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Author: Lorenza Saitta
Publisher: Springer Science & Business Media
ISBN: 1461470528
Category : Computers
Languages : en
Pages : 484
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Book Description
Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.
Author: Lorenza Saitta
Publisher: Springer Science & Business Media
ISBN: 1461470528
Category : Computers
Languages : en
Pages : 484
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Book Description
Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences. After discussing the characterizing properties of abstraction, a formal model, the KRA model, is presented to capture them. This model makes the notion of abstraction easily applicable by means of the introduction of a set of abstraction operators and abstraction patterns, reusable across different domains and applications. It is the impact of abstraction in Artificial Intelligence, Complex Systems and Machine Learning which creates the core of the book. A general framework, based on the KRA model, is presented, and its pragmatic power is illustrated with three case studies: Model-based diagnosis, Cartographic Generalization, and learning Hierarchical Hidden Markov Models.
Author: Longbing Cao
Publisher: Springer
ISBN: 1447165519
Category : Computers
Languages : en
Pages : 348
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Book Description
Provides a comprehensive overview and introduction to the concepts, methodologies, analysis, design and applications of metasynthetic computing and engineering. The author: • Presents an overview of complex systems, especially open complex giant systems such as the Internet, complex behavioural and social problems, and actionable knowledge discovery and delivery in the big data era. • Discusses ubiquitous intelligence in complex systems, including human intelligence, domain intelligence, social intelligence, network intelligence, data intelligence and machine intelligence, and their synergy through metasynthetic engineering. • Explains the concept and methodology of human-centred, human-machine-cooperated qualitative-to-quantitative metasynthesis for understanding and managing open complex giant systems, and its computing approach: metasynthetic computing. • Introduces techniques and tools for analysing and designing problem-solving systems for open complex problems and systems. Metasynthetic Computing and Engineering uses the systematology methodology in addressing system complexities in open complex giant systems, for which it may not only be effective to apply reductionism or holism. The book aims to encourage and inspire discussions, design, implementation and reflection of effective methodologies and tools for computing and engineering open complex systems and problems. Researchers, research students and practitioners in complex systems, artificial intelligence, data science, computer science, and even system science, cognitive science, behaviour science, and social science, will find this book invaluable.
Author: Kagan Tumer
Publisher: Springer Science & Business Media
ISBN: 1441989099
Category : Mathematics
Languages : en
Pages : 323
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Book Description
Many complex systems found in nature can be viewed as function optimizers. In particular, they can be viewed as such optimizers of functions in extremely high dimensional spaces. Given the difficulty of performing such high-dimensional op timization with modern computers, there has been a lot of exploration of computa tional algorithms that try to emulate those naturally-occurring function optimizers. Examples include simulated annealing (SA [15,18]), genetic algorithms (GAs) and evolutionary computation [2,3,9,11,20-22,24,28]. The ultimate goal of this work is an algorithm that can, for any provided high-dimensional function, come close to extremizing that function. Particularly desirable would be such an algorithm that works in an adaptive and robust manner, without any explicit knowledge of the form of the function being optimized. In particular, such an algorithm could be used for distributed adaptive control---one of the most important tasks engineers will face in the future, when the systems they design will be massively distributed and horribly messy congeries ofcomputational systems.
Author: Yeliz Karaca
Publisher: Academic Press
ISBN: 0323886167
Category : Science
Languages : en
Pages : 352
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Book Description
Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems addresses different uncertain processes inherent in the complex systems, attempting to provide global and robust optimized solutions distinctively through multifarious methods, technical analyses, modeling, optimization processes, numerical simulations, case studies as well as applications including theoretical aspects of complexity. Foregrounding Multi-chaos, Fractal and Multi-fractional in the era of Artificial Intelligence (AI), the edited book deals with multi- chaos, fractal, multifractional, fractional calculus, fractional operators, quantum, wavelet, entropy-based applications, artificial intelligence, mathematics-informed and data driven processes aside from the means of modelling, and simulations for the solution of multifaceted problems characterized by nonlinearity, non-regularity and self-similarity, frequently encountered in different complex systems. The fundamental interacting components underlying complexity, complexity thinking, processes and theory along with computational processes and technologies, with machine learning as the core component of AI demonstrate the enabling of complex data to augment some critical human skills. Appealing to an interdisciplinary network of scientists and researchers to disseminate the theory and application in medicine, neurology, mathematics, physics, biology, chemistry, information theory, engineering, computer science, social sciences and other far-reaching domains, the overarching aim is to empower out-of-the-box thinking through multifarious methods, directed towards paradoxical situations, uncertain processes, chaotic, transient and nonlinear dynamics of complex systems. Constructs and presents a multifarious approach for critical decision-making processes embodying paradoxes and uncertainty. Includes a combination of theory and applications with regard to multi-chaos, fractal and multi-fractional as well as AI of different complex systems and many-body systems. Provides readers with a bridge between application of advanced computational mathematical methods and AI based on comprehensive analyses and broad theories.
Author: Fatos Xhafa
Publisher: Springer Science & Business Media
ISBN: 1441916369
Category : Mathematics
Languages : en
Pages : 278
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Book Description
"Complex Intelligent Systems and Applications" presents the most up-to-date advances in complex, software intensive and intelligent systems. Each self-contained chapter is the contribution of distinguished experts in areas of research relevant to the study of complex, intelligent, and software intensive systems. These contributions focus on the resolution of complex problems from areas of networking, optimization and artificial intelligence. The book is divided into three parts focusing on complex intelligent network systems, efficient resource management in complex systems, and artificial data mining systems. Through the presentation of these diverse areas of application, the volume provides insights into the multidisciplinary nature of complex problems. Throughout the entire book, special emphasis is placed on optimization and efficiency in resource management, network interaction, and intelligent system design. This book presents the most recent interdisciplinary results in this area of research and can serve as a valuable tool for researchers interested in defining and resolving the types of complex problems that arise in networking, optimization, and artificial intelligence.
Author: Melanie Mitchell
Publisher: Farrar, Straus and Giroux
ISBN: 0374715238
Category : Computers
Languages : en
Pages : 336
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Book Description
Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
Author: Qiang Yang
Publisher: Springer Science & Business Media
ISBN: 3642606180
Category : Computers
Languages : en
Pages : 263
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Book Description
"The central fact is that we are planning agents." (M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2) Recent arguments to the contrary notwithstanding, it seems to be the case that people-the best exemplars of general intelligence that we have to date do a lot of planning. It is therefore not surprising that modeling the planning process has always been a central part of the Artificial Intelligence enterprise. Reasonable behavior in complex environments requires the ability to consider what actions one should take, in order to achieve (some of) what one wants and that, in a nutshell, is what AI planning systems attempt to do. Indeed, the basic description of a plan generation algorithm has remained constant for nearly three decades: given a desciption of an initial state I, a goal state G, and a set of action types, find a sequence S of instantiated actions such that when S is executed instate I, G is guaranteed as a result. Working out the details of this class of algorithms, and making the elabora tions necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.
Author: Ian Miguel
Publisher: Springer
ISBN: 3540735801
Category : Computers
Languages : en
Pages : 420
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Book Description
This is a subject that is as hot as a snake in a wagon rut, offering as it does huge potentiality in the field of computer programming. That’s why this book, which constitutes the refereed proceedings of the 7th International Symposium on Abstraction, Reformulation, and Approximation, held in Whistler, Canada, in July 2007, will undoubtedly prove so popular among researchers and professionals in relevant fields. 26 revised full papers are presented, together with the abstracts of 3 invited papers and 13 research summaries.
Author: Elinor Särner
Publisher: Linköping University Electronic Press
ISBN: 9180756220
Category :
Languages : en
Pages : 69
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Book Description
Artificial intelligence (AI) is increasingly introduced into many systems that modern society rely on and is often portrayed as a savior that can contribute to finding solutions to societal challenges e.g., social, and ecological sustainability. Many of these systems can be classified as complex systems, with interdependencies, emergent behaviors and a diversity of actors involved. As AI is increasingly introduced into these systems, we witness a transformation from complex systems into complex intelligent systems. At the same time caution is invoked toward the risks of AI regarding e.g., biases and loss of control as more tasks are transferred to AI. Hence, the introduction of AI into complex systems is associated with uncertainties around management of AI initiatives and their influence on future systems. Challenges like this can affect many different functions and professions and thus need to be understood collectively. The aim of this thesis is to examine how the actors’ prospective collective sensemaking processes in developing complex systems are affected by AI introduction. Previous research within complex systems literature shows important aspects regarding sensemaking of the system and situations within operations of complex systems. However, sensemaking in the development process of complex systems has been less studied. By examining the introduction of AI in complex systems development this thesis explores collective prospective sensemaking processes in the development of complex intelligent systems. To study an emerging phenomenon like AI introduction in complex systems, an explorative case study was found suitable. The case chosen for the study was a cross-organizational development project of an AI-tool based on Machine Learning for planning of energy systems to be used in the urban planning process of new city districts. This setting revealed plenty prospective and collective sensemaking occasions around AI introduction and exhibited continuous engagement in prospective collective sensemaking relating to the development of the AI tool and the imagined use of the AI tool in the system, which have been reported in the two appended papers. The first paper showed misalignment between actors’ sensemaking processes that alternated between seeking and disengaging behaviors. It also identified the use of boundary objects to retain disengaged actors and raised considerations around the level of detail of the boundary objects in relation to the sensemaking behaviors. The second paper identified dependencies between near- and distant-sensemaking loops that highlight challenges to connect retrospective insights with prospective imaginations by action in the present. In the creation of complex intelligent systems, human involvement seems inevitable, and the second paper exposes how AI can augment human cognition and organizational capabilities for creative imagination around possible and desirable distant-future scenarios. This thesis extends previous research on prospective and collective sensemaking in the development process of complex intelligent systems by presenting a framework of near and distant future sensemaking and internal and external complexity. This provided new insights of how knowledge flows over system levels and how to use boundary objects throughout such projects. Insight that can be useful for management of purposeful AI introduction in complex systems and society. Moreover, it contributes with an empirical case of AI introduction in complex systems to the innovation management literature. Artificiell intelligens (AI) integreras alltmer i de system som vårt moderna samhälle vilar på och framställs ofta som en viktig faktor för att lösa de utmaningar som samhället står inför, till exempel social och ekologisk hållbarhet. Många av dessa system kan definieras som komplexa system, med ömsesidiga beroenden, oförutsedda beteenden och en mångfald av inblandade aktörer. När AI introduceras i sådana system kan vi skönja en transformation från komplexa system till komplexa intelligenta system. Samtidigt påtalas ofta riskerna med AI avseende till exempel partiskhet och minskad kontroll när uppgifter tas över av AI. Införandet av AI i samhällskritiska system förknippas därmed med osäkerheter kring hanteringen av AI-initiativ och dess påverkan på framtiden. Detta leder till utmaningar som berör många olika funktioner och professioner och därmed behöver förstås kollektivt. Syftet med avhandlingen är att undersöka aktörernas framtidsorienterade, gemensamma, meningsskapande processer under utveckling av komplexa system och hur de påverkas av introduktionen av AI. Tidigare forskning inom komplexa system har visat på viktiga aspekter gällande människors förståelse av systemet och situationer framförallt inom driften av komplexa system. Meningsskapande i utvecklingsprocessen av komplexa system har dock hittills inte uppmärksammats i samma utsträckning. Genom att undersöka introduktionen av AI i utvecklingen av komplexa system utforskar denna avhandling kollektiva, framåtblickande, meningsskapande processer inom utvecklingen av komplexa intelligenta system. För studier av ett framväxande fenomen som AI-introduktion i komplexa system ansågs en explorativ fallstudie lämplig. Det valda fallet var ett utvecklingsprojekt av ett AI-verktyg baserat på maskininlärning med syfte att användas i planeringen av energisystem inom stadsplaneringsprocessen av nya stadsdelar och hade flera medverkande organisationer. Fallet visade på flera framtidsorienterade och gemensamma meningsskapande situationer kring AI-introduktion. Därmed synliggjordes de medverkandes kontinuerliga deltagande i framåtblickande gemensamt meningsskapande relaterat till utvecklingen av AI-verktyget och dess tänkta användningen i systemet, vilket rapporterats i de två bilagda artiklarna. Den första artikeln visade att de meningsskapande processerna var ur fas mellan aktörerna, vilka växlade mellan sökande och oengagerade beteenden, och att användningen av gränsöverskridande objekt med rätt detaljnivå kan involvera oengagerade aktörer. Den andra artikeln identifierade beroenden mellan olika meningsskapande cykler, närliggande respektive avlägsen framtid, vilket belyser utmaningar med att koppla tillbakablickande insikter till framåtblickande föreställningar om systemet genom handling i nuet. Vid skapande av komplexa intelligenta system framstår mänsklig inblandning som oundviklig, och den andra artikeln belyser även hur AI kan förstärka mänsklig kognition och organisatoriska förmågor för att främja kreativ föreställningsförmåga kring möjliga och önskvärda scenarier av en avlägsen framtid. Avhandlingen bidrar genom att bredda tidigare forskning om framtidsorienterat och kollektivt meningsskapande i utvecklingsprocessen av komplexa intelligenta system genom att presentera ett ramverk av närliggande och avlägset framåtblickande och intern och extern komplexitet. Det visar på nya insikter om hur kunskap flödar över systemnivåer och gränsöverskridande objekt kan användas under sådana utvecklingsprojekt. Sådana insikter kan vara praktiskt användbara för en välgrundad AI-introduktion i komplexa system och i samhället i stort. Dessutom bidrar den med en empirisk fallstudie av resan mot AI-introduktion i komplexa system till litteraturen inom innovationsledning.
Author: Andreas Tolk
Publisher: Springer Science & Business Media
ISBN: 3540880747
Category : Mathematics
Languages : en
Pages : 272
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Book Description
The tremendous growth in the availability of inexpensive computing power and easy availability of computers have generated tremendous interest in the design and imp- mentation of Complex Systems. Computer-based solutions offer great support in the design of Complex Systems. Furthermore, Complex Systems are becoming incre- ingly complex themselves. This research book comprises a selection of state-of-the-art contributions to topics dealing with Complex Systems in a Knowledge-based En- ronment. Complex systems are ubiquitous. Examples comprise, but are not limited to System of Systems, Service-oriented Approaches, Agent-based Systems, and Complex Distributed Virtual Systems. These are application domains that require knowledge of engineering and management methods and are beyond the scope of traditional systems. The chapters in this book deal with a selection of topics which range from unc- tainty representation, management and the use of ontological means which support and are large-scale business integration. All contributions were invited and are based on the recognition of the expertise of the contributing authors in the field. By colle- ing these sources together in one volume, the intention was to present a variety of tools to the reader to assist in both study and work. The second intention was to show how the different facets presented in the chapters are complementary and contribute towards this emerging discipline designed to aid in the analysis of complex systems.
