Books in Artificial intelligence expert systems and knowledge based systems

Automated Theorem Proving: A Logical Basis

1st Edition
August 19, 2016
D.W. Loveland
English
eBook
9 7 8 1 4 8 3 2 9 6 7 7 7

Fundamental Studies in Computer Science, Volume 6: Automated Theorem Proving: A Logical Basis aims to organize, augment, and record the major conceptual advances in automated theorem proving. The publication first examines the role of logical systems and basic resolution. Discussions focus on the Davis-Putnam procedure, ground resolution, semantic trees, general resolution procedure, basic concepts of first-order logic, refutation procedures, and preparation of formulas. The text then takes a look at the refinements of resolution, including unit preference and set-of-support, ordered clause deductions, and setting and linear refinements. The monograph tackles subsumption, resolution with equality, and resolution and problem reduction format. Topics include problem reduction format, paramodulation and linear refinements, paramodulation, and subsumption for linear and nonlinear procedures. The publication is a dependable reference for students and researchers interested in automated theorem proving.

Artificial Intelligence IV

1st Edition
January 22, 2016
P. Jorrand + 1 more
English
eBook
9 7 8 1 4 8 3 2 9 7 7 8 1

Presenting recent results and ongoing research in Artificial Intelligence, this book has a strong emphasis on fundamental questions in several key areas: programming languages, automated reasoning, natural language processing and computer vision.AI is at the source of major programming language design efforts. Different approaches are described, with some of their most significant results: languages combining logic and functional styles, logic and parallel, functional and parallel, logic with constraints.A central problem in AI is automated reasoning, and formal logic is, historically, at the root of research in this domain. This book presents results in automatic deduction, non-monotonic reasoning, non-standard logic, machine learning, and common-sense reasoning. Proposals for knowledge representation and knowledge engineering are described and the neural net challenger to classical symbolic AI is also defended.Finally, AI systems must be able to interact with their environment in a natural and autonomous way. Natural language processing is an important part of this. Various results are presented in discourse planning, natural language parsing, understanding and generation. The autonomy of a machine for perception of its physical environment is also an AI problem and some research in image processing and computer vision is described.

Automating Open Source Intelligence

1st Edition
December 3, 2015
Robert Layton + 1 more
English
Paperback
9 7 8 0 1 2 8 0 2 9 1 6 9
eBook
9 7 8 0 1 2 8 0 2 9 1 7 6

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media. As OSINT has applications in crime fighting, state-based intelligence, and social research, this book provides recent advances in text mining, web crawling, and other algorithms that have led to advances in methods that can largely automate this process. The book is beneficial to both practitioners and academic researchers, with discussions of the latest advances in applications, a coherent set of methods and processes for automating OSINT, and interdisciplinary perspectives on the key problems identified within each discipline. Drawing upon years of practical experience and using numerous examples, editors Robert Layton, Paul Watters, and a distinguished list of contributors discuss Evidence Accumulation Strategies for OSINT, Named Entity Resolution in Social Media, Analyzing Social Media Campaigns for Group Size Estimation, Surveys and qualitative techniques in OSINT, and Geospatial reasoning of open data.

Hidden Semi-Markov Models

1st Edition
October 22, 2015
Shun-Zheng Yu
English
Paperback
9 7 8 0 1 2 8 0 2 7 6 7 7
eBook
9 7 8 0 1 2 8 0 2 7 7 1 4

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.

Knowledge-Based Systems and Legal Applications

1st Edition
Volume 36
May 11, 2015
T.J.M. Bench-Capon
English
eBook
9 7 8 1 4 8 3 2 9 5 3 4 3

This book compiles the experience of the largest project in knowledge-based systems and the law yet undertaken. It provides an in-depth introduction to representation of law in computer programs, as well as more advanced discussion and description of large knowledge-based systems building, legal representation, cooperative work, and interface design in the context of the project.

Abstract Domains in Constraint Programming

1st Edition
May 6, 2015
Marie Pelleau
English
eBook
9 7 8 0 0 8 1 0 0 4 6 4 7

Constraint Programming aims at solving hard combinatorial problems, with a computation time increasing in practice exponentially. The methods are today efficient enough to solve large industrial problems, in a generic framework. However, solvers are dedicated to a single variable type: integer or real. Solving mixed problems relies on ad hoc transformations. In another field, Abstract Interpretation offers tools to prove program properties, by studying an abstraction of their concrete semantics, that is, the set of possible values of the variables during an execution. Various representations for these abstractions have been proposed. They are called abstract domains. Abstract domains can mix any type of variables, and even represent relations between the variables. In this work, we define abstract domains for Constraint Programming, so as to build a generic solving method, dealing with both integer and real variables. We also study the octagons abstract domain, already defined in Abstract Interpretation. Guiding the search by the octagonal relations, we obtain good results on a continuous benchmark. We also define our solving method using Abstract Interpretation techniques, in order to include existing abstract domains. Our solver, AbSolute, is able to solve mixed problems and use relational domains.

Social Sensing

1st Edition
March 23, 2015
Dong Wang + 2 more
English
Paperback
9 7 8 0 1 2 8 0 0 8 6 7 6
eBook
9 7 8 0 1 2 8 0 1 1 3 1 7

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.

Industrial Agents

1st Edition
March 12, 2015
Paulo Leitão + 1 more
English
Paperback
9 7 8 0 1 2 8 0 0 3 4 1 1
eBook
9 7 8 0 1 2 8 0 0 4 1 1 1

Industrial Agents explains how multi-agent systems improve collaborative networks to offer dynamic service changes, customization, improved quality and reliability, and flexible infrastructure. Learn how these platforms can offer distributed intelligent management and control functions with communication, cooperation and synchronization capabilities, and also provide for the behavior specifications of the smart components of the system. The book offers not only an introduction to industrial agents, but also clarifies and positions the vision, on-going efforts, example applications, assessment and roadmap applicable to multiple industries. This edited work is guided and co-authored by leaders of the IEEE Technical Committee on Industrial Agents who represent both academic and industry perspectives and share the latest research along with their hands-on experiences prototyping and deploying industrial agents in industrial scenarios.

Foundations of Genetic Algorithms 1995 (FOGA 3)

1st Edition
Volume 3
November 27, 2014
FOGA
English
eBook
9 7 8 1 4 8 3 2 9 5 0 2 2

Foundations of Genetic Algorithms, 3 focuses on the principles, methodologies, and approaches involved in the integration of genetic algorithm into mainstream mathematics, as well as genetic operators, genetic programming, and evolutionary algorithms. The selection first offers information on an experimental design perspective on genetic algorithms; schema theorem and price's theorem; and fitness variance of formae and performance prediction. Discussions focus on representation-indep... recombination, representation-indep... mutation and hill-climbing, recombination and the re-emergence of schemata, and Walsh transforms and deception. The publication then examines the troubling aspects of a building block hypothesis for genetic programming and order statistics for convergence velocity analysis of simplified evolutionary algorithms. The manuscript ponders on stability of vertex fixed points and applications; predictive models using fitness distributions of genetic operators; and modeling simple genetic algorithms for permutation problems. Topics include exact models for permutations, fitness distributions of genetic operators, predictive model based on linear fitness distributions, and stability in the simplex. The book also takes a look at the role of development in genetic algorithms and productive recombination and propagating and preserving schemata. The selection is a dependable source of data for mathematicians and researchers interested in genetic algorithms.

Face Detection and Recognition on Mobile Devices

1st Edition
November 25, 2014
Haowei Liu
English
eBook
9 7 8 0 1 2 4 1 7 1 2 8 2

This hands-on guide gives an overview of computer vision and enables engineers to understand the implications and challenges behind mobile platform design choices. Using face-related algorithms as examples, the author surveys and illustrates how design choices and algorithms can be geared towards developing power-saving and efficient applications on resource constrained mobile platforms.

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