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Reinforcement Learning
Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as tran...
Embedded Automation in Human-Agent Environment
This research book proposes a general conceptual framework for the development of automation in human-agents environments that will allow human- agent teams to work effectively and efficiently. We examine various schemes to implement artificial intelligence techniques in agents. The text is directed to the scientists, application engineers, professors and students of all disciplines, interested in the agency methodology and applications.
Evolutionary Computation in Dynamic and Uncertain Environments
This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
Agent-Based Evolutionary Search
Agent based evolutionary search is an emerging paradigm in computational int- ligence offering the potential to conceptualize and solve a variety of complex problems such as currency trading, production planning, disaster response m- agement, business process management etc. There has been a significant growth in the number of publications related to the development and applications of agent based systems in recent years which has prompted special issues of journals and dedicated sessions in premier conferences. The notion of an agent with its ability to sense, learn and act autonomously - lows the development of a plethora of efficient algorithms to deal with complex problems. This notion o...
Linkage in Evolutionary Computation
In recent years, the issue of linkage in GEAs has garnered greater attention and recognition from researchers. Conventional approaches that rely much on ad hoc tweaking of parameters to control the search by balancing the level of exploitation and exploration are grossly inadequate. As shown in the work reported here, such parameters tweaking based approaches have their limits; they can be easily ”fooled” by cases of triviality or peculiarity of the class of problems that the algorithms are designed to handle. Furthermore, these approaches are usually blind to the interactions between the decision variables, thereby disrupting the partial solutions that are being built up along the way.
Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems - Volume 2
This book contains a collection of the papers accepted in the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2014), which was held in Singapore from 10-12th November 2014. The papers contained in this book demonstrate notable intelligent systems with good analytical and/or empirical results.
Differential Evolution in Electromagnetics
Differential evolution has proven itself a very simple while very powerful stochastic global optimizer. It has been applied to solve problems in many scientific and engineering fields. This book focuses on applications of differential evolution in electromagnetics to showcase its achievement and capability in solving synthesis and design problems in electromagnetics.Topics covered in this book include:• A comprehensive up-to-date literature survey on differential evolution• A systematic description of differential evolution• A topical review on applications of differential evolution in electromagnetics• Five new application examplesThis book is ideal for electromagnetic researchers and people in differential evolution community. It is also a valuable reference book for researchers and students in the optimization or electrical and electronic engineering field. In addition, managers and engineers in relevant fields will find it a helpful introductory guide.
Self-Sufficiency of an Autonomous Reconfigurable Modular Robotic Organism
This book describes how the principle of self-sufficiency can be applied to a reconfigurable modular robotic organism. It shows the design considerations for a novel REPLICATOR robotic platform, both hardware and software, featuring the behavioral characteristics of social insect colonies. Following a comprehensive overview of some of the bio-inspired techniques already available, and of the state-of-the-art in re-configurable modular robotic systems, the book presents a novel power management system with fault-tolerant energy sharing, as well as its implementation in the REPLICATOR robotic modules. In addition, the book discusses, for the first time, the concept of “artificial energy homeostasis” in the context of a modular robotic organism, and shows its verification on a custom-designed simulation framework in different dynamic power distribution and fault tolerance scenarios. This book offers an ideal reference guide for both hardware engineers and software developers involved in the design and implementation of autonomous robotic systems.
Adaptation and Hybridization in Computational Intelligence
This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.
Computational Intelligence for Big Data Analysis
The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.
