<abstract xmlns="http://www.w3.org/1999/xhtml">Evolutionary game theory is used to model the evolution of competing strategies in a population of players. Evolutionary stability of a strategy is a dynamic equilibrium, in which any competing mutated strategy would be wiped out from a population. If a strategy is weak evolutionarily stable, the competing strategy may manage to survive within the network. Understanding the network-related factors that affect the evolutionary stability of a strategy would be critical in making accurate predictions about the behaviour of a strategy in a real-world strategic decision making environment. In this work, we evaluate the effect of network topology on the evolutionary stability of a strategy. We focus on two well-known strategies known as the Zero-determinant strategy and the Pavlov strategy. Zero-determinant strategies have been shown to be evolutionarily unstable in a well-mixed population of players. We identify that the Zero-determinant strategy may survive, and may even dominate in a population of players connected through a non-homogeneous network. We introduce the concept of ‘topological stability’ to denote this phenomenon. We argue that not only the network topology, but also the evolutionary process applied and the initial distribution of strategies are critical in determining the evolutionary stability of strategies. Further, we observe that topological stability could affect other well-known strategies as well, such as the general cooperator strategy and the cooperator strategy. Our observations suggest that the variation of evolutionary stability due to topological stability of strategies may be more prevalent in the social context of strategic evolution, in comparison to the biological context.</abstract>

Evolutionary Stable Strategies In Networked Games: The Influence Of Topology

<abstract xmlns="http://www.w3.org/1999/xhtml">Pulsed power network is proposed for reliable and low loss electrical power distribution among various type of power sources and consumers. The proposed scheme is a derivative of power packet network so far investigated that has affinity with dispersion type power sources and has manageability of energy coloring in the process of power distribution. In addition to these advantages, the proposed scheme has system reliability and low loss property because of its intelligent operation performed by individual nodes and direct relaying by power routers. In the proposed scheme, power transmission is decomposed into a series of electrical pulses placed at specified power slots in continuous time frames that are synchronized over the network. The power slots are pre-reserved based on information exchanges among neighboring nodes following inherent algorithm of the proposed scheme. Because of this power slots reservation based on decentralized intelligence, power pulses are directly transmitted from various power sources to consumers with the least power dissipation even though a partial failure occurs in the network. The network operations with the proposed scheme is simulated to confirm the algorithms for the power slots reservation and to evaluate the power network capacity.</abstract>

Pulsed Power Network Based On Decentralized Intelligence For Reliable And Lowloss Electrical Power Distribution

<abstract xmlns="http://www.w3.org/1999/xhtml">Several hybrid neuron models, which combine continuous spike-generation mechanisms and discontinuous resetting process after spiking, have been proposed as a simple transition scheme for membrane potential between spike and hyperpolarization. As one of the hybrid spiking neuron models, Izhikevich neuron model can reproduce major spike patterns observed in the cerebral cortex only by tuning a few parameters and also exhibit chaotic states in specific conditions. However, there are a few studies concerning the chaotic states over a large range of parameters due to the difficulty of dealing with the state dependent jump on the resetting process in this model. In this study, we examine the dependence of the system behavior on the resetting parameters by using Lyapunov exponent with saltation matrix and Poincaré section methods, and classify the routes to chaos.</abstract>

Department of Management Information Science, Fukui University of Technology, 3-6-1 Gakuen, Fukui, Fukui, 910-8505 Japan

Graduate School of Applied Informatics, University of Hyogo, 7-1-28 Chuo-ku, Kobe, Hyogo, 650-8588 Japan

Center for Information and Neural Networks, National Institute of Information and Communications Technology, 588-2 Iwaoka, Iwaoka-cho, Nishi-ku, Kobe, Hyogo, 651-2492 Japan

Chaotic States Induced By Resetting Process In Izhikevich Neuron Model

<abstract xmlns="http://www.w3.org/1999/xhtml">Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E &amp; P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.</abstract>

Institute of Petroleum Engineering, Clausthal University of Technology, Germany

Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

<abstract xmlns="http://www.w3.org/1999/xhtml">This paper proposes a method that discovers various sequential patterns from sequential data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many previous methods discover frequent sequential patterns from the data. However, the patterns tend to be similar to each other because they are composed of limited items. The patterns do not always correspond to the interests of analysts. Therefore, this paper tackles on the issue discovering various sequential patterns. The proposed method decides redundant sequential patterns by evaluating the variety of items and deletes them based on three kinds of delete processes. It can discover various sequential patterns within the upper bound for the number of sequential patterns given by the analysts. This paper applies the method to the synthetic sequential data which is characterized by number of items, their kind, and length of sequence. The effect of the method is verified through numerical experiments.</abstract>

Big Data Cloud Technology Center, Toshiba Corporation Cloud & Solutions Company, 72-34, Horikawa-cho, Saiwai-ku, Kawasaki 212-8585, Japan

Advanced IT Research and Development Center, Toshiba Solutions Company, 3-22, Katamachi, Fuchu, Tokyo 183-8512, Japan

A New Approach For Discovering Top-<italic>K</italic> Sequential Patterns Based On The Variety Of Items

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 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE FellowFrequency of publishing: quarterlyMonths of publication: January, April, July, October  ISSN (print): 2083-2567ISSN (on-line): 2449-6499  Journal of Artificial Intelligence and Soft Computing Research is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.  Why JAISCR?   Premier source of high quality research,  We have practice in publishing innovative articles dealing with various aspects of artificial intelligence methods and soft computing,  Extensive scope,  We are focused on permanent development of our journal.   JAISCR research areas  JAISCR publishes high quality, innovative research results in various areas of artificial intelligence and soft computing. These areas include, but are not limited to:   AI in Modelling and Simulation,  AI in Scheduling and Optimization,  Automated Reasoning and Inference,  Bioinformatics,  Cognitive Aspects of AI,  Computer Vision and Speech Understanding,  Data Mining,  Distributed Intelligent Processing,  Evolutionary Design,  Expert Systems,  Fuzzy Modelling and Control,  Hardware Implementations,  Heuristic Search,  Hybrid Models,  Information Retrieval,  Intelligent Database Systems,  Knowledge Engineering,  Mechatronics,  Multi-agent Systems,  Natural Language Processing,  Neural Network Theory and Architectures,  Neuro-informatics and Bio-inspired Models,  Pattern Recognition,  Robotics and Related Fields,  Rough Sets Theory: Foundations and Applications,  Supervised and Unsupervised Learning,  Swarm Intelligence and Systems,  Web Intelligence Applications &amp; Search,  Various Applications.   Submitting to JAISCR - a great advantage for the authorsWhy?   Fair and thorough peer review (at least three referees are required for the acceptance of each article),  Fast and efficient publication process,  Polishing the structure and technical format of each submission,  Open access (all articles published in JAISCR are open access - freely available on Sciendo platform),  Permanence (because of the open access form, permanent accessibility is assured),  High visibility within the field (the author's work is freely accessible to a global audience. In addition, articles are available through Sciendo),  Promotion of the published papers in the scientific community.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Journal of Artificial Intelligence and Soft Computing Research

Volume 5 (2015): Issue 2 (April 2015)

Evolutionary Stable Strategies In Networked Games: The Influence Of Topology

Pulsed Power Network Based On Decentralized Intelligence For Reliable And Lowloss Electrical Power Distribution

Chaotic States Induced By Resetting Process In Izhikevich Neuron Model

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

A New Approach For Discovering Top-K Sequential Patterns Based On The Variety Of Items

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