Artificial Intelligence Books

I read this book for one of my instructional design classes - which focuses on how people learn - it is one of the most interesting books that I have ever read. It gives you alot to think about. I think the best way to learn about a non-fiction book is through a series of quotes of what the person found interesting. So here are some quotes from the book:

"In effect, once she decided to see their situation as one of betrayal, she didn't need to see it any other way. Aspects of the relationship between the two people unrelated to betrayal, or that contradicted the notion of betrayal, were forgotten. Seeing a particular story as an instance of a more general and universally known story causes the teller of the story to forget the differences between the particular and the general.
....In other words, the concept of betrayal becomes what she knows about this situation. It controls her memory of the situation so that new evidence of betrayal is more likely to get admitted into memory than contradictory evidence."(P.148)

"...Is this relationship, however, an example of betrayal? Certainly, the teller relates the story so that betrayal is an accurate description. But betrayal was used as a skeleton story around which the actual story was constructed.
In other words, by using a skeleton story for betrayal, the teller could only construct a story of betrayal. All other aspects of the story were left out. But why, for example, could the teller not have told a story of "devotion"? Only small changes would be needed to make this a story of devotion - a statement that he still loves her and hopes that she will return to her former self or one that shows he values and will support her in her role as mother. ....We want to see the situations that we encounter in terms that are describable to others. We only have a short time in which to tell these stories. So, even if the fit with those stories is not exact, seeing and describing complex stories in terms of standard stories provides an easy shorthand method for communication." (P.148-149)

"The key point here is that once we find a belief and connected story, no further processing, no search for other beliefs need be done. We rarely look to understand a story in more than one way." (p.73)

"The skeletons we use indicate our point of view. Storytelling causes us to adapt a point of view. With this adaption comes a kind of self-definition, however. We are the stories we tell. ...As we come to rely upon certain skeletons to express what has happened to us, we become incapable of seeing the world in any other way. The skeletons we use cause specific episodes to conform to one another. The more a given skeleton is used, the more stories it helps form begin to coher in memory. Consequently, we develop consistent, and rather inflexible points of view." (P.170)

"An incident is remembered in terms of how it is seen in the first place. That is, labeling is in many respects an arbitrary process. ...And, of course, even that last categorization is arbitrary since one person might characterize the victim as being blond, while the other might characterise him as being fat." (P.222)


"We would like to imagine that we learn from the stories of others, but we really only do so when the stories we hear relate to beliefs that we feel rather unsure of, ones that we are flirting with at the moment, so to speak. When we are wondering, consciously or unconsciously, about the truth, about how to act or understand some aspect of the world, then the evidence provided by others can be of some use." (P.78)


"A good memory, then means an attentive labeling facility during processing or you aren't going to remember what you don't find interesting, so the more that interests you the better memory you are likely to have." (P.223-224)

"Yet what we learn is still entirely up to us. No one teaches us how to index after all. We make up our own way of seeing the world,..." (P.113)

"Knowing a great deal about a subject means being able to detect differences that will reflect themselves in differences in indexing. In other words, intelligence depends on clever indexing. Our expert is intelligent about military history. He sees nuances where others would not. He analyzes new stories well enough to be able to relate them to old stories that might not obviously be the same." (P.113)

When was the last time that you changed your mind? I hope it was not long ago.

With this phrase, Sven Ove Hansson introduces the reader to the topic of his book: The Dynamics of Belief (usually referred to as belief revision or logic of theory change).

Who can be interested in belief dynamics? The list is long: philosophers that want a model for the behaviour of the rational agent with respect to his belief dynamics, or for the changes in scientific theories; legal theorists that seek a model that would represent the dynamics of amendments, promulgations or derogations in the legal codes; computer scientists that want a model to formalize the notion of updating databases and knowledge bases; etc.

The belief dynamics was introduced in the late 70's, and the seminal paper of Alchourron, Gardenfors and Makinson 1985, where they presented the (later called) AGM model, became its main milestone.

The AGM model was used as a formal framework to characterize the dynamics and state of belief of a rational agent \footnote{{\sc Peter G{a}rdenfors}. " Knowledge in Flux: Modeling the Dynamics of Epistemic States}. The MIT Press, Cambridge, 1988.}. The beliefs are represented by sets of sentences closed under logical consequence (belief sets). Further works (in which Hansson was one of the most important researchers) extended the model to non-closed set of sentences (belief bases).

In the AGM framework, the dynamics of belief recognizes three basic operations: Expansion consists simply in adding a new belief to the corpus of belief. Revision, related to expansion, incorporates new beliefs, but, as opposed to expansion, consistency is preserved. Consequently, the revision process must eliminate enough sentences to avoid contradiction with the new belief. Contraction occurs when some beliefs are retracted but no new belief is added.
These changes have the following constraints (in priority order): {\bf 1.} " Primacy of new information}: the new information is always accepted {\bf 2.} " Consistency}: if possible, the new belief set must be consistent {\bf 3.} " Minimal loss of previous beliefs}: This criteria attempts at retaining as much of the old beliefs as possible. \\
In the 80's the AGM model acquired the status of a standard model, and was characterized in at least five different equivalent ways: " Postulates, partial meet functions, epistemic entrenchment, safe/kernel contraction" and " sphere-systems".\\
In the 90's, the AGM model was extended, generalized and modified. Among these extensions, we can mention: Non prioritized belief revision, iterable belief change functions, withdrawal functions, modal logics for theory change, hidden structures of beliefs, etc..

In this book, Sven Ove Hansson made an exhaustive description of the AGM model and its equivalent presentations, both in belief sets and belief bases. He also included a wide compilation of the approaches mentioned above developed until the end of 1993 and some further developments. The book has two major achievements: It bridges the gap of a textbook on this subject (the only previous one was the mentioned Gardenfors' book) and provides a deep study of the dynamics in belief bases, needed to apply the AGM model to real contexts.

The book is perfectly oriented to teach courses in belief dynamics, and provides a list of suggested courses (page 1) according to the level of the audience.
Each chapter is divided into two parts: the first one is more colloquial and useful to understand the intuitions, the construction and the purposes of the different approaches. The second one, marked in all cases with ``+", is devoted to the formal apparatus of the previous part. In the "informal" part, the theorems and properties are explained, but the proofs are left to the ``+" parts (in some of them, new and more elegant proofs are provided).

Chapter 1 introduces belief dynamics and the basic tools needed to represent beliefs: logical consequence, belief sets, belief bases, expansions, remainder sets. Chapter 2 explains contraction, including the five AGM presentations and the representation theorems that show the equivalence among them. This chapter also opens the discussion to alternative models like Severe contraction, or Levi's contraction. Chapter 3 is devoted to revision, its different presentations, alternative models and its relation with contraction. In Chapter 4, Hansson introduces an analysis which relates changes in belief bases to changes at "knowledge level"\footnote{See: {\sc Allen Newell}. The knowledge level. " Artificial Intelligence 18\/} (1982), 87--127.}. Chapter 5 presents two extension for the representation language: modal sentences and conditional sentences. Introducing the latter in the representation of belief is incompatible with the Ramsey test. This incompatibility is well known as " Gardenfors impossibility theorem}. Chapter 6 introduces the reader to open problems.

To sum up, this book is an excellent guide to belief change researchers who need a summary of the area. It is also an excellent textbook for beginners who can read the book by following any of the courses suggested.

I wanted a book that covered Inverse Kinematics for animation in computer graphics. What I found was a book that not only went further than my needs, but turned out to be the most comprehensive coverage of IK and associated topics that I have yet come across.

All of the material covered needs some preliminary study before reading this book, probably from several sources. However, if anyone should want a full treatment of Rotation,Orientation,Motion and Forward Kinematics, to bring it all together, then this is the book for it. The author gives the clearest diagrams and explanations of the Denavit-Hartenberg Notation I have yet seen. If the previous papers on this topic are anything to go by, this has not been an easy task.

Chapter 2. Rotation Kinematics. Excellent examples of Rotation and Successive Rotation about Global Cartesian Axes; Global Roll-Pitch-Yaw Angles; Successive Rotation about Local Cartesian Axes; Euler Angles.
Chapter 3, on Orientation Kinematics gives advanced treatment of this area.

Chapter 4, again the best single treatment I have seen on Rigid Body Motion, Inverse and Compound Homogeneous Transformations. Screw Coordinates are included for advanced study.

Chapter 5, on Forward Kinematics, gives numerous examples on applications of the Denavit-Hartenberg Notation to Transformations. Again, the best I have seen yet, with respect to the diagrams and accompanying examples.

Chapter 6, on Inverse Kinematics, is well explained.

The remaining chapters, from Angular Velocity to Numerical Methods, Acceleration, Robot and Motion Dynamics appear to be in the same vein, although I have only scanned the contents.

Of course, the reader will come across the odd typo. However, I would like to congratulate the author on writing what must be the leading textbook in this field.

This book is serious research. Greatly expanding beyond a lot of the consciousness literature, Estep focuses on the scientific evidence to develop a serious theory of human awareness that should be of interest to neuroscientists, surgeons, and ordinary laymen who are sometimes faced with decisions about their own loved ones who may be in comatose or persistent vegetative states. Medical practitioners need to get clear on the parameters of awareness. Estep's work and her extensive research will assist them. Highly recommend this book.

Exactly like the exploration of a new territory allows, sometimes, to discover new resources and new problems, the research on the artificial allows, in our opinion, to set up new intellectual resources and also original problems. Rooted in the imitative disposition of man, the artificial constitutes its more complete and often rational development. Limited both by the nature of the materials (lato sensu) and by the selections of an observation level, of an exemplar and of an essential performance, the artificial possess a sui generis status, which shows not only a reproductive character but also is unavoidably able to innovate the exemplar that, at the beginning, it wanted only to reproduce. Both in a strictly technological domain and in the cases in which the artificial includes non material 'objects' (like communication and arts), it gives us matter enough for reflexions which - independently on their epistemological inspiration - concern the deep sense of wide classes of human activity. In fact, terms like imitation, duplication, replication, falsification, reproduction are concepts only appearently analogous: really they refer to very different forms of 'competition' with the nature and it is, anyway, by means of them that the man achieves some probability to survive, to communicate, to externalize his own mental statuses. The artificial reveals itself, very often, as an illusory reproduction and, simultaneously, as a cause of expansion of the variety of the world. Thus, though it is unable to cope in a complete successful way with the natural exemplar, the technology of the artificial doesn't reproduce mere and useless simulacres of the reality, but new perspectives of knowledge, in the same way in which the communication doesn't give way for a disconsolate monadology but give raise to rhetoric and to the art. The artificialism is not only an ambition but a true human and cultural condition and, in this sense, it represents an important chapter of the research in the domain of human and social sciences. Some possible developments of such a research may be already pointed out and this book tries to do so. For a recent, short outline of the theory of the artificial and an application to the fields of the art, see M. Negrotti, From the artificial to the art, Leonardo, Isast, MIT press, Volume 32, Number 3, May/June 1999.

Totally useless as a reference to today's standards of Artificial Intelligence know-how, this book is a fascinating look into the 1976 cutting edge knowledge and a glimpse of what academics were striving for. Covering such classic topics as ELIZA and the various simple algorithms of AI of the time - with examples of 'current technology' that would make any geek smile. I love this book.

A text that provides good reading to anyone who loves to reminisce or explore the path our technical scientists took to bring us to today's AI systems. Well worth having a dog-eared copy on your bookshelf.

In the early 80s an organisation based in Switzerland published a series of studies about multinationals and their perceptions in the world. This one was extremely well documented on multinationals in India (Tata Group), Brazil, Argentina and other third world countries. I read it when it was published.

Since the situation has evolved as anticipated by this 1983 book. Emerging markets multinationals have multiplied and can pose serious challenges to global corporations (especially in Asia). Unfortunatly, the Swiss organisation ended its activity because the multinationals had become more accepted in the late 1980s.

With the rise of anti-corporate-led globalisation and a possible global financial crisis, a new serie on corporate future in a non-corporate led globalisation might be required. Still the 1983 book is of historical value and anticipated today's trend.

Can a computer program running on a computer demonstrate aspects of human intelligence? If so, what tests could be performed to demonstrate this?

Well, it is easy to think of some tests. Here are seven simple ones. 1) Look up a word in a dictionary. 2) Add a very long column of numbers. 3) Play Chess really well. 4) Play Go really well. 5) Solve a really difficult research problem in theoretical physics. 6) Write a really good novel. 7) Compose a really good classical symphony.

Today, computers have passed tests 1), 2), and 3). But they play chess well by using a very poor program indeed, relying instead on a computer that is extremely fast and has plenty of memory. We're seeing really good computers. But the programs we're seeing are awful.

Maybe better tests would be to see if a team of a human plus a computer could do much better than two humans with no computer at each of those seven tasks. And here, I'm sure the answer is yes to many of these seven tasks, and could easily be yes to all of them.

Still, those who work in the field of artificial intelligence have wanted to come up with programs that exhibit some intelligent features. And one test they have tried is one proposed by Turing in 1950: if a human or computer may be in the next room and you exchange messages with her, him, or it, (maybe at a rate of one message every 15 seconds), can you tell with better than 70% accuracy after 5 minutes if the entity in the other room is human or computer?

The book discusses whether or not such a test has much to do with intelligence or artificial intelligence. And it describes the failure of programs to pass that test so far.

In 2000, several programs were entered into a contest to try to mimic a human in this manner. No judge thought a program was in fact a human. And worse than that, the book tells us that all nine of the following questions were answered correctly by all the humans, while no computer got any of the nine questions right:

1) What is the color of a blue truck?
2) Where is Sue's nose when Sue is in her house?
3) What happens to an ice cube in a hot drink?
4) Altogether how many feet do four cats have?
5) How is the father of Andy's mother related to Andy?
6) What does the letter 'M' look like when turned upside down?
7) What comes next after A1, B2, C3?
8) Reverse the digits in 41.
9) PLEASE IMITATE MY TYPING STYLE.

That shows how utterly the programs failed Turing's test.

In short, programs today are horrible at understanding, reasoning, learning, judgment, and creativity.

I liked this book. It showed that no matter what one thinks of machine intelligence, AI people have made shockingly little progress in the past fifty years.

A programmable digital computer, based on Alan Turing's design, ran its first program in 1950 at the National Physical Laboratory, London. Even today, every computer in the world remains computationally equivalent to a Turing Machine. It is little known however, that Turing also investigated neural network architectures as early as 1948, and before the term genetic algorithm was coined, proposed configuring his networks with a "genetical search". In this book Teuscher presents the most extensive exploration of Turing's neural networks available. The book contains over 100 diagrams, detailed examinations of the logical behaviour of Turing's networks, experiments into their emergent properties and extensions of Turing's ideas based on recent findings. An understanding of Turing's networks allows insight into a number of modern research areas such as Kauffman's work on the principles of self-organisation, the boundaries of computability, and even the real neural networks of living things (Turing claimed that his neural networks were probably the simplest possible model of the human cortex). Because the discussion in the book starts with Turing's early networks and progresses through to current research, it can also be read as an accessible overview of the history of the field. In addition, the book makes it clear that there are many interesting research questions still to be answered in this area. As such, this book will be of interest to historians of computer science and modern researchers alike.

This is an absolutely brilliant work focusing on both fundamental aspects of Fuzzy logic systems. Firstly an intuitive and physical understanding essential for diverse applications of fuzzy sets is developed while at the same time mathematically sound treatment of fundamentals is carried out. There is no other book which integrates clarity and the underlying mathematics to such high level. The treatment of type-2 fuzzy sets is unrivalled by all standards. Mendel is perhaps first time ever in a published book, gives a truly geometric and easy to understand description of type-2 fuzzy sets, making lives of PhD students like myself easier. However, book can be read and used for applications by any one with undergraduate degree in science, engineering or even finance etc and requires moderate mathematical maturity. All relevant mathematical aspects of fuzzy logic systems are covered in detailed in the book itself.

The book begins with a comprehensive and deep treatment of type-1 logic system or in other words, ordinary fuzzy set theory. Then type-2 fuzzy is introduced early on with extremely easy to understand format. While theory of ordinary fuzzy logic is developed to the full extend in part 1, part two of the book, focuses on parallel development of type-2 fuzzy sets. There are no other works, except perhaps Mendel's own published papers, which have such easy to understand and easy to internalized treatment of relatively new and difficult area of type-2 fuzzy logic system. Throughout the easy to understand examples with detail applications of type-2 fuzzy logic system to engineering ranging from digital communications to knowledge mining are given.

While mathematical aspects are covered in detail and without sacrificing required rigor, overall style is quite informal and very easy to read. The book successfully advocates the use of type-2 fuzzy logic systems as a generalization of type-1 fuzzy logic system while demonstrating increased applicable power of type-2 fuzzy systems to tackle difficult engineering and scientific problems including but not limited to time varying, systems, systems involving non-stationary noise processes and nonlinear systems.

Relevant examples of above areas are considered in detail along with reasons to use type-2 fuzzy systems as a solution mechanism. Examples of application areas covered include equalizations of nonlinear and time-varying digital communications channels, rule based classifications of video traffic, knowledge mining using IF-THEN questionnaires and so on. This aspect makes this book quite unique and allows easier transition from described theory to the required applications for the reader.

In the final analysis, this is the perhaps the only book available, which while stressing mathematical details relating to type-1 and type-2 fuzzy logic systems, provides physical and intuitive understanding drawn from detailed discussions and heavily supplemented with figures and pictures with remarkable clarity as well as gives easier to follow practical application based examples. All three aspects work together in a synergetic manner to clarify and imprint deep knowledge relating to fuzzy systems in the minds of the readers.

Last but not least, detailed computer programs in Matlab are provided covering both type-1 and type-2 fuzzy logic systems. This aspect allows immediate applications to practical problems.

This book is greatly appreciated and highly recommended for anyone interested in type-1, or type-2 fuzzy logic systems aiming either to deeply understand these important areas or to apply them in his or her field of interest.