Automated Decision-making Using Neural Networks PDF Download

Author: Feichin Ted Tschang
Publisher:
ISBN:
Category : Decision making
Languages : en
Pages : 170

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Author: S.G. Tzafestas
Publisher: Springer Science & Business Media
ISBN: 9401103054
Category : Computers
Languages : en
Pages : 778

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Book Description
This book is concerned with Artificial Intelligence (AI) concepts and techniques as applied to industrial decision making, control and automation problems. The field of AI has been expanded enormously during the last years due to that solid theoretical and application results have accumulated. During the first stage of AI development most workers in the field were content with illustrations showing ideas at work on simple problems. Later, as the field matured, emphasis was turned to demonstrations that showed the capability of AI techniques to handle problems of practical value. Now, we arrived at the stage where researchers and practitioners are actually building AI systems that face real-world and industrial problems. This volume provides a set of twenty four well-selected contributions that deal with the application of AI to such real-life and industrial problems. These contributions are grouped and presented in five parts as follows: Part 1: General Issues Part 2: Intelligent Systems Part 3: Neural Networks in Modelling, Control and Scheduling Part 4: System Diagnostics Part 5: Industrial Robotic, Manufacturing and Organizational Systems Part 1 involves four chapters providing background material and dealing with general issues such as the conceptual integration of qualitative and quantitative models, the treatment of timing problems at system integration, and the investigation of correct reasoning in interactive man-robot systems.

Author: Hemachandran K
Publisher: CRC Press
ISBN: 1000934624
Category : Technology & Engineering
Languages : en
Pages : 372

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Book Description
Artificial Intelligence and Knowledge Processing play a vital role in various automation industries and their functioning in converting traditional industries to AI-based factories. This book acts as a guide and blends the basics of Artificial Intelligence in various domains, which include Machine Learning, Deep Learning, Artificial Neural Networks, and Expert Systems, and extends their application in all sectors. Artificial Intelligence and Knowledge Processing: Improved Decision-Making and Prediction, discusses the designing of new AI algorithms used to convert general applications to AI-based applications. It highlights different Machine Learning and Deep Learning models for various applications used in healthcare and wellness, agriculture, and automobiles. The book offers an overview of the rapidly growing and developing field of AI applications, along with Knowledge of Engineering, and Business Analytics. Real-time case studies are included across several different fields such as Image Processing, Text Mining, Healthcare, Finance, Digital Marketing, and HR Analytics. The book also introduces a statistical background and probabilistic framework to enhance the understanding of continuous distributions. Topics such as Ensemble Models, Deep Learning Models, Artificial Neural Networks, Expert Systems, and Decision-Based Systems round out the offerings of this book. This multi-contributed book is a valuable source for researchers, academics, technologists, industrialists, practitioners, and all those who wish to explore the applications of AI, Knowledge Processing, Deep Learning, and Machine Learning.

Author: Ariel Geib
Publisher: Springer Nature
ISBN: 3031015789
Category : Computers
Languages : en
Pages : 134

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Book Description
Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

Author: Gloria Phillips-Wren
Publisher: Springer Science & Business Media
ISBN: 3540768289
Category : Mathematics
Languages : en
Pages : 414

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Book Description
Intelligent Decision Support Systems have the potential to transform human decision making by combining research in artificial intelligence, information technology, and systems engineering. The field of intelligent decision making is expanding rapidly due, in part, to advances in artificial intelligence and network-centric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to a human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. This book includes contributions from leading researchers in the field beginning with the foundations of human decision making and the complexity of the human cognitive system. Researchers contrast human and artificial intelligence, survey computational intelligence, present pragmatic systems, and discuss future trends. This book will be an invaluable resource to anyone interested in the current state of knowledge and key research gaps in the rapidly developing field of intelligent decision support.

Author: Patanjali Kashyap
Publisher: Apress
ISBN: 1484229886
Category : Computers
Languages : en
Pages : 381

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Book Description
Take a deep dive into the concepts of machine learning as they apply to contemporary business and management. You will learn how machine learning techniques are used to solve fundamental and complex problems in society and industry. Machine Learning for Decision Makers serves as an excellent resource for establishing the relationship of machine learning with IoT, big data, and cognitive and cloud computing to give you an overview of how these modern areas of computing relate to each other. This book introduces a collection of the most important concepts of machine learning and sets them in context with other vital technologies that decision makers need to know about. These concepts span the process from envisioning the problem to applying machine-learning techniques to your particular situation. This discussion also provides an insight to help deploy the results to improve decision-making. The book uses case studies and jargon busting to help you grasp the theory of machine learning quickly. You'll soon gain the big picture of machine learning and how it fits with other cutting-edge IT services. This knowledge will give you confidence in your decisions for the future of your business. What You Will Learn Discover the machine learning, big data, and cloud and cognitive computing technology stack Gain insights into machine learning concepts and practices Understand business and enterprise decision-making using machine learning Absorb machine-learning best practices Who This Book Is For Managers tasked with making key decisions who want to learn how and when machine learning and related technologies can help them.

Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 336

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Author: Ameer Hamza Khan
Publisher: Springer Nature
ISBN: 9811593922
Category : Technology & Engineering
Languages : en
Pages : 91

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Book Description
In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.

Author: Hasmik Osipyan
Publisher: CRC Press
ISBN: 0429556209
Category : Computers
Languages : en
Pages : 158

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Book Description
The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machineries in the industries during the first industrial revolution; the internal combustion engine that gave rise to cars, trucks, and airplanes; electricity that caused the second industrial revolution through the discovery of direct and alternating current; and the Internet, which led to the emergence of the information age, AI is a transformational technology. It will cause a paradigm shift in the way’s problems are solved in every aspect of our lives, and, from it, innovative technologies will emerge. AI is the theory and development of machines that can imitate human intelligence in tasks such as visual perception, speech recognition, decision-making, and human language translation. This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence. Research overview includes reasoning approaches, problem solving, knowledge representation, planning, learning, natural language processing, perception, motion and manipulation, social intelligence and creativity. It will allow the reader to gain a deep and broad knowledge of the latest engineering technologies of AI and Deep Learning and is an excellent resource for academic research and industry applications.

Author: Charles Wong
Publisher:
ISBN:
Category :
Languages : en
Pages : 240

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Book Description
Abstract: This thesis provides a multi-disciplinary approach to decision modeling using finance and neural networks. Neural modeling simulates biological decision-making mechanisms. Modern finance formalizes human decision-making. Combining them illuminates how to better emulate intelligent decision processes. Three projects address key aspects of decision processes heretofore neglected in automated decision models: selective attention, working memory, and social learning. Exploring each aspect from the perspective of real world traders yields quantifiable enhancements to existing neural models. Financial decision-making first involves gathering evidence to consider before rendering an informed decision. Basing decisions on unfiltered and conflicting evidence raises confounds that reduce the reliability of the judgment. Clustering based on professional trader interviews can implement selective attention that filters and cleans the evidence. If similar lines of evidence are treated as a single unit, this can clarify and improve the decision-making process. Results from neural networks enhanced with this selective attention process show significant improvements on standard benchmark tasks relative to alternative approaches. Financial decision-making next considers the context under which the evidence arrives. For example, investing in a bankruptcy-prone company is far riskier than investing in a company with growth prospects, all else equal Financial and accounting industry best practices connect past and present evidence to establish the context, which implements a form of working memory. Working memory enables a neural network to aggregate multiple observations in time to form a temporal contextual pattern. Results show the enhancement provides significant improvements. Finally, financial decision-makers often bias their actions by observing others' actions and inferring the relevant evidence. Industry best practices allow auditors and analysts to partially leverage others' recommendations to reduce the information costs of decision making. Allowing a population of heterogeneous neural networks to observe each others' decisions simulates markets of constantly learning individuals. The simulations allow empirical exploration of novel market dynamics, and results corroborate prior research predictions on the nature of markets.