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2002 AAAI Fall Symposium

作者： Brown, Scott M. Santos, Eugene Bell, Benjamin Electronic Systems Center Information Operations Program Office 240 Hall Blvd Suite 105 San AntonioTX78243-7058 United States University of Connecticut Dept of Computer Science & Engineering UTEB 191 Auditorium Rd. U-155 StorrsCT06269-3155 United States CHI Systems Inc. 1035 Virginia Drive Suite 300 Ft. WashingtonPA19034-3107 United States

Intelligence Preparation of the Battlespace (IPB) is a predominantly "gray matter-based" fusion and information synthesis process conducted to predict possible future adversary courses of action. The purpose is to understand where the enemy is in the battlespace, and to infer what we believe he will do next. From that understanding, military commanders plan their own course of action. As the state of the art improves, we are in a position to begin applying technologies to move the labor-intensive parts of IPB to the computer, allowing the planner to perform those tasks that are more suited to human capabilities. This is a primary focus of our research effort. This paper presents the approaches that we are adopting to acquire the knowledge necessary to build models to assist decision makers determine adversary intent. We discuss how the IPB process can assist with knowledge acquisition and we present a detailed discussion of our AII system and the techniques we have developed to collect and process the data necessary to map observations of the adversary into evidence to support reasoning about the adversary’s intent. Copyright © 2002, AAAI (***). All rights reserved.

关键词： Knowledge acquisition

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An efficient heuristic for routing and wavelength assignment in optical WDM networks

An efficient heuristic for routing and wavelength assignment...

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IEEE International Conference on Communications (ICC)

作者： Yongbing Zhang K. Taira H. Takagi S.K. Das Institute of Policy and Planning Sciences University of Tsukuba Tsukuba Ibaraki Japan Doctoral Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Department of Computer Science and Engineering University of Texas Arlington TX USA

ISBN: (纸本)0780374002

We propose an efficient heuristic algorithm that sets up and releases lightpaths for connection requests dynamically. We partition the routing and wavelength assignment (commonly known as RWA) problem into two subproblems and solves both of them using a well-known shortest path routing algorithm. For solving the routing subproblem, an auxiliary graph is created whereby the nodes and links in the original network are transformed to the edges and vertices, respectively, and the availability of each wavelength on the input and output links of a node as well as the number of available wavelength converters are taken into account in determining the weights of edges. Furthermore, for solving the wavelength assignment subproblem, an auxiliary graph is also utilized and the cost for wavelength conversion is taken into consideration in the edge weight function. A distinguished feature of our algorithm is that it employs more accurate network information on the availability of both the wavelengths and the wavelength converters than the existing algorithms in deciding the routing and the wavelength assignment. Simulation results show that our algorithm performs much better than previously proposed algorithms with comparable computation time, especially when the number of wavelengths is large while the number of converters at each node is limited.

关键词： Wavelength routing Wavelength assignment Optical fiber networks WDM networks Optical wavelength conversion Partitioning algorithms Heuristic algorithms Cost function Availability Computational modeling

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A soft expert system for the creative exploration of first principles of crystal-laser design

A soft expert system for the creative exploration of first p...

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IEEE International Conference on systems, Man and Cybernetics

作者： S.H. Rubin R.J. Rush M.H. Smith S.N. Jayaram Murthy L. Trajkovic SPAWAR Systems Center San Diego CA USA BISC Program University of California Berkeley CA USA Department of Computer Science Central Michigan University Mount Pleasant MI USA School of Engineering Science Simon Fraser University Burnaby BC Canada

A soft expert system is defined to be one that is qualitatively fuzzy. We present such a system known as KASER which stands for Knowledge Amplification by Structural Expert Randomization. KASER facilitates reasoning using domain specific expert and commonsense knowledge. It accomplishes this through object-classed predicates and an associated novel inference engine. It addresses the high cost associated with the knowledge acquisition bottleneck. It also enables the entry of a basis of rules and provides for the automatic extension of that basis through domain symmetries. We demonstrate an application for KASER in the design of an intelligent tutoring system that teaches the basic science of crystal-laser design. It enables the student to experiment with various design concepts and receive feedback on the functionality of the proposed design. This is possible without a need to preprogram all possible scenarios.

关键词： Expert systems Knowledge acquisition Intelligent systems USA Councils Engines Costs Production systems computer errors Hybrid intelligent systems Fuzzy systems

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From standard to non-standard semantics by semantics modifiers

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International Journal of Foundations of computer science 2001年第2期12卷 171-211页

作者： Abramov, Sergei Glück, Robert Program Systems Institute Russian Academy of Sciences RU-152140 Pereslavl-Zalessky Russian Federation Department of Information and Computer Science School of Science and Engineering Waseda University Shinjuku-ku Tokyo 169-8555 Japan

An approach for systematically modifying the semantics of programming languages by semantics modifiers is described. Semantics modifiers are a class of programs that allow the development of general and reusable semantics components. Language independence is achieved through the interpretive approach: an interpreter serves as a mediator between the new language and the language for which the non-standard semantics was implemented. Inverse computation, equivalence transformation and neighborhood analysis are shown to be semantics modifiers. Experiments with these modifiers show the computational feasibility of this approach. Seven modifier projections are given which allow the efficient implementation of non-standard interpreters and non-standard compilers by program specialization or other powerful program transformation methods. © 2001 World Scientific Publishing Company.

关键词： interpreters non-standard semantics partial evaluation program transformation programming languages supercompilation

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A theoretical model for autism

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Journal of Theoretical Medicine 2001年第4期3卷 271-286页

作者： Vidal De Caralho, Luís Alfred De Carvalho Ferreifa, Níve Fiszman, Adriana Program of Systems Engineering and Computer Science RJ Brazil Institute of Psychiatry Rio de Janeiro Brazil

Autism is a mental disorder characterized by deficits in socialization, communication, and imagination. Along with the deficits, autistic children may show savant skills ("islets of ability") of unknown origin that puzzles their families and the psychologists. Comorbidity with epilepsy and mental retardation has brought the researchers' attention to neurobiological and cognitive theories of the syndrome. The present article proposes a neurobiological model for the autism based on the fundamental biological process of neuronal competition. A neural network capable of defining neural maps-synaptic projections preserving neighborhoods between two neural tissues-simulates the process of neurodevelopment. Experiments were performed reducing the level of neural growth factor released by the neurons, leading to ill-developed maps and suggesting the cause of the aberrant neurogenesis present in autism. The computer simulations hint that brain regions responsible for the formation of higher level representations are impaired in autistic patients. The lack of this integrated representation of the world would result in the peculiar cognitive deficits of socialization, communication, and imagination and could also explain some "islets of abilities", like excellent memory for raw data and stimuli discrimination. The neuronal model is based on plausible biological findings and on recently developed cognitive theories of autism. Close relations are established between the computational properties of the neural network model and the cognitive theory of autism denominated "weak central coherence", bringing some insight to the understanding of the disorder.

关键词： Autism Cortical maps Neural networks Neurodevelopment Plasticity Weak central coherence

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Fuzzy Bayes predictor in electric load forecasting

Fuzzy Bayes predictor in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program Graduate Programs in Engineering COPPE Federal University of Rio de Janeiro Brazil Electric Power Research Center CEPEL CEPEL Brazil

ISBN: (纸本)0780370449

We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.

关键词： Load forecasting Fuzzy systems Power engineering and energy Smoothing methods Testing Niobium compounds systems engineering and theory computer science Predictive models Electric variables measurement

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Elastic neural net algorithm for cluster analysis

Elastic neural net algorithm for cluster analysis

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Brazilian Symposium on Neural Networks

作者： R.L. Salvini L.A. V. de Carvalho Program of Systems Engineering and Computer Science COPPE/UFRJ Rio de Janeiro Brazil

Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.

关键词： Neural networks Clustering algorithms Algorithm design and analysis Databases Performance analysis Testing Test pattern generators Iris Pattern recognition Convergence

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Biophysical and spectral characteristics of cool- and warm-season grasslands under three land management practices in Eastern Kansas

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Natural Resources Research 2000年第4期9卷 321-331页

作者： Guo, Xulin Price, Kevin P. Stiles, James M. Kansas Applied Remote Sensing Program (KARS) Department of Geography University of Kansas Lawrence KS 66045 2335 Irving Hill Road United States Radar Systems and Remote Sensing Laboratory (RSL) Department of Electrical Engineering and Computer Science University of Kansas Lawrence KS 66045 2335 Irving Hill Road United States Lawrence KS 66045 2335 Irving Hill Road United States

Grasslands are the largest of the Earth's four major vegetation types and are among the most agriculturally productive lands. Grassland management practices alter biophysical factors, such as plant species composition, soil structure and chemistry, and primary productivity. Large-scale changes in land management practices can have significant impacts on available natural resources, therefore impacting a regional socioeconomics. For these reasons, an improved understanding of the geographic distribution of changing land management practices and how these changes affect grassland biophysical characteristics of an area is critical to improve land use planning and practices. This study investigated the spectral characteristics, as measured using satellite remotely sensed data, of three land management practices [grazing, haying, and management under Conservation Reserve program (CRP)] on areas dominated by cool- and warm-season grasslands. The satellite imagery was acquired in May, July, and September in 1997 over Douglas County, Kansas. Biomass, vegetation cover, and species richness data were collected around the satellite overflight periods. Our results indicate that the biophysical characteristics and spectral reflectances are significantly different among the three management practices over cool- and warm-season grasslands. Interestingly, sites grazed by livestock had the highest species richness and the highest forb cover, although the total cover is almost the same as the other practices, and they have the highest near-infrared reflectance throughout the growing season. © 2000 International Association for Mathematical Geology.

关键词： Landsat Tallgrass prairie Thematic Mapper (TM)

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A comparison of training algorithms for DHP adaptive critic neurocontrol

A comparison of training algorithms for DHP adaptive critic ...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.G. Lendaris T.T. Shannon A. Rustan Systems Science Ph.D. Program and Electrical & Computer Engineering Department Portland State University USA

A variety of alternate training strategies for implementing the dual heuristic programming (DHP) method of approximate dynamic programming in the neurocontrol context are explored. The DHP method of controller training has been successfully demonstrated by a number of authors on a variety of control problems in recent years, but no unified view of the implementation details of the method has yet emerged. A number of options are described for sequencing the training of the controller and critic networks in DHP implementations. Results are given about their relative efficiency and the quality of the resulting controllers for two benchmark control problems.

关键词： Neural networks Shape control Dynamic programming Equations Design optimization Amorphous materials Design methodology Feedback loop Supervised learning Process design

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Recurrent neural gas in electric load forecasting

Recurrent neural gas in electric load forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： M.A. Teixeira G. Zaverucha V.N.A.L. da Silva G.F. Ribeiro Systems Engineering and Computer Science Program-Graduate Programs in Engineering Federal University of Rio de Janeiro Brazil Electric Power Research Center Brazil

We have proposed for the task of hourly electric load forecasting a hybrid neural system combining unsupervised and supervised learning. The system consists of a recurrent neural gas (RNG) network and many Elman neural networks (ENs). RNG is a modification we introduced in the neural gas (NG) network in order to enable it to do clustering using a sequence of input data. For verifying the RNG's performance, many architectures are compared in the learning of global and local models. In a global model only one supervised network is trained and in a local model the training examples are grouped by a clustering algorithm and each one of these groups is sent to different supervised networks. These architectures use different clustering algorithms (NG and RNG) or different supervised networks for prediction (ENs that are trained by backpropagation or backpropagation through time, and feedforward networks).

关键词： Load forecasting Clustering algorithms Neural networks Predictive models Power engineering and energy Backpropagation algorithms Supervised learning systems engineering and theory computer science Recurrent neural networks

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