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Advances in artificial intelligence: models, optimization, and machine learning
Contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems.
Advances in Artificial Economics : The Economy as a Complex Dynamic System
Perceiving the economy as a complex dynamic system, generates a need for new tools for its study. As a constructive simulation method, Agent-Based Computational Economics (ACE) has in recent years proven its strength and extensive applicability. Fields of study are widely spread within economics, with a cluster around financial markets. This book is based on communications given at AE’2006 (Aalborg, Denmark) – the second symposium on Artificial Economics, and covers both wellknown questions of economics, like the existence of market efficiency, as well as new questions raised by the new tools, for example questions related to networks of social interaction.
Advanced Information Systems Engineering ; 20th International Conference, CAiSE 2008 Montpellier, France, June 16-20, 2008 Proceedings
This book constitutes the refereed proceedings of the 20th International Conference on Advanced Information Systems Engineering, CAiSE 2008, held in Montpellier, France, in June 2008.
Ad-Hoc, mobile, and wireless networks ; Vol.3738 ; 4th International conference, ADHOC-NOW 2005, Cancun, Mexico, October 6-8, 2005, Proceedings
This book constitutes the refereed proceedings of the 4th International Conference on Ad-Hoc Networks and Wireless, ADHOiNOW 2005, The papers discuss architectures, protocols, and algorithms for: access control, scheduling, ad hoc and sensor networks analytic methods and modelling for performance evaluation, characterization, optimization, auto-configuration, incentives and pricing, location awareness, discovery, dependence, and management, mesh networks, new applications, power management, power control, and energy-efficiency, quality-of-service, resource allocation, multimedia, routing (unicast, multicast, etc.), security and privacy, service discovery, systems and testbeds, wireless internet, and data management.
Adaptive Learning of Polynomial Networks : Genetic Programming, Backpropagation and Bayesian Methods
This book provides theoretical and practical knowledge for develop ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well.
Abstraction, Reformulation, and Approximation ; 7th International Symposium, SARA 2007, Whistler, Canada, July 18-21, 2007, Proceedings
This volume contains the proceedings of SARA 2007, the seventh symposium, held at Whistler Village, British Columbia, Canada, July 18-21. Three distinguished speakers were invited to give keynote presentations, and their abstracts are included herein,It has been recognized since the inception of artificial intelligence that abstractions, problem reformulations and approximations (AR&A) are central to human common-sense reasoning and problem solving and to the ability of systems to reason effectively in complex domains.AR&A techniques have been used in a variety of problem-solving settings, including automated reasoning, cognitive modelling.
A Guide to Graph Algorithms
Offers high-quality content in the research area of graph algorithms and explores the latest developments in graph algorithmics. The reader will gain a comprehensive understanding of how to use algorithms to explore graphs. It is a collection of texts that have proved to be trend setters and good examples of that. The book aims at providing the reader with a deep understanding of the structural properties of graphs that are useful for the design of efficient algorithms. These algorithms have applications in finite state machine modelling, social network theory, biology, and mathematics. The book contains many exercises, some up at present-day research-level. The exercises encourage the reader to discover new techniques by putting things in a clear perspective.
3D Mesh processing and character animation : with examples using OpenGL, OpenMesh and Assimp
Focusses specifically on topics that are important in three-dimensional modelling, surface design and real-time character animation. It provides an in-depth coverage of data structures and popular methods used in geometry processing, keyframe and inverse kinematics animations and shader based processing of mesh objects. It also introduces two powerful and versatile libraries, OpenMesh and Assimp, and demonstrates their usefulness through implementations of a wide range of algorithms in mesh processing and character animation respectively. This Textbook is written for students at an advanced undergraduate or postgraduate level who are interested in the study and development of graphics algorithms for three-dimensional mesh modeling and analysis, and animations of rigged character models.
3D Imaging, Analysis and Applications
This textbook is designed for postgraduate studies in the field of 3D Computer Vision. It also provides a useful reference for industrial practitioners; for example, in the areas of 3D data capture, computer-aided geometric modelling and industrial quality assurance. This second edition is a significant upgrade of existing topics with novel findings. Additionally, it has new material covering consumer-grade RGB-D cameras, 3D morphable models, deep learning on 3D datasets, as well as new applications in the 3D digitization of cultural heritage and the 3D phenotyping of crops.
