Reinforcement And Systemic Machine Learning For Decision Making
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Reinforcement And Systemic Machine Learning For Decision Making
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Author : Parag Kulkarni
language : en
Publisher: John Wiley & Sons
Release Date : 2012-07-11
Reinforcement And Systemic Machine Learning For Decision Making written by Parag Kulkarni and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-11 with Technology & Engineering categories.
Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
Multi Objective Decision Making
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Author : Diederik M. Roijers
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2017-04-20
Multi Objective Decision Making written by Diederik M. Roijers and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-20 with Computers categories.
Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs). First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the available information about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems. Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting. Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Advances In Complex Decision Making
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Author : Walayat Hussain
language : en
Publisher: CRC Press
Release Date : 2023-12-06
Advances In Complex Decision Making written by Walayat Hussain and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-06 with Computers categories.
The rapidly evolving business and technology landscape demands sophisticated decision-making tools to stay ahead of the curve. Advances in Complex Decision Making: Using Machine Learning and Tools for Service-Oriented Computing is a cutting-edge technical guide exploring the latest decision-making technology advancements. This book provides a comprehensive overview of machine learning algorithms and examines their applications in complex decision-making systems in a service-oriented framework. The authors also delve into service-oriented computing and how it can be used to build complex systems that support decision making. Many real-world examples are discussed in this book to provide a practical insight into how discussed techniques can be applied in various domains, including distributed computing, cloud computing, IoT and other online platforms. For researchers, students, data scientists and technical practitioners, this book offers a deep dive into the current developments of machine learning algorithms and their applications in service-oriented computing. This book discusses various topics, including Fuzzy Decisions, ELICIT, OWA aggregation, Directed Acyclic Graph, RNN, LSTM, GRU, Type-2 Fuzzy Decision, Evidential Reasoning algorithm and robust optimisation algorithms. This book is essential for anyone interested in the intersection of machine learning and service computing in complex decision-making systems.
Reverse Hypothesis Machine Learning
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Author : Parag Kulkarni
language : en
Publisher: Springer
Release Date : 2017-03-30
Reverse Hypothesis Machine Learning written by Parag Kulkarni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-30 with Technology & Engineering categories.
This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same—the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.
Decision Intelligence
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Author : Miriam O'Callaghan
language : en
Publisher: CRC Press
Release Date : 2023-04-26
Decision Intelligence written by Miriam O'Callaghan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-26 with Business & Economics categories.
Revealing the limitations of human decision-making, this book explores how Artificial Intelligence (AI) can be used to optimize decisions for improved business outcomes and efficiency, as well as looking ahead to the significant contributions Decision Intelligence (DI) can make to society and the ethical challenges it may raise. From the theories and concepts used to design autonomous intelligent agents to the technologies that power DI systems and the ways in which companies use decision-making building blocks to build DI solutions that enable businesses to democratize AI, this book presents an impressive framework to integrate artificial and human intelligence for the success of different types of business decisions. Replete with case studies on DI applications, as well as wider discussions on the social implications of the technology, Decision Intelligence: Human–Machine Integration for Decision Making appeals to both students of AI and data sciences and businesses considering DI adoption.
Choice Computing Machine Learning And Systemic Economics For Choosing
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Author : Parag Kulkarni
language : en
Publisher: Springer Nature
Release Date : 2022-08-28
Choice Computing Machine Learning And Systemic Economics For Choosing written by Parag Kulkarni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-28 with Technology & Engineering categories.
This book presents thoughts and pathways to build revolutionary machine learning models with the new paradigm of machine learning to adapt behaviorism. It focuses on two aspects – one focuses on architecting a choice process to lead users on the certain choice path while the second focuses on developing machine learning models based on choice paradigm. This book is divided in three parts where part one deals with human choice and choice architecting models with stories of choice architects. Second part closely studies human choosing models and deliberates on developing machine learning models based on the human choice paradigm. Third part takes you further to look at machine learning based choice architecture. The proposed pioneering choice-based paradigm for machine learning presented in the book will help readers to develop products – help readers to solve problems in a more humanish way and to negotiate with uncertainty in a more graceful but in an objective way. It will help to create unprecedented value for business and society. Further, it will unveil a new paradigm for modern intelligent businesses to embark on the new journey; the journey of transition from shackled feature rich and choice poor systems to feature flexible and choice rich natural behaviors.
The The Reinforcement Learning Workshop
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Author : Alessandro Palmas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-18
The The Reinforcement Learning Workshop written by Alessandro Palmas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-18 with Computers categories.
Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Predicting Human Decision Making
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Author : Ariel Geib
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Predicting Human Decision Making written by Ariel Geib and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
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.
Machine Learning For Decision Makers
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Author : Patanjali Kashyap
language : en
Publisher: Apress
Release Date : 2018-01-04
Machine Learning For Decision Makers written by Patanjali Kashyap and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-04 with Computers categories.
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.
Machine Learning For Intelligent Decision Science
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Author : Jitendra Kumar Rout
language : en
Publisher: Springer Nature
Release Date : 2020-04-02
Machine Learning For Intelligent Decision Science written by Jitendra Kumar Rout and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-02 with Technology & Engineering categories.
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.