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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Chris Miles Sushil J. Louis Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada Reno USA

We investigate the use of genetic algorithms to play real-time computer strategy games. To overcome the knowledge acquisition bottleneck found in using traditional expert systems, scripts, or decision trees we use genetic algorithms to evolve game players. The spatial decision makers in our game players use influence maps as a basic building block from which they construct and evolve trees containing complex game playing strategies. Information from influence map trees is combined with that from an A* pathfinder, and used by another genetic algorithm to solve the allocation problems present within many game decisions. As a first step towards evolving strategic players we develop this system in the context of a tactical game. Results show the co-evolution of coordinated attacking and defending strategies superior to their hand-coded counterparts

关键词： Game theory Genetic algorithms Resource management computer science Genetic engineering Humans Earth computer architecture Decision making Real time systems

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A Behavior-Based Architecture for Realistic Autonomous Ship Control

A Behavior-Based Architecture for Realistic Autonomous Ship ...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Adam Olenderski Monica Nicolescu Sushil J. Louis Robotics Research Laboratory Department of Computer Science and Engineering University of Nevada Reno USA Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada Reno USA

Game environments provide a good domain for serious simulations such as those used in training navy conning officers. Currently, a typical training scenario requires multiple personnel to play each of the boats and thus is expensive. We propose an approach to addressing this issue by developing intelligent, autonomous controllers for each boat. Significant challenges toward achieving these goals are the realism of behavior exhibited by the automated boats and their realtime response to change. In this paper we describe a control architecture that enables the real-time response of boats and the repertoire of realistic behaviors we developed for this application. We demonstrate the capabilities of our system with experimental results

关键词： Marine vehicles Boats Automatic control Personnel Robot kinematics Navigation computer architecture computer science Robustness Costs

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Multi-modal biometric recognition

Multi-modal biometric recognition

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International Conference on computer science and Its Applications, CIIA 2006

作者： Adjoudj, Réda Boukelif, Aoued Evolutionary Engineering and Distributed Information Systems Laboratory EEDIS Computer Science Department University of Sidi Bel-Abbès Algeria Digital Signal Processing Laboratory DSP Electronic Department University of Sidi Bel-Abbès Algeria

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Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks

Search Space Analysis of Recurrent Spiking and Continuous-ti...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Ventresca B. Ombuki Systems Design Engineering and member of the Pattern Analysis and Machine Intelligence Laboratory University of Waterloo Waterloo ONT Canada Department of Computing and Information Science University of Guelph Guelph ONT Canada Computer Science Department Brock University Saint Catharines ONT Canada

The problem of designing recurrent continuous-time and spiking neural networks is NP-Hard. A common practice is to utilize stochastic searches, such as evolutionary algorithms, to automatically construct acceptable networks. The outcome of the stochastic search is related to its ability to navigate the search space of neural networks and discover those of high quality. In this paper we investigate the search space associated with designing the above recurrent neural networks in order to differentiate which network should be easier to automatically design via a stochastic search. Our investigation utilizes two popular dynamic systems problems; (1) the Henon map and (2) the inverted pendulum as a benchmark.

关键词： Neural networks Recurrent neural networks Stochastic processes Chaotic communication Neurons Biological system modeling evolutionary computation Navigation Helium Visualization

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Speeding up fractal image compression by genetic algorithms

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MULTIDIMENSIONAL systems AND SIGNAL PROCESSING 2005年第2期16卷 217-236页

作者： Mohamed, F Aoued, B Department of Computer Science Evolutionary Engineering and Distributed Information Systems Laboratory EEDIS University of Sidi Bel-Abbès Algeria Department of Electronics Communications Networks Architectures and Multimedia Laboratory University of Sidi Bel Abbès Algeria

The main problem with all fractal compression implementation is execution time. Algorithms can spend hours to compress a single image. Most of the major variants of the standard algorithm for speeding up computation time have led to a bad-quality or a lower compression ratio. For example, the Fisher's [ 7] proposed classification pattern greatly accelerated the algorithm, but image quality was poor due to the search-space reduction imposed by the classification, which eleminates a lot of good solutions. By using genetic algorithms to address the problem, we optimize the domain blocks search. We explore all domain blocks present in the image but not in exhaustive way ( like a standard algorithm) and without omitting any possible block (solution) as a classification pattern does. A genetic algorithm is the unique method for satisfying these constraints. And it is a way to do be a random search because the genetic one is directed by fitness selection, which produces optimal solutions. Our goal in this work is to use a genetic algorithm to solve the IFS inverse problem and to build a fractal compression algorithm based on the genetic optimization of a domain blocks search. we have also implemented standard Barnsley algorithm, the Y. Fisher based on classification, and the genetic compression algorithm with quadtree partitioning. A population of transformations was evolved for each range block, and the result is compared with the standard Barnsely algorithm and the Fisher algorithm = based classification. We deduced an optimal set of values for the best parameters combination, and we can also specify the best combination for each desired criteria: best compression ratio, best image quality, or quick compression process. By running many test images, we experimentally found the following set of optimal values of all the algorithm parameters that ensure compromise between execution time and solutions optimality: Population size = 100, Maximum generations = 20, Crossover rat

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Learning classifier systems for user context learning

Learning classifier systems for user context learning

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Congress on evolutionary Computation

作者： A. Shankar S. Louis Evolutionary Computing Systems Laboratory Department of Computer Science and Engineering University of Nevada Reno USA

Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCS's performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.

关键词： Application software Machine learning computer applications Decision trees computer interfaces Laboratories computer science Keyboards Mice Speech

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Context learning can improve user interaction

Context learning can improve user interaction

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IEEE International Conference on Information Reuse and Integration (IRI)

作者： S.J. Louis A. Shankar Evolutionary Computing Systems Laboratory ECSL Department of Computer Science and Engineering University of Nevada Reno NV USA

Current computer applications lack user context and do not learn to use this context to improve user interaction. In this paper we present Sycophant, a context learning calendar application program which learns a mapping from user-related contextual features to application actions. In this preliminary work, Sycophant achieves good accuracy in learning this mapping. In addition, we find that including external context such as the presence or absence of motion and speech provides better performance in learning accurate mappings.

关键词： Application software Speech Keyboards Mice computer applications Machine learning algorithms Laboratories computer science Calendars Clocks

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Artificial neural network-based face recognition

Artificial neural network-based face recognition

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International Symposium on Communications Control and Signal Processing (ISCCSP)

作者： A. Reda B. Aoued Evolutionary Engineering and Distributed Information Systems Laboratory EEDIS Computer Science Department University of Sidi-Bel Abbes Algeria Digital Signal processing laboratory Electronic Department University of Sidi-Bel Abbes Algeria

This document demonstrates how a face recognition system can be designed with artificial neural network. Note that the training process did not consist of a single call to a training function. Instead, the network was trained several times on various input ideal and noisy images, the images that contents faces. In this case training a network on different sets of noisy images forced the network to learn how to deal with noise, a common problem in the real world.

关键词： Artificial neural networks Face recognition Neural networks Neurons Face detection Image databases Laboratories Humans Design engineering Distributed information systems

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Highlights on the evolutionary engineering approach: the EE-method

Highlights on the evolutionary engineering approach: the EE-...

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ACS/IEEE International Conference on computer systems and Applications

作者： A. Lehireche A. Rahmoun A. Gafour Evolutionary Engineering and Distributed Information Systems Laboratory EEDIS Computer Science Department University Djillali Liabes of Sidi Bel-Abbès Algeria

ISBN: (纸本)0769511651

Recent significant advances in artificial intelligence have led to the emergence of new concepts of soft computing genetic programming, evolutionary engineering, hybrid intelligent systems, and artificial life. EE design relies on evolutionary algorithms to construct or evolve complex systems. This paper is intended to highlight the concept of evolutionary engineering, the motivations behind the use of EE in building complex systems, and a step by step methodology for designing such systems. Finally, we emphasize on a typical example of "Brain Building design project using EE: the CAM Brain Machine.

关键词： Artificial intelligence Genetic programming Genetic engineering systems engineering and theory Hybrid intelligent systems Algorithm design and analysis evolutionary computation Design engineering Buildings Design methodology

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Preface

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2037卷 V-VI页

作者： Boers, Egbert J. W. Cagnoni, Stefano Gottlieb, Jens Hart, Emma Lanzi, Pier Luca Raidl, Günther R. Smith, Robert E. Tijink, Harald Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 Leiden Netherlands Dept. of Computer Engineering University of Parma Parco Area delle Scienze 181/a Parma Italy SAP AG Neurottstrasse 16 Walldorf Germany Napier University School of Computing 219 Colinton Road Edinburgh United Kingdom Polytechnic of Milan Piazza Leonardo da Vinci 32 Milan Italy Algorithms and Data Structur es Group Institute of Computer Graphics Vienna University of Technology Favoritenstrasse 9-11/186 Vienna Austria The Intelligent Computer Systems Centre The University of The West of England Coldharbour Lane Frenchay Bristol United Kingdom Data and Knowledge Systems Department National Aerospace Laboratory NLR P.O. Box 153 Emmeloord Netherlands

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