Customer Reviews (1)

Of historical importance
The AM machine, developed by Doug Lenat in 1976 in his Phd dissertation, was designed to invent mathematical concepts and engage in making mathematical conjectures in elementary set theory and number theory. In the first article of this book, and the only one that will be reviewed here, Lenat summarizes AM in the context of the theme of the book. The AM machine is not viewed anymore as being interesting (no pun intended), but readers who have an interest in automated mathematical discovery should read this article, both for its insights and because of its historical importance. Many of the approaches to automated mathematical discovery that came after AM were very similar to it, both in terms of the form of the reasoning patterns and the use of extensive knowledge bases in mathematics.

As Lenat describes it in his article, AM began with 115 elementary concepts such as sets and "bags", and was able to arrive at concepts such as "subset" and "disjoint set". It was also able to formulate concepts in number theory such as prime numbers and highly composite numbers. A concept in AM is given a "frame representation", where each frame has 25 "facets" and can have multiple entries for each facet. The facets could be definitions, algorithms, or examples of a concept, or generalizations or specializations of a concept, or conjectures involving a particular concept. A collection of tasks acting on a facet of a concept, and ordered by "interestingness," were then processed by AM. A task performs a particular action on the facet by searching through its knowledge base of 242 heuristics. AM then chooses the appropriate heuristic(s) for the task, and then performs any subtasks that are suggested by the heuristic(s).

A weighting scheme, consisting of an assignment of a numerical value, is applied to concepts, the individual facets, and the actions on concepts. This scheme is used to judge whether a concept, facet, or action is "interesting" in some sense. A formula is then used to calculate the "worth" of a task, this formula being dependent on a weighted sum of these numerical values and the actual number of reasons it counted for the judgment of "interestingness". The knowledge base of heuristics in AM included a collection of heuristics to be used for deciding the interestingness of a concept. One of these heuristics was that a concept is interesting if there are interesting conjectures about it. A concept is considered uninteresting if no examples or at best only a few examples of it can be found, even after repeated attempts by AM.

Several questions arise when considering the discovery process utilized by AM. One obvious one concerns the originality of the discoveries which it made. Were the concepts truly discovered or were they hidden behind the scenes in the elementary concepts? For example, was the idea of a subset already encoded in the elementary concepts? Frequently, a human was called upon to recognize the "rediscovery" of the concept of subset, but was this really necessary? Would AM have eventually discovered the concept of a subset, if given sufficient time? This brings up the general question as to whether a human could at all times be capable of serving as a tutor or advisor to the machine. What if the concepts are too obscure or complex for human understanding or able to be assimilated by a human on a reasonable time scale?In addition, a result might be interesting from the viewpoint of the machine, but be vacuous or completely uninteresting from a human standpoint. Should then the machine be thought of as being uncreative when this occurs? In the actual use of AM, the users were able to manipulate AM to make it reason in a particular direction. On the other hand, this issues would not be troubling if viewed from the standpoint of another machine who might be doing the "coaching". Receiving help from a human or otherwise may be viewed as a bias term for the learning/discovery process. This also would take into account the fact that research in mathematics does not take place in isolation, but instead in a "community of mathematicians".

Another issue concerns the need for doing proofs in mathematics. Typically, a result, in particular an interesting result, would not be judged as such unless there was a proof given for it. This of course excludes conjectures, which are generated quite frequently in the actual practice of mathematical discovery. The making of conjectures is thus certainly thought of as something that a machine purporting to be engaged in mathematical discovery should be able to do. However, it should also be expected to do proofs of some of the discovered concepts. Proofs however were never generated by the AM machine. Does this mean that it was not exhibiting true creativity? From another standpoint, it would be advantageous for AM to be able to engage in the construction of proofs, since, as every human mathematician knows, the actual proof of a mathematical result can frequently inspire more mathematical ideas and conjectures. Thus the construction of proofs would make AM more effective as an automated discovery machine. ... Read more

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As the power of Bayesian techniques have become more fully realized, the field of artificial intelligence (AI) has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition, but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' Web site. ... Read more

Customer Reviews (3)

Very good introduction in causal Modeling
The book by Korb and Nicholson is very readable and structured. Starting with some background information in statistics it comes directly to the major topic of the book - bayesian networks. The theory thereof is nicely evolved and applied to small examples to demonstrate its usage. Each chapter finishes with a short summary and bibliographical notes for further readings.

In my opinion this book is well written and the chosen examples are insightful. What I do not like is part three of the book which is devoted to case studies and praktical examples. If this space had been used for the first two parts by providing more details, e.g., for the discussion of path models (which is given but only short), this book could be even great on a more advanced level. In this form it is very good as an introduction in Bayesian Networks and related topics like the larger class of causal models.

Excellent Introductory Text
It is difficult to assess a review without understanding the biases of the reviewer.I fall under the category of researcher/practitioner when it comes to reasoning with graphical models.I am familiar with and make use of several books and papers on this topic in my work.Of the set of standard references (Pearl, Jensen, Neapolitan, Jordan, Cowell et al., Borgelt & Kruse) the text by Korb and Nicholson (K&N) stands out in terms of its clarity and accessibility.Does the book have everything one would ever want to know about Bayesian inference?Not by a long shot.Is it, however, a good place to start?Definitely.The basic concepts are presented relatively completely and with clarity.I consistently recommend K&N over other alternatives to colleagues new to the field.Is there a chasm separating concept and algorithm in the book?I don't think there is, especially relative to other references.With tools such as Kevin Murphy's BNT, or Netica available on the Web, it seems to me that providing a solid conceptual framework becomes paramount for a textbook such as this.I believe K&N succeed admirably in this sense.Why four stars and not five?Even for an introductory text such as K&N, it would be nice to have more development of some concepts such as causality, context specific independence, or loss of independence in dynamic nets.Although it won't be your last book on reasoning with graphical models, K&N should probably be your first.

Bayesian Networks for Undergrads and Practicioners
Despite its name "Bayesian Artificial Intelligence" covers Bayesian network (BN) techniques only.Other Bayesian techniques useful for AI are not treated.
The content is divided in three main sections: (1) The basics of probabilistic reasoning with BNs, (2) Causal discovery (finding BNs from data), and (3) "Knowledgeengineering".
The first part covers the fundamental concepts and algorithms around BNs and (simple) decision networks.It is well-written and clear, but readers who are not totally new to the field might find only little new information (e.g., loopy belief propagation, continuous densities, large decision networks, etc. are not covered).
The second part is on how to deduce causal relationships from observational data.Constrained-based and Bayesian approaches are covered, but on a rather general level.I am not sure how easy it is to implement the algorithms from the descriptions provided.When it comes to details of the algorithms, proofs, or mathematical background the authors very often refer to the literature due to "lack of space".From a practical standpoint, it is unfortunate that the different methods are compared to each other only superfiscially.For instance, one method presented performs a large number of statistical tests; one would expect that this requires large amounts of data in order to avoid false positive results.Is this a problem? With questions like these the reader is often left alone.
I am not competent to talk about part three (knowledge engineering), so I end with my general impression of the book: I would have appreciated if the authors had treated some the algorithms in greater detail and had spent a few pages on advanced concepts and current research directions.On the other hand, some information provided could have easily been left out.(For instance, how to download and install certain software packages from the internet, Kevin Murphy's well-known survey on BN software packages, screenshots of user dialogs, etc. just eat pages.Providing the URLs to the corresponding sites on the internet is completely sufficient, and the information there is more likely to be up-to-date.)The saved pages could then be spent on information which is not readily available elsewhere.
To summarize: The book provides a mostly well-written general overview of the basic concepts and could serve as a first introduction to the field. However, it leaves the reader often alone when it comes to the mathematical background, potential practical pittfalls, or advanced algorithms. ... Read more

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This book constitutes the thoroughly refereed post-proceedings of the Third International Workshop on Multi-Agent-Based Simulation, MABS 2002, held as part of the AAMAS 2002 conference in Bologna, Italy, in July 2002.

The 12 revised full papers presented together with an invited paper were carefully selected during two rounds of reviewing and improvement. The papers are organized in topical sections on emergence, alliances, and groups; MABS platforms and languages; and MABS applications.

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This monograph provides a comprehensive survey of the different approaches to coordination in societies of artificial an human agents. Setting out from a critical assessment of the state of the art, the author develops a method of structuring multi-agent applications with a mechanism called structural cooperation. Agents are equipped with expertise about their environment in order to detect and overcome specific types of problem, they make use of their social knowledge to mutually adjust their activities, and they are coerced toward coherent collective behavior through normative rules. The proposed model is formalized theoretically within game theory and realized by means of an agent architecture. It is assessed experimentally by building a prototype of a distributed decision support system for road traffic management and compared to an alternative model based on a centralized architecture. ... Read more

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THIS EDITION IS INTENDED FOR USE IN SCHOOLS AND LIBRARIES ONLY. ... Read more

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This volume presents selected papers from KBCS '89, which is the second in a series of annual conferences hosted by the Knowledge Based Computer Systems Project funded by the Government of India with United Nations assistance. The papers are grouped into sections including: - AI applications - computer architecture and parallel processing - expert systems - intelligent tutoring systems - knowledge representation - logic programming - natural language understanding - pattern recognition - reasoning - search - activities at the KBCS Nodal Centres. ... Read more

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This book presents a collection of thoroughly refereed papers drawn together from three meetings on multi-agent systems. Five of the tutorial lectures included were presented at the ACAI/EASSS 2001 summer school on MAS, held in Prague, Czech Republic, in July 2001; seven revised reviewed student papers dealing with various aspects of MAS are included as well. A workshop on Adaptability and Embodiment using MAS, AEMAS 2001, also held in Prague, Czech Republic, concurrently with the ACAI/EASSS summer school, is represented by three papers. Finally, a further nine papers were selected from an International Workshop on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2001, held in Munich, Germany, in September 2001. ... Read more

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This book constitutes the refereed proceedings of the 10th International Workshop on Cooperative Information Agents, CIA 2006, held in Edinburgh, UK in September 2006. The 29 revised full papers presented together with four invited papers were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections.

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This book constitutes the third volume documenting the results achieved within a priority program on spatial cognition funded by the German Science Foundation (DFG). The 23 revised full papers presented went through two rounds of reviewing and improvement and reflect the increased interdisciplinary cooperation in the area. The papers are organized in topical sections on routes and navigation, human memory and learning, spatial representation, and spatial reasoning. ... Read more

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This book constitutes the refereed proceedings of the 5th International Workshop on Cooperative Information Agents, CIA 2001, held in Modena, Italy, in September 2001.
The 12 revised full papers presented together with 3 full papers and 2 abstracts reflecting invited contributions were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on personal assistance: interaction and avatars, information search and recommendation, data warehousing and data mining, collaboration and coordination, and information agents for mobile and wireless environments. ... Read more

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This monograph presents the author's studies in musicrecognition aimed atdeveloping a computer system forautomatic notation of performed music. The performance ofsuch a system is supposed to be similar to that of speechrecognition systems: acoustical data at the input and musicscoreprinting at the output.The approach to pattern recognition employed is thatofartificial perception, based on self-organizing input datain orderto segregate patterns before their identificationby artificial intelligencemethods. The special merit of theapproach is that it finds optimal representations of datainstead of directly recognizing patterns. ... Read more

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Descriptions given include precise definitions, complete algorithms and thorough complexity analysis. Serves as a basis for future research. Softcover. ... Read more

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Issues concerning computational properties are solved by exploiting a broad variety of results and methods from logic and theoretical computer science. Complete classification of computation properties for all fragments of RCC8 is included. Softcover. ... Read more

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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.

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This book presents a coherent state-of-the-art survey on the area of systematic and cognitive musicology which has enjoyed dynamic growth now for many years. It is devoted to exploring the relationships between acoustics, human information processing, and culture as well as to methodological issues raised by the widespread use of computers as a powerful tool for theory construction, theory testing, and the manipulation of musical information or any kind of data manipulation related to music. The book comes with a CD providing sound examples for various chapters; it contains a comprehensive name and subject index and the following chapters: Gestalt theory revisited, from pitch to harmony, from rhythm to expectation, from timbre to texture, from musical expression to interactive computer systems. ... Read more

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This book constitutes the refereed proceedings of the First International Joint Conference on Automated Reasoning, IJCAR 2001, held in Siena, Italy, in June 2001. The 37 research papers and 19 system descriptions presented together with three invited contributions were carefully reviewed and selected from a total of 112 submissions. The book offers topical sections on description, modal, and temporal logics; saturation based theorem proving, applications, and data structures; logic programming and nonmonotonic reasoning; propositional satisfiability and quantified Boolean logic; logical frameworks, higher-order logic, and interactive theorem proving; equational theorem proving and term rewriting; tableau, sequent, and natural deduction calculi and proof theory; automata, specification, verification, and logics of programs; and nonclassical logics. ... Read more

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Over the years numerous attempts have been made to show that human intelligence is related to some quality, feature, or ability that machines cannot possibly have. In this unique study, Dr, Goldkind reconstructs and analyzes the principal arguments of this kind that have not received adequate treatment in the past and responds to each of them in detail. Among the questions explored are whether machines can engage in purposive behavior, what the relationship is between causal and purposive explanations of behavior, whether machines are capable of "human error," and whether they can perform activities and functions such as natural language understanding and dealing with contexts. Dr. Goldkind concludes that none of the arguments succeeds in proving that machines must lack the specific abilities or qualities that are posited as uniquely human. ... Read more

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This book is based on the author's PhD thesis which was selected during the 1993 ACM Doctoral Dissertation Competition as one of the three best submissions. This monograph investigates the problem of selecting adequate models for reasoning about physical systems and applications to engineering problem solving. An elegant treatment of both the theoretical and practical sides are presented: the problem is precisely formalized, its computational complexity is analyzed in detail, and an efficient algorithm for finding adequate models is derived; on the practical side, a methodology for building systems that automatically construct adequate models is provided, and implementational aspects and tests are described. ... Read more

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Speech technology, the automatic processing of (spontaneously) spoken language, is now known to be technically feasible. It will become the major tool for handling the confusion of languages with applications including dictation systems, information retrieval by spoken dialog, and speech-to-speech translation. The book gives a throrough account of prosodic phenomena. The author presents in detail the mathematical and comnputational background of the algorithms and statistical models used and develops algorithms enabling the exploitation of prosodic information on various levels of speech understanding, such as syntax, semantics, dialog, and translation. Then he studies the integration of these algorithms in the speech-to-speech translation system VERBMOBIL and in the dialog system EVAR and analyzes the results. ... Read more

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Artificial Intelligence is one of the most fascinating and unusual areas of academic study to have emerged this century. For some, AI is a true scientific discipline, that has made important and fundamental contributions to the use of computation for our understanding of nature and phenomena of the human mind; for others, AI is the black art of computer science. Artificial Intelligence Today provides a showcase for the field of AI as it stands today. The editors invited contributions both from traditional subfields of AI, such as theorem proving, as well as from subfields that have emerged more recently, such as agents, AI and the Internet, or synthetic actors. The papers themselves are a mixture of more specialized research papers and authorative survey papers. The secondary purpose of this book is to celebrate Springer-Verlag's Lecture Notes in Artificial Intelligence series. ... Read more