Book Description
The fifth edition of this book continues to provide a balanced perspective on the language schools, theories, and applications of artificial intelligence. These diverse branches are unified through detailed discussions of AI's theoretical foundations. The book is broken down into six parts to provide readers complete coverage of AI. It begins by introducing AI concepts, moves into a discussion on the research tools needs for AI problem solving, and then demonstrates representations for AI and knowledge-sensitive problem solving. The second half of the book offers an extensive presentation of issues in machine learning, continues presenting important AI application areas, and presents Lisp and Prolog to the reader. This book is appropriate for programmers both as an introduction to and a reference of the theoretical foundations of artificial intelligence.
Customer Reviews:
Superficial and unclear.......2005-05-27
Trying to gather the greatest audience possible, this book is superficial, completly unclear and boring. Why? Topics are quickly introduced, concepts are rarely analized deeply, it's more discorsive than formal. With so many subjects of AI in the same book not enough space can be given to all of them, so most of the chapters are lists of important algorithms or concepts, barely explained. Do you want to verify it? See the table of contents and the number of pages, and try to see how much space can be given to every point... not enough.
Fantastic Introduction to AI.......2005-01-06
This book really stands out among the AI texts (I've read 4 others). First, the language is clear and simple enough for undergrads to grasp. Second, there are consistent examples that pervade the text to help the reader apply each method to an established problem. Third, the explanations of algorithms/structures are crafted and phrased to TEACH, not merely to summarize a bunch of material for reference purposes. Finally, the programming chapters allow the student to realize the material, and really think about the problems by implementing them and hashing out the details.
I cannot complain about any lack of depth - the length already exceeds 900 pages. To those that desire more, look into academic journals - this is an intro. Moreover, robotics, vision, neural nets, and other topics already have their own "forked" research fields, with textbooks of comparable length focusing on those topics alone!
Enjoy! This text is sure to get you started!
this book not cover much.......2003-07-14
I bought this book for my introduction course in AI. I feel that this book has lack of somethings which are very important, neural networks, and Ai and robotics to name a few. I found that the text is very hard to understand. Again he didn't use enough example to explain some of the topics. I am lost reading this book. The book is not well structured and turned me bored after 30 minutes reading it. The reason are, AI term definations are not included as other book do, few visual diagrams, objective is not well defined. Once again, he didn't include introduction/review of what we acpect to learn of each of every chapters. Reading it is like reading a "white bible". Only plain text and unprofessional layout. This book discorage me reading it. I think i should buy other book that have a wider coverage topics in AI and yet easy to understand, consistent with my AI course syllibus and yet easy for my eyes.
Good For Beginners in AI.......2002-12-05
This is a very good book for anyone wanting to get an insight. Good for the first college course in AI too. It introduces the different areas of AI quite well, and develops logic before doing that. Prolog and LISP are also introduced.
The only reason I wouldn't give this book 5 stars is because
1) The Prolog and LISP features aren't all that great. They could have done better than just explaining what they did.
2) There was very little or almost no depth in the material covered. I wanted to go on reading more about the advanced features, but that never happened. So, I had to go to the library and look for something there.
But a great book for a college course. I wouldn't recommend this for a Grad course in CS...A grad student should be knowing beyond what this book covers.
Don't miss it!.......2002-05-24
This is the best general AI book I've seen this far. It introduces all the popular branches of AI clearly. If you are serious about AI, you should own this book...
Book Description
This book illustrates how organizations can make better, faster decisions about their customers, partners, and operations by turning mountains of data into valuable business information that's at the fingertips of decision makers. It describes what's involved in using business intelligence to bring together information, people, and technology to create successful business strategies-and how to execute those strategies with confidence. Real-life case studies show how world leaders in finance, manufacturing, and retail have successfully implemented business intelligence solutions and detail the benefits they have reaped.
Customer Reviews:
Good read if you are a non techie.......2006-03-29
The book was a very easy read. Finised it in one afternoon. Definitely recommended for a novice. However, if you have an understanding of BI, then this book is not for you.
I like the cover. Its orange !!!
A great primer.......2005-03-30
First of all, I will have to admit that I am a Microsoft advocate. I like their solutions and I think very highly of what they have to offer with SQL Server and Analysis Services.
This book lays down a good foundation for anyone to follow. It explains the concept of BI, the uses of BI, and the payback of BI. What more do you want.
I have been in charge of an SAP/BW group for a large consumer electronics company for the past 4 years. SAP's architecture for BI is very expensive, inflexible, and limited. Using Microsoft's concepts of BI would be cheaper, very flexible, with much more capabilities.
So, grab this book, read it, then read it again. Install SQL Server 2k. Install Analysis Services (comes with SQL Server 2k) and install SQL Servers Service Packs 1-3).
Then experiement with what they are telling you in this book and you will be amazed at what you can do....and cheaply!!!
Good Luck!
Concise, Practical and Inspiring Advice.......2004-01-10
Techies will enjoy learning from real world examples of business intelligence technologies. Business leaders will appreciate how complex technical and business topics are tackled from various perspectives - what is BI, how BI will help your organization, and the most helpful chapter, how to actually identify, start and implement a BI solution.
Only wish the authors had spent a little more time identifying pitfalls, but that is why you hire experts to help you out.
Book Description
Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.
Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming.
* Explains how the success of genetic programming arises from seven fundamental differences distinguishing it from conventional approaches to artificial intelligence and machine learning
* Describes how genetic programming uses architecture-altering operations to make on-the-fly decisions on whether to use subroutines, loops, recursions, and memory
* Demonstrates that genetic programming possesses 16 attributes that can reasonably be expected of a system for automatically creating computer programs
* Presents the general-purpose Genetic Programming Problem Solver
* Focuses on the previously unsolved problem of analog circuit synthesis, presenting genetically evolved filters, amplifiers, computational circuits, a robot controller circuit, source identification circuits, a temperature-measuring circuit, a voltage reference circuit, and more
* Introduces evolvable hardware in the form of field-programmable gate arrays
* Includes an introduction to genetic programming for the uninitiated
Customer Reviews:
READ IT BEFORE REVIEWING PLEASE.......2004-07-06
my five stars are just to counteract the single star from the idiotic reviewer who gave the book one star w/o even bothering to read it. i haven't read this volume yet either, but i just ordered it and it's on its way; i'm certain i won't be disapointed. i'm a programmer and an artist and i use GP effectively to evolve forms both sonic and visual. just because you know your field(s) doesn't mean you can't benefit from a knowledge of evolutionary algorithms, quite the contrary. we have koza and friends to thank for a lot of inspirational work. i for one enjoy the interaction that i have with my algorithms, and since i'm the fitness function, forms crop up which never would have if either i or the machine were working alone. apologies for being guilty of the same crime as said reviewer but i feel in this case it's justified.
And the future is..........2002-03-14
Genetic programming is like a new Big Bang in computer universe.
Reach the automatic programming level is a revolution that will affect the way things are done today.
In a very cientifyc way, the book shows all the aspects of how to get ready for this evolution.
Why Should You Buy This Book???.......2002-01-26
Why this book, when there are several shorter books on GP, and its principle author, John Koza, has written two other, more general and equally voluminous books on GP? This book addresses how to evolve program architecture, that's why! Living organisms didn't grow arms and eyes through simple mutation. It required more subtle genetic operators. Traditional genetic operators (as used in genetic algorithms) may be sufficient for evolving solutions to optimization problems where the structure if not the specifics of each solution is pretty much the same. But to effectively evolve program structures, you need architecture altering genetic operators. This book provides motivations in computer science, foundations in biology, and explanations in English.
A hint of the future............2001-07-28
The authors have written a fine book here and it has and will continue to be a source of good information on the subject. What is most interesting about the approach of genetic programming is that it does not make use of the inference methods of formal logic in the search for the correct program. Correctly observing that logical thinking is insufficient for invention and creativity, the authors follow the "logic considered harmful" philosophy in their attempts to get a computer to find a creative/original solution to a problem. And most importantly, they discuss fourteen examples where genetic programming has produced results that are competitive with human-produced results. The book is almost 1200 pages long, but without reading all the examples one could cover the main points in a reasonable time frame. The reader knowing the LISP language will appreciate the discussion more.
After a brief introduction to the book in chapter 1, the authors move on to a detailed discussion of the philosophy and approaches used in genetic programming. They list the five steps that must be done before applying a genetic algorithm to a problem and give an overview of the LISP background needed to understand genetic programming. The authors emphasize that the genetic algorithm is probabilistic in nature, with the initial populations, individual selection, and genetic operation chosen at random. They give flowcharts illustrating a typical genetic algorithm and program, and then show executable programs can be automatically created. A very extensive list of references on genetic programming is given at the end of the chapter.
In the next part, the authors discuss how to eliminate the requirement that the programmer specify the architecture in advance to the program to be created. After reviewing some methods that were previously used to make the choice of architecture, the authors move on to describing a set of architecture-altering operations that give an automated method for determining the architectures of evolving programs. The discussion on automatically defined recursion is particularly interesting.
The book then shows how to use the results so far to allow problem-solving to be done using genetic programming, the first one being the rotation of automobile tires and the second being evolving a computer program with the behavior of Boolean even-parity functions. This is followed by a discussion of how to use architecture-altering operations to solve a time-optimal control problem. The most interesting part of this discussion is that it illustrates the important point that disadvantageous actions should be taken in the short term so that the long-term objective can be achieved.
In chapter 14, the ant foraging problem is used to illustrate a form of the (Minsky) multiagent problem and architecture-altering operations. This is followed by discussions on the digit recognition problem and the transmembrane segment identification problem. The authors choose the Fibonacci sequence to illustrate how recursion can be used in solving problems with genetic programming. The necessity of using internal storage is illustrated using the cart centering problem.
The authors then overview the use of the Genetic Programming Problem Solver (GPPS) for automatically creating a computer program to solve a problem. Several problems are examined using this Solver, such as symbolic regression, sorting networks, and the intertwined spirals problem.
The next part then considers the application of genetic programming to the automated synthesis of analog electrical circuits. The authors judge, rightfully, that the design process is one that will be a good judge of automated technique versus one that was done by humans, especially considering the fact that analog design is considered by many to be an "art" rather than a "science". The authors show how to import the SPICE simulation system into the genetic programming system, and discuss how validation of circuit design using this simulator would be done by the genetic programming system. After showing how a low-pass filter may be successfully designed using the genetic programming system, the authors show how with a few changes it can be used to design many different types of circuits. Interestingly, the authors cite the rediscovery by genetic programming of the elliptic filter topology of W. Cauer. Cauer arrived at his discovery via the use of elliptic functions, but the genetic program did not make use of these, but relied solely on the problem's fitness measure and natural selection!
An interesting discussion is also given of the role of crossover in genetic programming by comparing the problem of synthesizing a lowpass filter with and without using crossover. The authors conclude that the crossover operation plays a large contribution to the actual solution of the problem.
Then later, the authors show how genetic programming actually evolved a cellular automata that performs better than a succession of algorithms written by humans in the last two decades. Specifically, they show how genetic programming evolved a rule for the majority classification problem for one-dimensional two-state cellular automata that exceeds the accuracy of all known rules.
Most interestingly, the authors show how genetic programming evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family.
The actual performance and implementation issues involved in genetic programming are discussed in the last two parts of the book. They discuss the computer time needed to yield the 14 instances where they claim that genetic programming has produced results that are competitive with human-produced results.
The authors wrap things up in the last chapter of the book and discuss other instances where genetic programming has succeeded in automatically producing computer programs that are competitive with human-produced results. The evidence they have in the book is impressive but there are a few areas that will be ultimate tests of this approach, the most important being the discovery of new mathematical results or algorithms. It is this area that requires the most creativity on the part of the inventor.
Can computers be creative?.......1999-12-03
The quest for *Automatic Programming* is the holy grail of artificial intelligence. The dream of having computer programs write other useful computer programs has haunted researchers since the nineteen fifties. In Genetic Programming III -Darwinian Invention and problem solving (GP3) by John R. Koza, Forest H Bennet III, David Andre and Martin A Keane, the authors claim that the first inscription on this trophy should be the name Genetic Programming (GP). GP is about applying evolutionary algorithms to search the space of computer programs. The authors paraphrase Arthur Samuel of 1959 and argue that with this method it is possible to "tell the computer what to do without telling it explicitly how to do it".
The main hypothesis of the book is that GP is not only the first instance of true automatic programming but also creative to such an extant that it competes with humans in solving very hard problems and therefore the solutions produced by GP can sometimes be called inventions, thus the name "Darwinian Invention Machine". The book starts by listing sixteen proposed attributes of any automatic programming system. The attribute list begins with obvious properties such as the ability to produce entities that can run on a computer, continues by describing components of full computer programs and ends by expressing fuzzier concepts such as applicability, scalability and competitiveness with human-produced results. The authors argue that GP definitely has most of the 16 attributes and at least to some extent possesses the remaining few. The last attribute, human competitive results, is in turn defined by a list of eight properties where each of them gives enough evidence to conclude competitiveness to results produced by the intellect of a human. This list includes concepts such as whether the results are pantentable, publishable in scientific journals or better then best known human solutions. GP3 reports 14 experiments by the authors where the they claim that GP produced results fulfilling one or more of these properties and thus are competitive with that of a skilled human such as an engineer, mathematician, designer or programmer. Examples of results with the "darwinian invention quality" include sorting networks, analogue electrical circuit synthesis and creation of motifs for protein family detection. Pointers are also given to human competitive solutions evolved by other researchers.
Overall there is no question that this is an important book putting the spotlight on one of the peak performing and most promising candidates for the general AI prize. There is no doubt that this book belongs in the standard library of all GP researchers or practitioners. This volumous book is a bit heterogeneous, probably stemming from the fact that is combined from a number of previously published papers with some new material. On the other hand is the volume important documentation of innovative work done by John Koza and his colleagues. In many place numerous pointers to work by other researchers are given but in the end I believe that the book would have a stronger case for presenting the GP state-of-the-art by including more references to similar research by other research groups.
However most important and intriguing thing about this book is the provocative questions raised concerning definitions and claims of human competitive performance, "Darwinian invention" and artificial intelligence - particularly whether we have already passed an important milestone in the history of AI - automatic programming.
Book Description
Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to support practical decision-making. Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds of models, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models.
Customer Reviews:
Great start to bridge insights from complexity science into business applications.......2007-04-16
'Managing Business Complexity' is a great start at bridging the significant academic work in complexity science over recent years to business applications. It provides an introduction to agent-based simulations and an overview of the characteristics and advantages of agents, and walks the reader through two illustrative examples. Such examples are especially helpful to readers with little or no previous experience using agent-based models.
Applications of complexity science to business are still in their infancy. As such, the discussion in the book leans towards the academic, and more pointedly towards programmers rather than business practitioners with scant scientific background. Future editions of this book might benefit from including further business applications, and including the impact to bottom line.
This book is highly recommended as an overview of this exciting subject and its applicability to business situations.
Average customer rating:
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Intelligent Desicion -making Support Systems: Foundations, Applications and Challenges (Decision Engineering)
Gupta J.N.
Manufacturer: Springer
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Binding: Hardcover
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ASIN: 1846282284 |
Book Description
Decision-making Support Systems (DMSS) are computer-based systems that support individual or organisational decision-making processes. Recent advances in information technology and artificial intelligence are enhancing these systems and giving rise to intelligent-DMSS.
Intelligent Decision-making Support Systems: Foundations, Applications and Challenges is the first book to provide integrated coverage of the technical aspects of intelligent Decision-Making Support Systems together with discussion of their application and evaluation in organisational structures.
The book brings together up-to-date information on the theory and application of i-DMSS. Readers will learn about the foundations, architectures, methods and strategies for successfully designing, developing, implementing, and evaluating intelligent Decision-making Support Systems.
Intelligent Decision-making Support Systems: Foundations, Applications and Challenges will be of value to researchers in AI and management studies interested in the latest thinking in decision-making, as well practising managers and consultants who are involved with putting advanced information technologies into practice in organisations.
Book Description
The book is a short, concise and complete presentation of constraint programming and reasoning. The use of constraints had its scientific and commercial breakthrough in the 1990s. Programming with constraints makes it possible to model and solve problems with uncertain, incomplete information and combinatorial problems, as they are abundant in industry and commerce, such as scheduling, planning, transportation, resource allocation, layout, design and analysis. The theoretically well-founded presentation includes application examples from real life. It introduces the common classes of constraint programming languages and constraint systems in a uniform way. Constraint solving algorithms are specified and implemented in the constraint handling rules language (CHR).
This book is ideally suited as a textbook for graduate students and as a resource for researchers and practitioners. The Internet support includes teaching material, software, latest news and online use and examples of the CHR language.
Book Description
The Knowledge Evolution offers a unique and powerful road map for understanding knowledge creation, learning, and performance in everyday work. This book reframes current thinking by delving into the hidden world of knowledge supporting both individual and organizational performance, laying the foundation for the emerging art of knowledge management. Packed with best practices from leading edge companies, essential guidelines, design principles, analogies, and conceptual frameworks, it serves as a practical guidebook for mastering the Knowledge Era. It will help managers make more intelligent decisions about knowledge creation, reduce wasteful technology investments and lead to new ease and confidence in applying knowledge and learning principles for themselves and for their organizations.
Verna Allee delves into current thinking and practice to unravel the genetic code of knowledge itself. This revolutionary approach has surfaced a simple and elegant knowledge archetype. She demonstrates how this archetype can help us deal with complexity and suggests ways of self-organizing that make profound sense in today's networked enterprises. From strategies for core knowledge competencies to the key components of individual expertise, The Knowledge Evolution zeroes in on the critical success factors for the knowledge-based enterprise. What emerges is an approach to knowledge management that is simple enough to communicate at every level of the organization, yet rich enough to encompass all the complexity of modern enterprises.
Verna Allee is the founder of Integral Performance Group, a consulting practice in California that specializes in the learning organization, knowledge competencies, organizational systems change, systems thinking, total quality and learning, benchmarking support, best practices research, and strategic development. She holds a degree in the Study of Human Consciousness and her work is informed by a deep interest in intelligence, human development, cognition, intuition and consciousness. She is the author of Learning Links: Enhancing Individual and Team Performance, Pfeiffer and Co-Jossey Bass, 1996.
Explains the best practices from leading edge companies.Contains a learning guide for TQM tools.Gives navigational aids for the Knowledge Era.
Customer Reviews:
A good book for leaders of knowledge-rich organizations........1999-05-08
It should come as no surprise that without knowledge, systems fail. In The Knowledge Evolution, Verna Allee focuses on an analysis of the role knowledge plays in contributing to the success of an organization. According to Allee, knowledge becomes of greater and greater importance as the validity and strength of an organization is called into question. Without a doubt, the availability of a strong knowledge base has, in many corporations, played a fundamental role in the company's survival.
Presently, workers at numerous companies are struggling with the concept of how knowledge becomes a driving influence within their organization. Because it is intangible and hard to grasp a common understanding of what organizational knowledge is, leaders are finding it hard to capture, organize, and use knowledge as a tool. Verna attempts to establish a frame-work which lends itself to this understanding and provides today's corporate leaders with the tools to better make decisions regarding organizational knowledge.
Allee provides a general understanding of how advancements in technology define corporate culture and how learning is effected by the widespread use of available technologies. Community relationships are also influenced by technology and leaders are being forced to rethink how organizations are structured and how knowledge is used to influence decision-making processes.
Current conditions indicate that the future of companies -- how well prepared they will be to compete in the global economy -- will be determined by how well they understand and use knowledge. Allee provides a management road-map to get companies where they want to be regarding knowledge.
Like it or not, the Knowledge Evolution is upon us. Allee identified some effective ways organizations should create, adapt and apply knowledge. This book is essential reading for leaders who want to win!
Comprehensive, introductory text........1999-05-03
At times, The Knowledge Evolution delved too much into semantics and ontology, in an attempt to develop a "common language". At the time of publication (1997) this may have been relevant but the prevalence of "knowledge management glossaries" reveals that indeed a common language has emerged, making this specific content a bit of an arduous read. Perhaps a second edition is needed, especially since the proprietary value of this book is still not being addressed in current knowledge management (KM) literature. Current KM books almost exclusively focus on data, information and knowledge (DIK). The Knowledge Evolution visions past DIK and adds complex dimensions of meaning, wisdom and philosophy to the knowledge framework.
If you do purchase this selection, I recommend writing unabashedly in the margins, or keeping a notebook on hand, connecting the theoretical constructs of knowledge and it's management discussed in the book to a personal context. By and large the scope of the book is very broad, looking inclusively at the field of KM. Since the writing of this book, specific sub-fields within KM have emerged i.e. data mining, group memory technologies, knowledge mapping etc. A reorganization of the content in this book along those lines (i.e. "procedural knowledge" with data mining, "functional knowledge" with knowledge mapping) was a worthwhile endeavor of this reader and would be a tremendous value-add to a second edition.
By and large The Knowledge Evolution is a readable and comprehensive introductory text to the field of KM. The book is broad and encompasses so much of the theory and principles behind KM that the actual industry has yet achieve or implement everything discussed. With the enormous influx and popularity of KM books in the past two years I hadn't heard anything about The Knowledge Evolution and now having read it, I realize the book that all these current authors must have also read.
Excellent Framework for building and creating knowledge........1999-04-03
An excellent treatment of the subject matter. Provided insigts on knowledge learning and performance. Prepares us for the knowledge era, where knowledge is king. I wish she had included some material on creativity, having knowledge is nice but being creative is better.
A penetrating look at the knowledge-organization connection........1999-02-14
Taking an organic systems view, the author explores how organizations acquire, manage, and transfer knowledge. Covers: the nature, role and order of knowledge; best practices; types of organizational structures that support knowledge sharing; technologies for knowledge building; developing knowledge strategies; and efforts to measure results. An very impressive gathering and integration of ideas about organization in the Knowledge Era. A first-rate book on a cutting-edge subject.
Impressive integration of theory, innovation & practicality!.......1999-01-23
Verna quickly brings us all up to speedon the field of knowledge. As she reviews it, she compares and contrasts the current working theories and then integrates them into a working template that links knowledge, learning and performance. This template/model allows us to quickly apply the information to the organizations in which we live or to which we consult. She presents a developmental process for the different levels of knowledge that compares in scholarship and in practical use with Piaget's developmental intelligence, and Kolbergs developmental morality. She then follows up with 6 practical chapters where she more fully explores each level, helping us all to identify where our organization is, where it should be, and some methods to use to get us where we need to be. She writes in an exciting, clear and creative style giving us the basis for understanding more thoroughly what has gone before (giving consistent credit to the many minds that have worked in this field), what links knowledge management does and should have with other crucial systemic concepts such as systems thinking, organization learning, and organizational sustainability, and including us in the innovative ideas that occurred to her as she was doing such scholarly research. The bibliography and references are worth the price of admission themselves.
Average customer rating:
- goes beyond the idea of a probability distribution
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Graphical Belief Modeling
Russell G. Almond
Manufacturer: Chapman & Hall/CRC
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Binding: Loose Leaf
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Book Description
This innovative volume explores graphical models using belief functions as a representation of uncertainty, offering an alternative approach to problems where probability proves inadequate. Graphical Belief Modeling makes it easy to compare the two approaches while evaluating their relative strengths and limitations. The author examines both theory and computation, incorporating practical notes from the author's own experience with the BELIEF software package. As one of the first volumes to apply the Dempster-Shafer belief functions to a practical model, a substantial portion of the book is devoted to a single example--calculating the reliability of a complex system. This special feature enables readers to gain a thorough understanding of the application of this methodology. The first section provides a description of graphical belief models and probablistic graphical models that form an important subset: the second section discusses the algorithm used in the manipulation of graphical models: the final segment of the book offers a complete description of the risk assessment example, as well as the methodology used to describe it. Graphical Belief Modeling offers researchers and graduate students in artificial intelligence and statistics more than just a new approach to an old reliability task: it provides them with an invaluable illustration of the process of graphical belief modeling.
Customer Reviews:
goes beyond the idea of a probability distribution .......2006-05-15
A belief function is a step beyond traditional probability distribution functions. The latter describe uncertainty. But, by definition, you somehow know precisely that a given pdf is a correct description of a process. A belief function tries to express the imprecision in knowledge about a pdf.
The book is an advanced treatment of how graphical methods can be used to aid in the construction of belief models. Be aware though that the book has complicated ideas drawn from set theory and graph theory. With rigourous derivations of theorems.
Usages are also stressed. Examples are given of how to model failure rates in systems of many parts. Where a traditional probabilistic analysis may be far too difficult or labourious.
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Expert Systems and Artificial Intelligence in Decision Support Systems
Manufacturer: Springer
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ASIN: 9027724377 |
Books:
- Bear Encounter Survival Guide
- Beating the CSET! Methods, Strategies, and Multiple Subjects Content for Beating the California Subject Examinations for Teachers (Subtests I-III) (Boosalis Series)
- Biology, Sixth Edition
- Buffy the Vampire Slayer Season 8, Issue 1
- Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM
- Cable News Confidential: My Misadventures in Corporate Media
- Classroom Assessment & Grading That Work
- Competing on Analytics: The New Science of Winning
- Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
- Consilience: The Unity of Knowledge
Books Index
Books Home
Recommended Books
- Against All Odds: My Story
- The Strongest NIV Exhaustive Concordance
- The Hiding Place: A Novel
- The Lost German Slave Girl: The Extraordinary True Story of Sally Miller and Her Fight for Freedom i
- The Measure of a Man: A Spiritual Autobiography
- Structural Equation Modelling: A Bayesian Approach
- The Spirituality of Imperfection: Storytelling and the Search for Meaning
- Her Little Majesty: The Life of Queen Victoria
- The Conquest of Labor: Daniel Pratt and Southern Industrialization
- General Hieu, Arvn: A Hidden Military Gem
Artificial Intelligence: A Modern Approach (2nd Edition)
AI Application Programming (Programming Series) (Programming Series)
Introduction to Artificial Intelligence: Second, Enlarged Edition
Artificial Intelligence (3rd Edition)
Database Management Systems
