Product Description

Artificial intelligence—broadly defined as the study of making computers perform tasks that require human intelligence—has grown rapidly as a field of research and industrial application in recent years. Whereas traditionally, AI used techniques drawn from symbolic models such as knowledge-based and logic programming systems, interest has grown in newer paradigms, notably neural networks, genetic algorithms, and fuzzy logic.

The significantly updated second edition of Fundamentals of the New Artificial Intelligence thoroughly covers the most essential and widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, and chaos. In particular, this unique textbook explores the importance of this content for real-world applications. The exposition reveals the core principles, concepts, and technologies in a concise and accessible, easy-to-understand manner, and as a result, prerequisites are minimal: A basic understanding of computer programming and mathematics makes the book suitable for readers coming to this subject for the first time.

Topics and features:

• Retains the well-received features of the first edition, yet clarifies and expands on the topic

• Features completely new material on simulated annealing, Boltzmann machines, and extended fuzzy if-then rules tables [NEW]

• Emphasizes the real-world applications derived from this important area of computer science

• Provides easy-to-comprehend descriptions and algorithms

• Updates all references, for maximum usefulness to professors, students, and other readers [NEW]

• Integrates all material, yet allows each chapter to be used or studied independently

This invaluable text and reference is an authoritative introduction to the subject and is therefore ideal for upper-level undergraduates and graduates studying intelligent computing, soft computing, neural networks, evolutionary computing, and fuzzy systems. In addition, the material is self-contained and therefore valuable to researchers in many related disciplines. Professor Munakata is a leading figure in this field and has given courses on this topic extensively.

Product Description

This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Virtual Agents, IVA 2009, held in Amsterdam, The Netherlands, in September 2009.

The 19 revised full papers and 30 revised short papers presented together with 35 poster papers, three keynote talks, and 7 GALA papers were carefully reviewed and selected from 104 submissions. The papers are organized in topical sections on personality and memory, gesture and bodily behavior, evaluation, facial expression and gaze, culture, affect and empathy, agents in virtual worlds and games, tools and motion capture, and speech and dialogue.

Product Description

This book constitutes the thoroughly refereed post-workshop proceedings of the Third International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2006, held in Rome, Italy, in September 2006, in association with SAB 2006, the 9th International Conference on Simulation of Adaptive Behavior.

The 20 revised full papers presented were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The introductory chapter of this state-of-the-art survey not only provides an overview of the contributions included in this volume but also proposes a taxonomy of how anticipatory mechanisms can improve adaptive behavior and learning in cognitive systems. The papers are organized in topical sections on anticipatory aspects in brains, language, and cognition, individual anticipatory frameworks, learning predictions and anticipations, anticipatory individual behavior, as well as anticipatory social behavior.

Product Description
Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed.

This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

*Authors are well-recognized experts in the field who have applied the techniques to real-world problems
* Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems
*Offers the first true synthesis of the field in over a decade ... Read more

Customer Reviews (4)

Ok, but not enlightening
I own an old edition ofthe classic Russell and Norvig (R&N) which I read 10 years ago and did not feel like going through the huge new 2009 edition to learn about current topics, so I went looking for something a bit more recent with a focus on knowledge representation, and came up with this book. I have to say unfortunately that while not a bad book, it does not cover much more than the old R&N (side note on this: R&N is very comprehensive and covers the full AI spectrum. This book seems biased toward one particular school of AI. This may or may not be bad for you: if you're not interested in the additional material in R&N, such as neural nets, you're possibly better off with this book. I doubt there are many of you in this case though) and tends to be less pedagogical. It is also more uneven regarding the depth at which topics are covered, with a fairly strong bias toward the topics where the authors appear to be active researcher. Such a bias would be ok for a more advanced textbook, but we're talking about a fairly introductory text here, and it feels a bit unbalanced. I cannot therefore recommend it highly, but I am not highly critical either, as I still managed to learn a couple of things. Below are detailed notes, which I hope might be of interest to outline the stronger points. As a side note, this is a very theoretical book, with no direct programming application or exercises. This did not bother may, but may not be clear from the other reviews.

The introduction sets the scene well and provides a useful conceptual background. How the following chapters are articulated against the principles discussed in the introduction is not always straightforwardly clear though. In that sense, the authors may fall a bit short of their overall goal.

The second chapter (the language of first order logic) is unlikely to be big news for anybody schooled in undergraduate mathematics, but I understand the material must be included for the sake of completeness and autonomy. The third chapter is entitled "expressing knowledge" and in my view does not really do justice to the topic, as demonstrated by the matter covered in the afterthought section "other sorts of facts": these "other facts" include statistical and probabilistic facts, default and prototypical facts, intentional facts (beliefs etc...). The book deals with some of these later to be fair.

At this point in the book, all that has been achieved is to show how one can use first order logic (FOL) to deallogically with some problems that a six years old can probably solve without the need for the framework. Chapter 4 shows that it is possible to teach FOL to a computer and to have him assess the truth of a statement formulated in FOL given a number of others FOL statements. The algorithm is not completely trivial but not overly complex either. Unfortunately, the time taken to deal with such tasks is potentially very large for problems not amazingly complex if one allows FOL statements of arbitrary structured. Chapter 5 is dedicated to the exploration of Horn clauses, which are basically a type of FOL statement for which algorithms are available that converge faster. This motivates the need to embed some hints on how to reason with a given problem within computer languages. Chapters 6 and 7 explore this respectively in the context of PROLOG and of the so-called "production rules systems". As one gets familiar with the above approaches, a number of limitations become clear and the subsequent chapters are about moving away somewhat from FOL. Chapter 8 introduces object oriented representation, using a formalism a bit on the heavy side for a concept that's actually fairly clear. Give or take a few examples, a reader of R&N is on familiar ground up to this point in the book. The next chapters, respectively on description logics and inheritance cover material that was less familiar to me and might be a reason to dig into this book. It shows a couple of neat ideas (taxonomies, inheritance networks) and how reasoning with such data structures can be difficult when one encounters contradictions. This motivates the need to clarify the concept of "default", which is done in chapter 11, another good chapter in my view. Chapter 12 includes an introduction to probabilities that probably ranks with chapter 2 as something most readers don't really need. It also covers fairly superficially bayesian networks, influence diagrams and the Dempster Shafer theory. In all honesty given the brisk pace at which this is all done, I don't think it's really possible to get much out of what's covered here.

The concepts in the next chapter (Explanation and Diagnostic) were newer to me. While not straigthforward to implement, it seems the core approach of the authors is here at an advantage over other more opaque techniques.

The next two chapters ("Actions" and "Planning") deal with topics that are closer to the preoccupation of standard AI. As they're both good topics to motivate the AI endeavour, introducing them earlier might have made more sense.

The last chapter is about "the tradeoff between expressiveness and tractability". The authors look back at the big picture that had been evoked during the introduction, but which had to some extent taken the back seat during most of the time. Fairly uncontroversially, they point out that being able to deal with very expressive languages is desirable, but typically fraught with tractability issues. One senses that the

PS: I bought the Kindle edition of the book, and as unfortunately too frequent, it suffers from some navigational issues: the table of content does not link correctly to the materials referenced (links are off by a few pages) and citations are not hyperlinked, which makes it less than user-friendly to determinewhat book or article stands behind the reference [137]. This is something the publishers really ought to sort out, as I cannot think of any good justification for such sloppiness.

A complete knowledge representation book
This book easily introduce the most important knowledge representation techniques, with a clear style and lots of examples. It is complete yet not disorganized. A must for everyone who wants to become a knowledge engineer.

Don't have to be a math buff to understand
I came across this book looking for a text that would explain the context of First Order Logic, why it is used for so many knowledge representation problems, how it is used to solve them, and its limitations.I must say that this is far and away the best book I've found to answer these questions.If you search around a little at the competition, you will find much of the text quickly turning to mathematical proofs and deductions in their explanations.While this is of course necessary and helpful, it doesn't (for me) really give an idea of how and why these methods are used practically.You can tell that these authors spent some time on ensuring consistency and fluency of the writing, which I find so very helpful.

I'm trying to think of something bad to say about it: I wish it were longer!If you read the preface you will see the authors call it an introduction, which is definitely true.Maybe they will team up again for a more in-depth text on some aspect of this subject.

This book is an Eye-Opener!
I love this book- It is a comprehensive introduction into knowledge representation, with enough detail to create your own knowledge representation programs.

Are you a programmer who wonders what it really means when an object *IS* another object, in the form of inheritance found in object-oriented systems? Ever confused by the nuances of multiple inheritance? Ever wonder what XML or OOP or Relational Databases have to do with each other? Ever wonder if all those A.I. programmers in the 70s actually created anything useful? Ever wonder how type systems work? Ever wonder how to store complicated and vague data into a database?

This book doesn't really have answers to these questions (nobody really does, in my opinion) but learning the information in this book is the first step you'll want to take to get closer to some answers...

It basically covers 3 main topics: FOL (traditional logic like you probably learned in college) Frames (sort of the grandaddy of OOP) and Description Logics (a really powerful synthesis of object-thinking with strict logical fundamentals)

This book has a bit of hairy mathematical notation in it, so if your not comfortable talking about things like "an object x that is an element in the domain" some of the chapters will require a bit of effort on your part. The authors are careful, however, to follow every difficult mathematical analysis with some concrete examples that ease the learning process- I often wish examples were more frequent in other theoretical tombs like this. Any computer programmer can process this text with a bit of moderate effort.

I couldn't imagine being a professional programmer and not knowing the information in this book now that I have read it. Although the topics in this book are somewhat obscure today, I think they will receive far greater appreciation in the future- especially among medical software developers. Here's your chance to be ahead of the curve in the field of knowledge representation! ... Read more

Product Description
A fascinating portrait of the people, programs, and ideas that have driven the search to create thinking machines. Rich with anecdotes about the founders and leaders and their celebrated feuds and intellectual gamesmanship, AI chronicles their dramatic successes and failures and discusses the next nece ssary breakthrough: teaching computers "common sense".Amazon.com Review
Perhaps no venture in the history of computing has produced somany high hopes and attracted so many brilliant minds, yet produced somany daunting failures as the quest for artificial intelligence.Daniel Crevier' fascinating and deeply researched history of theAI traces the search for machine intelligence from theoptimistic first experiments of the mid 1950s, through the classicprojects of the next two decades, on to the mixed fortunes of thecommercial AI ventures that began in the 1980s. In addition to being ahistory of an intellectual field, it's a portrait gallery of thebrilliant and often eccentric people who built it. Crevier'sdiscussion does not demand a programming background, yet takes thereader deeply into theoretical issues that make us ponder thephenomenon of human intelligence. ... Read more

Customer Reviews (4)

Excellent introduction to artificial intelligence.
It's a great pity that this book is out of print. Daniel Crevier has written an excellent history of AI, which is also insightful in its analysis of the field. It is that rare creature in AI literature, somethingthat is written clearly and objectively for the layperson, but that alsoconveys the complexity of AI. AI would find a lot more use in ourworkplaces and homes than it already has, if more people would communicateits strengths and weaknesses as lucidly as Daniel Crevier, and focus on itspractical applications rather than speculating on spiritual machines. Hewould do a great service to the field to bring it up to date and have itrepublished.

AI: The search continues
AI Tells the bumpy story of the search for artificial intellegence from the eyes of the people who were there. The stories start in the 1950's and include present day research. It is well written and captures the reader with a touch of philosophy, asking such questions as "are computers really smart?"

Wonderful book on a much misunderstood subject.
I thought this was a great book on Artificial Intelligence.It would be a good book for anyone interested in the subject at all because it is written in a way to give just enough technical detail for the enthusiast, while keeping the casual reader interested.The author does a very commendable job in his interpretation of the history of this subject.He uses personal experience as well as good research on the subject to give us a great story as well as the cold hard facts.

An amusing history of this much-maligned topic
As this book demonstrates, you can learn a lot from what doesn't work. The history and politics of AI are as interesting as its successes and failures to date. There's not a lot of technical detail here, but the author does manage to weave the thread of a pretty good story through this history. ... Read more

Product Description
Automated planning technology now plays a significant role in a variety of demanding applications, ranging from controlling space vehicles and robots to playing the game of bridge. These real-world applications create new opportunities for synergy between theory and practice: observing what works well in practice leads to better theories of planning, and better theories lead to better performance of practical applications.

Automated Planning mirrors this dialogue by offering a comprehensive, up-to-date resource on both the theory and practice of automated planning. The book goes well beyond classical planning, to include temporal planning, resource scheduling, planning under uncertainty, and modern techniques for plan generation, such as task decomposition, propositional satisfiability, constraint satisfaction, and model checking.

The authors combine over 30 years experience in planning research and development to offer an invaluable text to researchers, professionals, and graduate students.

*Comprehensively explains paradigms for automated planning.
*Provides a thorough understanding of theory and planning practice, and how they relate to each other.
*Presents case studies of applications in space, robotics, CAD/CAM, process control, emergency operations, and games.

*Provides a thorough understanding of AI planning theory and practice, and how they relate to each other.
*Covers all the contemporary topics of planning, as well as important practical applications of planning, such as model checking and game playing.
*Presents case studies and applications in planning engineering, space, robotics, CAD/CAM, process control, emergency operations, and games.
*Provides lecture notes, examples of programming assignments, pointers to downloadable planning systems and related information online. ... Read more

Customer Reviews (3)

Excellent presentation that fills a void
Until this book, possibly the only comprehensive treatment of planning has been a paper collection: Readings in Planning (Morgan Kaufmann Series in Representation and Reasoning). What these authors have done is phenominal - they've marshalled a bibliography of 565 publications into a comprehensive treatment from a common point of view. That makes it much easier to analyze different approaches to planning, as well as to see how various application domains have applied these approaches to solve real problems.

The first 448 pages of the book discusses various planning approaches, from classical state-space planning including recent improvements in the STRIPS model (GraphPlan), to dealing with temporal operations and resource scheduling. They then use the readers understanding of these deterministic approaches to bridge to planning under uncertainty, which is where planning meets the "real world" of imperfect knowledge, observability or even actions having unintended effects. The next roughly 100 pages goes into application domains discussing how space applications, robotics, manufacturing, emergency evacuation and even the game of bridge has used these planning methods to give the reader better intuitions on their own domain.

Finally some minority approaches such as case-based planning and plan related areas such as plan recognition are introduced briefly, leading to tutorial appendices on search (and complexity), first order logic, and model checking.

I have been working on the periphery of planning research for over 25 years, including (currently) directing advanced research in adversarial planning (a topic not addressed by this book, but that's hardly surprising given the novelty of the approach ;-). This is the best overview and reference I've seen to date for this very important area.

Good book, but could be better
The book is good, covers a lot and is very clear.

The downside: there are some small errors and mistakes.For example, the authors define gamma: SxAxE -> 2^Sas the transition function, where S is the state space, A is the set of actions, E is the set of events. Later they say that if there are no events to be considered from the outside world, then you could use E={} (empty set) -- Assumption A3, page 10. Although this is intuitively OK, it is mathematically flawed, because the cartesian product of anything with {} is{}.
Planning with MDPs and specially with POMDPs deserves more attention. In particular, the very short commentary on planning with POMDPs mentions that it is not possible to solve big POMDPs. This is not true anymore; there are very good heuristics for POMDP solving currently.
I think more theorems could have been presented and proved, and some advanced sections could be added to each chapter (some authors include a section with a star, for example)
I also don't like the way pseudo-code is presented, but that is a matter of taste.

It would also be nice if the examples in chapter 2 were fully specified. That helps a lot to understand how problems are represented.

On the good side, there are LOTS of examples for each definition, and there are exercises at the end of each chapter (more exercises would be nice, actually). I also like the discussion and historical remarks at the end of chapters.

This is certainly a very good book. Anyone interested in planning ought to have it (and people interested in AI will certainly benefit from it).

Great Introductory Book.
Automated Planning is a good book for those who get started out in the field of search and planning. It's a good overview of the topics that abound within the planning community.

The only downside of the book is its dealing with important topics like planning graphs and markov description process is cursory, and more detail would have been nice. ... Read more

Product Description
Anticipating the needs of professionals and researchers alike, this dictionary is the first up-to-date reference volume on a discipline of ever-growing importance. Artificial intelligence is rapidly becoming the foundation discipline for thousands of new applications and will have dramatic effects on virtually every activity we engage in.

This essential reference features over 2,500 entries--all defined, explained and illustrated--as well as detailed explanations of major concepts in related disciplines. A completely cross-referenced index, annotated bibliography, and extensive appendix of World Wide Web sites on the latest trends in AI will make this a favorite resource for practitioners and researchers around the world.

... Read more

Product Description
In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking.

By examining these different forms of mind activity, Minsky says, we can explain why our thought sometimes takes the form of carefully reasoned analysis and at other times turns to emotion. He shows how our minds progress from simple, instinctive kinds of thought to more complex forms, such as consciousness or self-awareness. And he argues that because we tend to see our thinking as fragmented, we fail to appreciate what powerful thinkers we really are. Indeed, says Minsky, if thinking can be understood as the step-by-step process that it is, then we can build machines -- artificial intelligences -- that not only can assist with our thinking by thinking as we do but have the potential to be as conscious as we are.

Eloquently written, The Emotion Machine is an intriguing look into a future where more powerful artificial intelligences await.

... Read more

Customer Reviews (24)

Good job
The book arrived in good condition. What I did not enjoy that much, was the black marker line on the side of the book, over the pages. Overall, it was a good deal and I would buy again.

A brilliant book about the mind.
The ''Emotion Machine'' by Marvin Minsky is an introduction to how our minds work.
An endeavor to understand mind (thinking, intellect) in terms of its design (how it is
built, how it works).
Sure, the inner workings of the mind sometimes appear to be impervious to any kind of
scientific approach. Routine stuff like making mental models of the world, design plans,pursue goals and feel desires aren't all that routine, when you think about it. And the way it all plays together - from simple, instinctive kinds of thought to more complex forms, such as consciousness or self awareness - is obviously very complex.

It would be very easy to get stuck in too much detail or to be too superficial. But somehow Minsky finds the right balance. So overall, the Emotion Machine is a brilliant introduction to how our minds work.

Often he will give us an idea about what must be going in computational terms, and then shield us from the usual deluge of ''neuro-technical'' terms. Because (in his own words) ''research on the (actual nitty gitty of the) brain is advancing so quickly that any conclusion one might make today could be outdated in just a few weeks.'' In one reviewers words: ''The Emotion Machine rewards careful reading. You'll learn a lot about how your mind works, even if you won't be all that much wiser about what is actually going on within your brain.'' :-)

Nevertheless, the book is obviously based on the latest advances in computer science, psychology,neuroscience, engineering etc. And certainly, his book is not a bad place to start if one wants to be a little wiser on what goes on inside our heads.
A wonderful book.

-Simon

Simple and brilliant framework for understanding mind.Is it strong AI done right at last?
Early efforts to model human-like thinking with machines using rules were interesting but failed in a number of ways to capture even simple ways that humans think. Marvin Minsky, AI pioneer at MIT, insists that we understand the mistakes and can begin to appreciate how the mind actually works in functional terms from the lessons we have learned. Learninig from our past mistakes, what a novel idea.

To put this into perspective, the question of whether a machine model can adequately describe a brain has long been considered in terms of either strong AI or weak AI. Most people find weak AI plausible: computers can solve certain kinds of problems better than humans. We mostly balk at strong AI however: machines can literally think like humans and solve the same kinds of problems just as well.

In The Emotion Machine, Marvin Minsky presents a very machine-like architecture that he claims actually represents the way real minds probably work in fundamental respects. That sounds pretty much like strong AI. So a lot of people will reject the concept of this book out of hand. I think that would be a mistake. Minsky has done a very good job identifying plausible specifics of why AI programs have failed to deliver on, where they have actually managed to deliver, and speculates on how we can fill in the gaps.

No, he doesn't spend time arguing against Searle's Chinese Room or other conundrums of AI, he just presents his case and gives examples in a clear, simple, accessible way. And I am persuaded that he probably gets a lot right. Probably more than he gets wrong. And that's a lot better than a lot of critics will give him credit for because it goes against both the mainstream disdain for strong AI and the mainstream love of flashy neuroscience images.

Minsky skips right on past the issue of connectionist networks vs. semantic networks and simply posits that we had to evolve semantic representations at some point. How is left as an exercise for neuroscientists. There is a lot of "details to be filled in later" sort of thinking here, so don't look to this book as a detailed physical model of the brain. This is a high level functional model of the mind and I like it.

So I claim that this is an important book that seems to promise a 21st century reboot of scientific naturalism as our guiding philosophy for the future. Minsky takes on nothing less than an overall architectural model for the mind in natural terms. It is brilliant. Too brilliant to be appreciated in its time because Minsky makes complex ideas so accessible that the biggest challenge for this book is that people will not appreciate its power. It reads like a simple AI model of a mind, but it is much deeper than that because of the amount of deep thought that has gone into it and the consideration of the weaknesses as well as strengths of previous AI programs.

We are currently in the grip of a widespread fascination with poorly understood pop neuroscience, and most readers will be deeply disappointed that this book does not attempt to wrestle with brain science at all. I think that's a strength because it means Minsky is not falling into the weird metaphysical spins that we too often see in pop neuroscience books, especially those by non-researchers and over-enthusiastic under-trained journalists.

What Minsky is doing here is simply coming up with a logical model of what a mind has to be able to do to provide the capabilities that we observe real human minds to possess. Sounds simple, right? No, not at all. The reason Minsky has accomplished something special here is that he recognizes many of the powerful fallacies we usually fall into when we introspect about thinking and rely on traditional models. We tend to think of emotions and reasoning as separate kinds of things, and then we talk about how they are both needed and how they interact. But as Minsky points out, both neuroscience and psychology seem to provide us evidence that these are points on a continuum, not different kinds of things. Minsky takes that seriously and builds on it.

The result is something amazing that looks like a simplistic mechanical model of the mind but captures some deep insights into how minds really work.

The central implication of Minsky's model is an epistemological stance that resourcefulness in human thinking is a matter of switching between different kinds of representations, each used in a different way of thinking, each of which captures something essential about specific things in our world while neccessarily leaving out other details. A mind can't comprehend everything at once. Some decisions simply don't have an optimal answer because they look different from different angles.

The key concept underlying Minsky's model is that minds as we think of them had to start with simple rules for recognizing and responding to cues, had to be able to incorporate goals in some form in those rules as well, and then eventually had to be able to recognize kinds of problem and activate appropriate ways of thinking. It makes sense to think of this in terms of logical levels of recognizers and responders, and importantly, what Minsky calls "critics" and "selectors," where each new level provides some way to resolve conflicts that arise in the level below it.

So conflicts in our instincts can be resolved by learned rules, conflicts in learned rules can be resolved by deliberation strategies, and in turn levels with different kinds of representations of the problem and eventually the problem solver and their own ways of thinking. Once the problem solver can represent themselves and their own thinking, we have the power to shape our own thinking in meaningful ways.

I'm really not doing justice to this book in this review, because it's power is in the details of his examples and how they illustrate the architecture at work. Suffice to say that I think if you find a functional architecture of the mind of interest, I highly recommend this book. I think it gives a much more fundamental understanding of how minds most probably work than any amount of flashy recent brain scans, and certainly more than untestable holistic and quantum mechanical theories will ever tell us until we better understand the functional design. Neuroscience in the future will, I believe, be filling in the details of a framework very much like this one.

brilliant explanation of the mind
Minsky gives us an accounting for how the mind might work that is consistent with the neurological/anatomical make up of the central nervous system.He "de-mystifies" thinking about thinking, and points us in a promising direction for the study of mind.The Psychology of Positive Thinking

Worth the read.
Minsky presents interesting new ideas on understanding ourselves. It makes sense that the mind, like the body, may seem simple on the outside but is amazingly complex on the inside. ... Read more

Product Description
In clear, readable language, consultant and researcher Kevin Desouza accomplishes an unlikely feat: explaining artificial intelligence to nonspecialists in a way that experts will recognize and accept as correct and immediately applicable. ... Read more

Product Description
Apocalyptic AI, the hope that we might one day upload our minds into machines or cyberspace and live forever, is a surprisingly wide-spread and influential idea, affecting everything from the world view of online gamers to government research funding and philosophical thought. In Apocalyptic AI, Robert Geraci offers the first serious account of this "cyber-theology" and the people who promote it.

Drawing on interviews with roboticists and AI researchers and with devotees of the online game Second Life, among others, Geraci illuminates the ideas of such advocates of Apocalyptic AI as Hans Moravec and Ray Kurzweil. He reveals that the rhetoric of Apocalyptic AI is strikingly similar to that of the apocalyptic traditions of Judaism and Christianity. In both systems, the believer is trapped in a dualistic universe and expects a resolution in which he or she will be translated to a transcendent new world and live forever in a glorified new body. Equally important, Geraci shows how this worldview shapes our culture. For instance, Apocalyptic AI has influenced funding agencies such as the National Science Foundation, helping to prioritize robotics and AI research. It has become the ideology of choice for online gamers, such as those involved in Second Life; it has had a profound impact on the study of the mind; and it has inspired scientists and theologians alike to wonder about the super robots of the future. Should we think of robots as persons? What kind of morality would intelligent robots espouse?

Apocalyptic AI has become a powerful force in modern culture. In this superb volume, Robert Geraci shines a light on this belief system, revealing what it is and how it is changing society. ... Read more

Product Description

Could a computer have a mind? What kind of machine would this be? Exactly what do we mean by "mind" anyway?

The notion of the "intelligent'"machine, while continuing to feature in numerous entertaining and frightening fictions, has also been the focus of a serious and dedicated research tradition. Reflecting on these fictions, and on the research tradition that pursues "Artificial Intelligence", raises a number of vexing philosophical issues.Minds and Computers offers an engaging, coherent, and highly approachable interdisciplinary introduction to the Philosophy of Artificial Intelligence.

Readers are presented with introductory material from each of the disciplines which constitute Cognitive Science: Philosophy, Neuroscience, Psychology, Computer Science, and Linguistics. Throughout, readers are encouraged to consider the implications of this disparate and wide-ranging material for the possibility of developing machines with minds. And they can expect to develop a foundation for philosophically responsible engagement with A.I., a sound understanding of Philosophy of Mind and of computational theory, and a good feel for cross-disciplinary analysis.

Features:

*A solid foundation in the Philosophy of Mind

*A broadly interdisciplinary purview

*A directed philosophical focus

*A clear and accessible explanation of technical material with abundant exercises

*A glossary of terms

Customer Reviews (1)

A first-rate introduction to the computational theory of mind
If you are looking for a comprehensive interdisciplinary overview of the core concepts of cognitive science lucidly presented in a couple of hundred pages, seek no more. Matt Carter accomplishes just that by examining the relation between minds and computers, a goal which takes him beyond the bounds of conventional artificial intelligence. In addition to classical AI, Carter ably discusses the fundamental ideas of philosophy of mind, psychology and behaviorism, neuroscience, computational intelligence, and linguistics: the fields constituting cognitive science. The aim of this grand tour is to facilitate development of a philosophically sound computational theory of mind.

The tour begins, quite properly, in the very beginning with a review of Cartesian dualism followed by concise discussions of behaviorism, neuroanatomy, Australian (or reductive) materialism, and functionalism. The classical symbolic computational architecture thesis of AI is then examined in some detail. Subsequent chapters delve into particular issues of interest, including computationalism, standard AI search techniques, machine and human reasoning, machine and human language, and artificial neural networks. Even automated game playing, a perennial AI favorite, gets a little chapter of its own. Classic AI icons are, of course, showcased: the Turing test, expert systems, the Chinese room. Alas, 200-odd pages impose severe limits; thus Minsky and Simon and Newell and McCarthy and many other legendary heavyweights don't even make it into the footnotes. Actually, there are no footnotes. Nor endnotes. But there are excellent suggestions for further reading and a very helpful glossary in the appendices.

This book is simply wonderful. It's everything you wanted to know about cog sci delivered under the label of AI. That is fine. It was, after all, good old fashioned AI that got the ball rolling in the first place. Think of the book as a broad-based introduction to AI sans the messy programming requirements. Naturally, possession of an alert brain is de rigueur. ... Read more

Product Description
Even as molecular biologists attempt to reproduce life in vitro, another group of scientists is creating life--or something very close to it--in silico, using computers to produce "organisms" that can move, see, feed, reproduce, and die. Photos. ... Read more

Customer Reviews (14)

Superb!!
AL is popular science writing of the first order: informative, clear, fascinating, and entertaining.My only disappointment is that it was published in 1992, and thus does not touch on developments in the field since then.I'd love to know how these have panned out, and whether scientists remain enthusiastic about the possibilities of A-Life.Judging from the textbooks on A-life that have been published since 1992, the field is alive, at least, and I can only assume it is well to boot.I'll have to hunt for bibliography elsewhere.My thanks to Levy for sending me on this hunt.AL is a book to fire the imagination.I'd give it 10 stars!

A note on the metaphysical material in AL that bears on the question of whether present iterations of 'artificial life' are, or whether future iterations may one day be, sufficiently complex that they should be considered true LIFE: throughout, Levy stresses the essential link between an (')organism(') (wet or dry) and its environment.Yet, it seems to me, in discussing the question of the LIFE-status of in-silico 'organisms', he considers the 'organisms' alone.I wonder whether this apparent preference reflects his own bias, or a bias on the part of the scientists he profiles?From the perspective of emergent behavior and the capacity to evolve, etc., AL 'creatures' self-evidently bear a striking resemblance to biological creatures.It strikes me, however, that a key consideration in the wet-life as LIFE versus dry-'life' as LIFE argument -- is that wet-life organisms express emergent behavior and evolve, etc., in environments that are, throughout, rife with other life, whereas dry-'life' 'organisms' do the same in environments that are otherwise sterile (by the standards that A-Life scientists themselves would apply).Some consideration of how environments contribute to the LIFE-status of particular (')organism(')s, and of any definition of LIFE (wet or dry) itself, seems to be of the essence.Yet another thought to pursue -- though doubtless ethologists, philosophers, and A-Life scientists have beaten me there.Proof positive that AL is a highly thought-provoking book.Read it!

Great Beginners book
I just loved this book. It gives the novice a very good sampling of the future of Artificial Intellegence and Artificial Life. I particularly enjoyed the chapter on the discovery of machine virus'. Somewhat dated, but an extremely good read.

My Review of this Book
I have read this book.

It is about artifical intelligence. If you have a computer you will know exactly what I mean. When you hook up a computer, it acts alive, and you gotta interact with it like it is artifically intelligent.

Like when I hook up the voice-recognition thing where you speake into the mikerofone, it acts like it hears you too, and does what it is told to do. Sometimes that is to write a letter, or to tell it to go onto the net.

I told my computer to go onto the net once thru the mike, and it did it, as it was spoken and said what to do.

So if you read and buy this book you will learn to do this, and hook it up yourself. The book has plans and charts to do all this stuff. When you read it, pass it onto a friend, and they may help you once they read it themselves.

I gave this book 5-stars, because it was a very good one, and I will now know how my computer is so smart. I told it what to do, and it help me with this revue to. So buy it but just one time, because a friend and other people will be able to read this for free, once you give it to them.

Engines are my hobbie, and so are electronic power supplys, so I plan to use this book for that to. I will design new ones that are faster than sound, and my computer will be smart and help me with that.

So buy this book, once, and you will like it along with all the friendly people that you knowe.That's my revuiew, but I will do anew one when a new adition of the book comes out to the press.

I do recomend that you buy this one time for the people who wanto know about how artifical intelligent computers get smarter and help you with life-things you need to do, but not all by yourselfe, but with a computer.

An excellent intro to a new science
While the concept of artificial life has been around at least since humans developed self-awareness, the commensurate decline of religion and rise of the scientific method was necessary for it to become a point of real debate.However, it was not until September 1987 when the event occurred that established a-life as an academic discipline, namely a conference devoted to its study.This work uses that event as a starting point, and does a superb job of presenting nearly all perspectives, including historical.
Like its counterpart, artificial intelligence, the discipline of a-life suffers from a lack of definition. There is no agreement on what life or intelligence are.Additional disagreement arises over the following distinctive descriptions of life.

(a) Objects such as rocks can be assigned a life (intelligence) value of zero and as we moveupward to humans and beyond, the measure of life (intelligence) characteristics isdescribed by a smooth, continuous function where the first derivative never becomes very large, but is always positive.There is no clearly discernible boundary between life and non-life.

(b) Starting from the same initial position as (a), the derivative stays close to zero for some time, and then suddenly becomes unbounded, as the matter now possesses the fundamental essence of life (intelligence).That point of the vertical derivative is the boundary point between animate and inanimate objects.

Much of this book deals with cellular automata and the algorithms used to create them.Like so many new, perhaps revolutionary disciplines, the major players tend to be free spirits.Many of the people described here bounced around before finding their ecological niche in a-life.With the exception of the originators, John von Neumann and John Horton Conway, those who established the study of cellular automata as an academic discipline were academic outsiders who literally created it from nothing.The explanation of that is very well done.While most of the work has been done by computer, no previous knowledge is necessary to understand the text.
One item could have been better handled, but that is largely due to the problems with definitions.Like the workers in chaos, a-lifers tend to see what they want to see.For example, simple rules are used to create an image that either looks or acts like something known to be alive and this is used to argue that life is being created or that the rules that create life are simple.Which is an extremely weak argument.What is being created are items that human eyes interpret as looking like life, and as all psychologists know, the human brain processes images with a bias towards previous experience.The devil's advocate against is a shadow here.However, it is difficult to argue in the negative when you are aiming at a nebulous target.
Whatever your interest in a-life, you will find something of value in this book.Biologists and philosophers who teach general education courses will also find a good deal of discussion material.The hypothetical qualification has been removed form the debate, as there are now objects to argue about.

Published in Journal of Recreational Mathematics, reprinted with permission

fascinating
I read this more than three years ago, before I started my undergraduate studies. I knew I was going to study computer science, but after reading this book I knew I would forever be drawn to the multidisciplinary fields of biology and computer science. From the question of the origin of life to intelligence, the book convinced me that a new approach is needed to solve these old mysteries.

It's not a masterpiece of literature, but it was interesting enough to forever change my research career. ... Read more

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The concept of artifical intelligence, the idea of something with mind-like attributes has been around foe centuries. This reference wok provides scholars with the essential material for charting the development of this controversial concept. ... Read more

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This volume constitutes the refereed proceedings of the 11th Pacific Rim Conference on Artificial Intelligence, PRICAI 2010, held in Daegu, Korea, in August/September 2010.The 48 revised full papers presented together with 21 short papers in this volume were carefully reviewed and selected from 191 submissions. The volume concentrates on AI theories, technologies and their applications in the areas of social and economic importance for countries in the Pacific Rim. ... Read more

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Proceedings of the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School, held in Prague, Czech Republic, July 2-13, 2001. Offers parts on foundations of MAS; social behavior, meta-reasoning, and learning and applications. Softcover. ... Read more

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Artificial intelligence is one of Wall street's most promising new technologies. Used to assist investment decision-making, artificial intelligence systems can handle more information, react more quickly and make more consistent decisions than a group of human experts. In Artificial Intelligence in Finance and Investing, Robert Trippi and Jae Lee thoroughly explain how artificial intelligence systems can help to improve investment returns. Completely updated, this edition also includes sections on neural network and case-based reasoning. Practical and filled with real-life examples, the book provides all the information a financial professional needs to understand and evaluate an artificial intelligence system. For investors who want to stay on the cutting edge of technology, Artificial Intelligence in Finance and Investing will be a must read. Highlights include: overview of artificial intelligence in invesment management; components of an artificial intelligence system; portfolio selection system issues; handling investment uncertainties; practice exercises with K-FOLIO, a typical aritificial intelligence system. ... Read more

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Intended both as a text for advanced undergraduates and graduate students, and as a key reference work for AI researchers and developers, Logical Foundations of Artificial Intelligence is a lucid, rigorous, and comprehensive account of the fundamentals of artificial intelligence from the standpoint of logic.

End-of-chapter bibliographic and historical comments provide background and point to other works of interest and research.Each chapter also contains numerous student exercises (with solutions provided in an appendix) to reinforce concepts and challenge the learner.A bibliography and index complete this comprehensive work. ... Read more

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In the 1950s, a new field, cognitive psychology, emerged as a dialogue between the growing capabilities of digital computers and the study of human cognition and perception. Artificial Intelligence (AI) researchers began to develop models of perception, reasoning, knowledge organization, and natural language communication. They also created neural networks, expert systems, and other software with practical applications. AI models in turn have offered provocative insights into the human mind; now, new developments in virtual community and cyberspace point toward a future in which human and computer minds will interact in increasingly complex ways. Ultimately, AI research compels us to ask what it is that makes us human. "Artificial Intelligence" presents dynamic new portraits of the men and women in the vanguard of this innovative field.Subjects include Alan Turing, who made the connection between mathematical reasoning and computer operations; Alan Newell and Herbert Simon, who created a program that could reason like a human being; Pattie Maes, who developed computerized agents to help people with research and shopping; and Ray Kurzweil, who, besides inventing the flatbed scanner and a reading machine for the blind, has explored relationships between people and computers that may exceed human intelligence. ... Read more