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Author: Leslie Pack Kaelbling
Publisher: Springer Science & Business Media
ISBN: 0792397053
Category : Computers
Languages : en
Pages : 286
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Book Description
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Author: Leslie Pack Kaelbling
Publisher: Springer Science & Business Media
ISBN: 0792397053
Category : Computers
Languages : en
Pages : 286
Get Book Here
Book Description
Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of important papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).
Author: N. Petkov
Publisher: IOS Press
ISBN: 1614999295
Category : Computers
Languages : en
Pages : 370
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Book Description
The deployment of intelligent systems to tackle complex processes is now commonplace in many fields from medicine and agriculture to industry and tourism. This book presents scientific contributions from the 1st International Conference on Applications of Intelligent Systems (APPIS 2018) held at the Museo Elder in Las Palmas de Gran Canaria, Spain, from 10 to 12 January 2018. The aim of APPIS 2018 was to bring together scientists working on the development of intelligent computer systems and methods for machine learning, artificial intelligence, pattern recognition, and related techniques with an emphasis on their application to various problems. The 34 peer-reviewed papers included here cover an extraordinarily wide variety of topics – everything from semi-supervised learning to matching electro-chemical sensor information with human odor perception – but what they all have in common is the design and application of intelligent systems and their role in tackling diverse and complex challenges. The book will be of particular interest to all those involved in the development and application of intelligent systems.
Author: Zhengtao Ding
Publisher: Institution of Engineering and Technology
ISBN: 1849195749
Category : Technology & Engineering
Languages : en
Pages : 288
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Book Description
An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive controlin a unified framework, presenting the major results at a moderate mathematical level, suitable for MSc students and engineers with undergraduate degrees. Topics covered include introduction to nonlinear systems; state space models; describing functions forcommon nonlinear components; stability theory; feedback linearization; adaptive control; nonlinear observer design; backstepping design; disturbance rejection and output regulation; and control applications, including harmonic estimation and rejection inpower distribution systems, observer and control design for circadian rhythms, and discrete-time implementation of continuous-timenonlinear control laws.
Author: Francisco S. Melo
Publisher: Springer Nature
ISBN: 3031201795
Category : Computers
Languages : en
Pages : 151
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Book Description
This book constitutes thoroughly refereed and revised selected best and visionary papers from the Workshops held at the International Conference on Autonomous Agents and Multiagent Systems AAMAS 2022, which took place online, during May 9–13, 2022. The 5 best papers and 4 visionary papers included in this book stem from the following workshops: - 13th Workshop on Optimization and Learning in Multi-agent Systems (OptLearnMAS);- 23rd Workshop on Multi-Agent Based Simulation (MABS);- 6th Workshop on Agent-Based Modelling of Urban Systems (ABMUS);- 10th Workshop on Engineering Multi-Agent Systems (EMAS);- 1st Workshop on Rebellion and Disobedience in AI (RaD-AI). There was a total of 59 submissions to these workshops.
Author: Robert Babuška
Publisher: Springer
ISBN: 3642116884
Category : Technology & Engineering
Languages : en
Pages : 598
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Book Description
The increasing complexity of our world demands new perspectives on the role of technology in decision making. Human decision making has its li- tations in terms of information-processing capacity. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and tra?c management, where humans need to engage in close collaborations with arti?cial systems to observe and understand the situation and respond in a sensible way. We believe that close collaborations between humans and arti?cial systems will become essential and that the importance of research into Interactive Collaborative Information Systems (ICIS) is self-evident. Developments in information and communication technology have ra- cally changed our working environments. The vast amount of information available nowadays and the wirelessly networked nature of our modern so- ety open up new opportunities to handle di?cult decision-making situations such as computer-supported situation assessment and distributed decision making. To make good use of these new possibilities, we need to update our traditional views on the role and capabilities of information systems. The aim of the Interactive Collaborative Information Systems project is to develop techniques that support humans in complex information en- ronments and that facilitate distributed decision-making capabilities. ICIS emphasizes the importance of building actor-agent communities: close c- laborations between human and arti?cial actors that highlight their comp- mentary capabilities, and in which task distribution is ?exible and adaptive.
Author: Shikha Agrawal
Publisher: Springer Nature
ISBN: 9811696500
Category : Technology & Engineering
Languages : en
Pages : 558
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Book Description
This book is a collection of peer-reviewed best selected research papers presented at the Second International Conference on Machine Intelligence and Smart Systems (MISS 2021), organized during September 24–25, 2021, in Gwalior, India. The book presents new advances and research results in the fields of machine intelligence, artificial intelligence and smart systems. It includes main paradigms of machine intelligence algorithms, namely (1) neural networks, (2) evolutionary computation, (3) swarm intelligence, (4) fuzzy systems and (5) immunological computation. Scientists, engineers, academicians, technology developers, researchers, students and government officials will find this book useful in handling their complicated real-world issues by using machine intelligence methodologies.
Author: Yashwant Singh
Publisher: Springer Nature
ISBN: 9819728398
Category :
Languages : en
Pages : 689
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Book Description
Author: Michael Behrisch
Publisher: Springer
ISBN: 3662450798
Category : Computers
Languages : en
Pages : 182
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Book Description
This book constitutes the thoroughly refereed proceedings of the First International Conference on Simulation of Urban Mobility, SUMO 2013, held in Berlin, Germany, in May 2013. The 12 revised full papers presented tin this book were carefully selected and reviewed from 22 submissions. The papers are organized in two topical sections: models and technical innovations and applications and surveys.
Author: Fei Hu
Publisher: CRC Press
ISBN: 100087723X
Category : Computers
Languages : en
Pages : 357
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Book Description
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
Author: Amit Joshi
Publisher: Springer Nature
ISBN: 9811571066
Category : Technology & Engineering
Languages : en
Pages : 627
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Book Description
This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis, presented virtually at the Fourth International Conference on Information and Communication Technology for Intelligent Systems (ICTIS 2020), India. It covers topics such as intelligent agent and multi-agent systems in various domains, machine learning, intelligent information retrieval and business intelligence, intelligent information system development using design science principles, intelligent web mining and knowledge discovery systems.
