The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of th...
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ISBN:
(数字)9783319002484
ISBN:
(纸本)9783319002477
The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a new class of learning algorithms for multilayer neural networks. Another purpose of this book is to show that the generalized net theory can be successfully used as a new description of multilayer neural networks. Several generalized net descriptions of neural networks functioning processes are considered, namely: the simulation process of networks, a system of neural networks and the learning algorithms developed in this book. The generalized net approach to modelling of real systems may be used successfully for the description of a variety of technological and intellectual problems, it can be used not only for representing the parallel functioning of homogenous objects, but also for modelling non-homogenous systems, for example systems which consist of a different kind of subsystems. The use of the generalized nets methodology shows a new way to describe functioning of discrete dynamic systems.
This book is a selection of results obtained within two years of research per- formed under SYNAT - a nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for ...
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ISBN:
(数字)9783642356476
ISBN:
(纸本)9783642356469;9783642356476
This book is a selection of results obtained within two years of research per- formed under SYNAT - a nation-wide scientific project aiming at creating an infrastructure for scientific content storage and sharing for academia, education and open knowledge society in Poland. The selection refers to the research in artificial intelligence, knowledge discovery and data mining, information retrieval and natural language processing, addressing the problems of implementing intelligent tools for building a scientific information *** book is a continuation and extension of the ideas presented in Intelligent Tools for Building a Scientific Information Platform published as volume 390 in the same series in 2012. It is based on the SYNAT 2012 Workshop held in Warsaw. The papers included in this volume present an overview and insight into information retrieval, repository systems, text processing, ontology-based systems, text mining, multimedia data processing and advanced software engineering.
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to ...
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ISBN:
(数字)9783319011684
ISBN:
(纸本)9783319011677
This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agents lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.
Artists and creators in interactive art and interaction design have long been conducting research on human-machine interaction. Through artistic, conceptual, social and critical projects, they have shown how interacti...
ISBN:
(数字)9783540798705
ISBN:
(纸本)9783540798699
Artists and creators in interactive art and interaction design have long been conducting research on human-machine interaction. Through artistic, conceptual, social and critical projects, they have shown how interactive digital processes are essential elements for their artistic creations. Resulting prototypes have often reached beyond the art arena into areas such as mobile computing, intelligent ambiences, intelligent architecture, fashionable technologies, ubiquitous computing and pervasive gaming. Many of the early artist-developed interactive technologies have influenced new design practices, products and services of today's media society. This book brings together key theoreticians and practitioners of this field. It shows how historically relevant the issues of interaction and interface design are, as they can be analyzed not only from an engineering point of view but from a social, artistic and conceptual, and even commercial angle as well.
The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often stu...
ISBN:
(纸本)3319026054;9783319026053
The problem of Multiagent Learning (or MAL) is concerned with the study of how intelligent entities can learn and adapt in the presence of other such entities that are simultaneously adapting. The problem is often studied in the stylized settings provided by repeated matrix games (a.k.a. normal form games). The goal of this book is to develop MAL algorithms for such a setting that achieve a new set of objectives which have not been previously achieved. In particular this book deals with learning in the presence of a new class of agent behavior that has not been studied or modeled before in a MAL context: Markovian agent behavior. Several new challenges arise when interacting with this particular class of agents. The book takes a series of steps towards building completely autonomous learning algorithms that maximize utility while interacting with such agents. Each algorithm is meticulously specified with a thorough formal treatment that elucidates its key theoretical properties.
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