Deterministic artificial intelligence (36 résultats)

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Taschenbuch. Etat : Neu. Neuware - Deterministic Sustainable Cost-Efficient Artificial Intelligence presents a complementary deterministic (non-iterative) perspective on artificial intelligence. Rather than treating learning exclusively as an iterative optimization process, this book explores conditions under which learning may be formulated as a structural equilibrium problem and computed directly through algebraic methods.The book develops the Cekirge s-Regularized Framework, a deterministic approach in which learning is obtained through closed-form computation instead of repeated gradient-based optimization. Topics include deterministic learning, perturbation analysis, structural equilibrium, transformer architectures, regularization, scalability, energy-efficient computation, and deterministic inference.Rather than replacing existing optimization-based methods, this work introduces a complementary mathematical framework intended to stimulate discussion, further research, and experimental validation.The book is intended for researchers, graduate students, engineers, and professionals working in artificial intelligence, machine learning, computational mathematics, optimization, and sustainable computing.…

Taschenbuch. Etat : Neu. Neuware - Artificial intelligence has achieved remarkable success through optimization-based learning, probabilistic reasoning, deep neural networks, and increasingly complex computational architectures. Despite these advances, many autonomous robotic systems continue to rely on iterative search, repeated optimization, and computationally expensive decision-making procedures.This book presents a complementary deterministic (non-iterative) perspective for autonomous robotics. Rather than viewing perception, decision-making, planning, and autonomous behavior primarily as optimization problems, it investigates whether many robotic tasks can be formulated as processes of structural compatibility, admissibility evaluation, equilibrium discovery, and progressive elimination of incompatible alternatives.The proposed framework develops deterministic methodologies for object recognition, environmental representation, autonomous action selection, multi-agent coordination, goal selection, robotic continuity, and hardware-oriented parallel filtering architectures. Throughout the book, compatible solutions emerge through constraint-governed elimination rather than exhaustive search, providing an alternative computational interpretation of intelligent robotic behavior.The objective of this work is not to replace existing machine learning, reinforcement learning, probabilistic robotics, or optimization theory. Instead, it offers a complementary framework that emphasizes interpretability, reproducibility, computational efficiency, operational stability, and energy-conscious autonomous decision-making.Intended for researchers, graduate students, engineers, and practitioners in robotics and artificial intelligence, this book introduces a unified deterministic framework that may stimulate further research into sustainable, computationally efficient, and structurally interpretable autonomous robotic systems.…

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Etat : gut. 2013. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning, 23, Band 23) In englischer Sprache. pages.

Paperback. Etat : new. Paperback. Deterministic Sustainable Cost-Efficient Artificial Intelligence presents a complementary deterministic (non-iterative) perspective on artificial intelligence. Rather than treating learning exclusively as an iterative optimization process, this book explores conditions under which learning may be formulated as a structural equilibrium problem and computed directly through algebraic methods.The book develops the Cekirge s-Regularized Framework, a deterministic approach in which learning is obtained through closed-form computation instead of repeated gradient-based optimization. Topics include deterministic learning, perturbation analysis, structural equilibrium, transformer architectures, regularization, scalability, energy-efficient computation, and deterministic inference.Rather than replacing existing optimization-based methods, this work introduces a complementary mathematical framework intended to stimulate discussion, further research, and experimental validation.The book is intended for researchers, graduate students, engineers, and professionals working in artificial intelligence, machine learning, computational mathematics, optimization, and sustainable computing. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Paperback. Etat : new. Paperback. Artificial intelligence has achieved remarkable success through optimization-based learning, probabilistic reasoning, deep neural networks, and increasingly complex computational architectures. Despite these advances, many autonomous robotic systems continue to rely on iterative search, repeated optimization, and computationally expensive decision-making procedures.This book presents a complementary deterministic (non-iterative) perspective for autonomous robotics. Rather than viewing perception, decision-making, planning, and autonomous behavior primarily as optimization problems, it investigates whether many robotic tasks can be formulated as processes of structural compatibility, admissibility evaluation, equilibrium discovery, and progressive elimination of incompatible alternatives.The proposed framework develops deterministic methodologies for object recognition, environmental representation, autonomous action selection, multi-agent coordination, goal selection, robotic continuity, and hardware-oriented parallel filtering architectures. Throughout the book, compatible solutions emerge through constraint-governed elimination rather than exhaustive search, providing an alternative computational interpretation of intelligent robotic behavior.The objective of this work is not to replace existing machine learning, reinforcement learning, probabilistic robotics, or optimization theory. Instead, it offers a complementary framework that emphasizes interpretability, reproducibility, computational efficiency, operational stability, and energy-conscious autonomous decision-making.Intended for researchers, graduate students, engineers, and practitioners in robotics and artificial intelligence, this book introduces a unified deterministic framework that may stimulate further research into sustainable, computationally efficient, and structurally interpretable autonomous robotic systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In AI planning and reinforcement learning, a policy prescribes an action given a non-empty sequence of states. A deterministic policy will always prescribe the same action, over time, for any given input. Automated planning and scheduling is a branch of artificial intelligence that concerns the realisation of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex, unknown and have to be discovered and optimised in multidimensional space.…

Paperback. Etat : new. Paperback. Deterministic Sustainable Cost-Efficient Artificial Intelligence presents a complementary deterministic (non-iterative) perspective on artificial intelligence. Rather than treating learning exclusively as an iterative optimization process, this book explores conditions under which learning may be formulated as a structural equilibrium problem and computed directly through algebraic methods.The book develops the Cekirge s-Regularized Framework, a deterministic approach in which learning is obtained through closed-form computation instead of repeated gradient-based optimization. Topics include deterministic learning, perturbation analysis, structural equilibrium, transformer architectures, regularization, scalability, energy-efficient computation, and deterministic inference.Rather than replacing existing optimization-based methods, this work introduces a complementary mathematical framework intended to stimulate discussion, further research, and experimental validation.The book is intended for researchers, graduate students, engineers, and professionals working in artificial intelligence, machine learning, computational mathematics, optimization, and sustainable computing. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Etat : new. Paperback. Artificial intelligence has achieved remarkable success through optimization-based learning, probabilistic reasoning, deep neural networks, and increasingly complex computational architectures. Despite these advances, many autonomous robotic systems continue to rely on iterative search, repeated optimization, and computationally expensive decision-making procedures.This book presents a complementary deterministic (non-iterative) perspective for autonomous robotics. Rather than viewing perception, decision-making, planning, and autonomous behavior primarily as optimization problems, it investigates whether many robotic tasks can be formulated as processes of structural compatibility, admissibility evaluation, equilibrium discovery, and progressive elimination of incompatible alternatives.The proposed framework develops deterministic methodologies for object recognition, environmental representation, autonomous action selection, multi-agent coordination, goal selection, robotic continuity, and hardware-oriented parallel filtering architectures. Throughout the book, compatible solutions emerge through constraint-governed elimination rather than exhaustive search, providing an alternative computational interpretation of intelligent robotic behavior.The objective of this work is not to replace existing machine learning, reinforcement learning, probabilistic robotics, or optimization theory. Instead, it offers a complementary framework that emphasizes interpretability, reproducibility, computational efficiency, operational stability, and energy-conscious autonomous decision-making.Intended for researchers, graduate students, engineers, and practitioners in robotics and artificial intelligence, this book introduces a unified deterministic framework that may stimulate further research into sustainable, computationally efficient, and structurally interpretable autonomous robotic systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnKirchhoff s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton s laws, while rotational motion mechanics comply with Euler s moment equations, a set of three nonlinear, .…