specialist audience with a liking for theory. No professional roboticist should be without it.

The theme that Kampis examines in Self-Modifying Systems is the self-generation of information by the nontrivial change (self-modification) of systems. Such a system is a network of many components, which have the property of being able to transform each other and organising themselves into larger components. It is this feature that makes such component systems closed to efficient cause. Component-systems, then, are not algorithmic, but this is not a reversible equation in that component-systems can, Kampis argues, give rise, in fact, to any particular algorithm. Kampis describes the difference as that between known complexity, that is to say complexity-to-be-realised, and unknown complexity, or complexity-to-be-explained. The first of these is relatively easy to realise, the second being impossible in that "a complex operation operating on components and bringing forth yet unknown and unidentified components cannot be described as an algorithm" (Kampis 1991:239).
Component-systems, therefore, have a high degree of creativity, but they also have characteristics that avoid many of the problems that other forms of nonlinear models.Kampis argues that nothing that such a process gives rise to can be predicted before hand, and no identity can be traced back to an origin. From this, Kampis states that the creation thesis emerges. This thesis can be stated in the following way:
The organisation of the world is continually self-creating; this process is at any given stage incomplete. Information about the future is not only inaccessible but does not exist in any form. Creation is a basic and general phenomenon that cannot be explained logically. (Kampis 1991: 258).
Self-creation occurs in the form of self-modification. A system that exhibits creativity, then, has to be continually redefined because, in the course of time, all variables and their interrelations will change in so far as each component is replaced by another. It is a system which will be defined (and constructed) by the very processes it undergoes. (Kampis 1991: 490).
The book unfolds, then, as a wonderfully sophisticated model to account for the very process of change and the important limitations of prediction the process of change implies. This book deserves to be one of the key texts of autopoiesis.

George Kampis follows in the ground breaking tradition of Robert Rosen. Examines the notions of reproduction and construction. His scope is wide and through. A must read for anybody interested in Rosen complexity.

As another reviewer notes, this book will be very helpful to those interested in the complexity research of theoretical biologist Robert Rosen. Whereas Rosen has a tight, highly rigorous focus on his goal in "Life Itself", Kampis paints on a somewhat broader canvas, referencing the work of many other researchers (including Rosen). However, Kampis is similarly detailed and methodical.

Kampis first describes the limits of dynamical models, and state-based approaches, including the limitations inherent in the 'canonical formalism' of mechanics.

He then goes on to introduce 'component-systems'. This is a general formal representation of a system as being composed of some number of components out of an essentially unlimited number of possible components. In component systems, the "rules" for the dynamics of the system are not independent of the components themselves. Self-modifying component systems generate new components and delete others, thereby changing the identity of the system itself. In mathematical terms, a self-modifying system is like a function f that belongs to its own domain and range ("f:f-->f"). The result is that such systems are non-algorithmic, nor are their dynamics describable in a state-based formalism (e.g., Newtonian, Hamiltonian, etc.). This has notable consequences for approaches that attempt to treat such systems as algorithmic, or via modelling their state-based dynamics. By comparison to component systems, cellular automata and similar algorithmic formal systems are entirely trivial.

Kampis devotes many chapters to what I have cursorily mentioned, and there is much, much more in this book that is worth reading. Although there is not alot of math, what is there is important to understand. It would be helpful for the interested reader to generally understand the basic notation of mechanics, first-order differential equations, basic logic, Godel's Incompleteness Theorems, Turing machines, basic set theory, system theory, a modicum of philosophy, and linguistics. Most of these aspects are fairly well-explained, so a diligent reader can pick them up as he goes along.

This is not a book of vague handwaving arguments. It will take some studious effort to read and grasp the concepts and profundity of what he presents. However, it will be well worth the effort, and afterward you will never be able to look at dynamical systems and models, complexity, and self-modifying systems, in the same way.

Although there are alot of similarities between Kampis' and Rosen's works, they are sufficiently distinct in approaches and conclusions that both are well worth reading.

One final note: the "typewriter" font used throughout may be a bit surprising to see in the 21st century, but I found it entirely legible and comfortable once I got used to it.

trust me!!
I know the guy who wrote it and i must say the book is something ...

This book really covers a vast literature and new trends of Pattern Recognition and Image Processing area. Are in need of new ideas? / Want to fill your voids in understanding the basic concept and especially if you want to explore the new area like Symbolic data analysis. Yes, this is the right choice please go ahead.

Covers different applications and contains real good stuffs for the one who likes to do bona fide research. Don't be Fuzzy! Just buy it.

This book provides a complete introduction to mechanical theorem proving, including a lesson in both predicate calculus and first order logic. It is also profoundly readable.

Each concept is presented intuitively, abstractly (rigorously), then practically. Every technique and virtually every definition is proceeded by an example in both predicate calculus and first order logic, the first to show its simplicity, and the second to show its power. Proofs of the validity and completeness of every technique are provided to satisfy any concerns about them (and to interest theorists), but are not essential for comprehension of the book. Furthermore, the algorithms are all presented in a mathematically rigorous way, yet in a way that is quickly implemented on a computer.

In short, this book contains everything you need, whether you are interested in the subject or actually want/need to build a theorem prover. Furthermore, it's made as easy as the concepts can possibly be, and very rarely do you have to re-read a section to understand. This is the perfect book on the subject.

If you are interested in Artificial Intelligence or you are one of those crazy guys who likes the Computer theory area this is a good book for you, mechanical theorem proving is an important subject into the AI area, all you want to know is Mathematical Logic, first order logic and predicate calculus. Very good book but very hard too (specially if you have to make some Mechanical theorem provers by yourself as a school project :) )

Best introductory book on automated theorem proving available. Although it was written in the early 70's, it is written in a very clear, but mathematically precise, manner. It does not drown a reader with an abundance of symbols and definitions. It is a clear and well written exposition on automated theorem proving based on resolution. Unlike some recent text books, it does NOT use sequentzen logic systems. It covers all aspects of resolution-based theorem proving: different forms of resolution, deletion strategies, unification.

Turtle Geometry teaches mathematics and physics via the computer and the Logo programming language. The mathematics covered is pretty advanced, including topology, and general relativity. Yet, through the use of turtle geometry this advanced math and physics becomes accessible to the layperson. Although all of the examples are in the Logo programming language there are listings of Basic routines in the back of the book. With the help of the Basic routines I was able to easily translate the Logo/Basic code to the Python programming language which I choose to use for reading this book. The reviewers of this book mention it as the beginnning of a revolution in mathematics education. It seems though, that this revolution did not come about as computers are still not used very effectively in the classroom. I think this is very sad as the teaching approach used in Turtle Geometry could be very successful in the classroom.

Anyone interested in logo from beginners to advanced users will benefit from reading this book. It has very easy and simple to understand examples, along with a review, and questions at the end of every chapter. Some solutions are provided at the end of the book, (and their even correct, as opposed to many other text books I've read). The pace of the book gets gradually more difficulst, yet more interesting as you reach the climax at the end. A must read for anyone interested in Mathematics.

I discovered this little gem of a book while exploring the stacks in the library when I was attending a local junior college back in the 80's. The author uses Logo's turtle graphics as a way of exploring the properties of geometric space. From very simple beginnings drawing regular polygons and other simple shapes, the book gradually works its way to more and more complicated scenarios. After exploring the properties of ordinary turtle graphics, turtle graphics are tried on the surfaces of spheres and cubes, then on more complicated surfaces. Little by little, concepts of non-Euclidean geometry are introduced, until the final chapters in which the turtle is used to demonstrate the geometric nature of gravity in Einstein's general theory of relativity.

I strongly recommend this book to anyone with interests in computer programming, geometry and physics. The unusual approach this book takes to the understanding of curved space is deceptively simple and surprisingly powerful.

C. J. Van Rijsbergen is not an author or editor of this book. See correct reference for ASIN: 3540196080

C. J. Van Rijsbergen was not an author on this book. See the correct reference for this book ASIN: 3540196080

This book celebrates the 90th anniversary of the birth of Alan Turing by bringing together a large set of essays on topics as diverse and colourful as the work and life of the man himself. Turing's fundamental contributions to computing kick started the modern computing era. However, he also made early and outstanding contributions to artificial intelligence, artificial neural networks, morphogenesis, cryptology and the philosophy of mind. The book touches on all these areas and includes contributions from luminaries such as Martin Davis, Daniel Dennett, Andrew Hodges, Douglas Hofstadter and Ray Kurzweil. The book also contains some essays on contemporary topics related to Turing's work such as the controversial area of so-called hypercomputation. While many of the essays are advanced, the material remains accessible and interesting. Turing had a strikingly original and whimsical imagination - reflecting this, and unlike many books on technical topics, this one includes some of the kind of speculation that is bound to fire the imagination of readers. Will computers outstrip human intelligence, and when might it happen? Will we become more like computers, or will they become more like us? Ninety years on from the birth of Alan Turing such issues are more relevant and pressing than ever, and this book makes an excellent advanced introduction to the breadth of Turing's work.

Teuscher has gathered together a set of thought provoking essays about Turing and the ideas he espoused. The diverse range of the essays is a good reflection of Turing's genius.

The essay on making a self-replicating Turing machine reflects earlier speculations on what might more generally be considered a self-replicating Neumann machine.

There is a palpable sense of loss in the book. Turing died at a relatively young age. What if he had lived decades longer? He could have seen the immense flowering of computing, in hardware and software. With his genius, what other insights might he have given us? If you wish, you can regard the book as speculations into this unknowable.

One of the book's authors, Copeland, has recently edited another book -'The Essential Turing', which has essays by Turing himself, and you may want to look at that text.

This was a very informative book and gave lots of insight into the hugh effort that must have gone into this age-old quest. The focus at the personal level gives a nice picture of what the individuals must have gone through. Makes you think "Hey, I could have been there..." Very enjoyable.

Morton Grosser came to Shafter and to our homes while we were trying to build the first man-powered plane. He wrote a book that centered on the people that were working, thinking and arguing towards the goal. If you want a feel of what it was like to do something for the first time ever, read this book.

This book is excellent. If you're tired of reading KM books that just say "KM is good" and that you need to "empower knowledge workers" then this book is a very refreshing change. The book is easy to read (even though writen by an academic) and is centred around a set of case studies from companies you've actually heard of (Microsoft, General Electric, Deloitte Touche, etc.). The case studies really inspire confidence that you actually could implement a KM system and live to see the benefits. Ian Watson writes a couple of chapters at the front which introduce the main ideas behind KM from a technical viewpoint, not a mangerial view, and then you're off into the case studies.

All the case studies use a technique called case-based reasoning that I'd never heard of before. I was fascinated to come across a business intelligence technique I'd never seen mentioned before that actually seems so simple and usable (I've just read the author's previous book on CBR which is also very good).

The book gives you plenty of practical ideas of how to implement a successful CBR KM system and I've been able to pursuade my mangers to start a KM project. This book is currently doing the rounds at work and (almost) everyone loves it.

I've bought too many of these books before which have disapointed because either they are just full of management speak and guru-buzzwords or they are so techie you need a PhD to understand them. Basically this book is practical, sensible and above all useful.

I work for major telco in Hong Kong and we've been applying KM for several years across the business. This book solves a problem I've had. When a new person joins our team or we go to a new business unit I am asked often to provide book to introduce KM. I've found this hard because so many are all theoory and management stuff. Our people want practical example thay can relate to. This book does that through set of case studies from a range of country and industry. The case studies are detailed in a technical way and let people see what a KM system looks like when implement.

Excellent, just the book I've been waiting for. We are now very keen to try CBR (case-based reasoning) on one of our next projects and this book gives lots of practical advice as well as telling us where to go for further information.

Logic is the most common method of artificial intelligence.
Facts, rules and consequences, automated theorem proving,
logic programming and language are all part of artificial
intelligence theory. Founders of formal logic established as
an aim that consequences of situations can be demonstrated
unambiguously. AI has been credited with generating more
productive associations between items of information at little
cost. Look for symmetries, patterns or analogies for establishing
artificial intelligence.

In a robot program:
Guiding systems employ a user to lead robots through motions
Robot level users specify motions and sensing
Task level-users specify operations by their desired effects on
objects

Heuristics consists of a general pruning method. It sets the
search based on local information and explores the alternatives
so that only a subset is explored at each step. Local information
consists of goodness of points in the search space or distance of points utilizing a MINIMAX strategy. If no heuristics,
anything which passes a given set of tests is a solution.

Yazdani's works are an important contribution to the theory
and implementation of artificial intelligence and expert systems.

The author provides important logical structures in artificial
intelligence. Logic is the most common method of AI.
Facts, rules, consequences, automated theorem proving,
logic programming and natural language applications are all
referenced by Yazdani. AI aims to generate more productive
associations between critical items of data at little cost.
An intelligent AI system looks for symmetries, patterns and
analogies for establishing learning. This work is a good
introduction into the area of artificial intelligence.