The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. PrefaceThis book is written for computer engineers, scientists, and students studying/working in reinforcement learning and artificial intelligence domains. The book collects recent advances in learning automaton theory as well as its applications in different computer science problems and domains. The book, in detail, describes the distributed learning automata and the cellular learning automata models for solving a variety of complex problems in wireless sensor networks, complex social networks, cognitive peer-to-peer networks, and adaptive Petri nets. Validation of the given learning automata-based methodologies is provided through extensive computer simulations. In addition, the book presents detailed mathematical and theoretical aspects of recent developments of cellular learning automata in real-world problems. The mathematical level in all chapters is well-suited within the gr...
In this paper, an adaptive Petri net (PN), capable of adaptation to environmental changes, is introduced by the fusion of learning automata and PN. In this new model, called learning automata-based adaptive PN (APN-LA), learning automata are used to resolve the conflicts among the transitions. In the proposed APN-LA model, transitions are portioned into several sets of conflicting transitions and each set of conflicting transitions is equipped with a learning automaton which is responsible for controlling the conflicts among transitions in the corresponding transition set. We also generalize the proposed APN-LA to ASPN-LA which is a fusion between LA and stochastic PN (SPN). An application of the proposed ASPN-LA to priority assignment in queuing systems with unknown parameters is also presented.
In recent years, artificial intelligence has had a tremendous impact on every field, and several definitions of its different types have been provided. In the literature, most articles focus on the extraordinary capabilities of artificial intelligence. Recently, some challenges such as security, safety, fairness, robustness, and energy consumption have been reported during the development of intelligent systems. As the usage of intelligent systems increases, the number of new challenges increases. Obviously, during the evolution of artificial narrow intelligence to artificial super intelligence, the viewpoint on the challenges such as security will be changed. In addition, the recent development of human-level intelligence cannot appropriately happen without considering whole challenges in designing intelligent systems. Considering the mentioned situation, no study in the literature summarizes the challenges in designing artificial intelligence. In this paper, a review of the challenges is presented. Then, some important research questions about the future dynamism of challenges and their relationships are answered.
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