http://www.bibsonomy.org/burst/concept/tag/descriptionBibSonomy BuRST Feed for /concept/tag/description2008-12-02T01:31:13+01:00http://www.bibsonomy.org/bibtex/278aa652e6dc362722684b8546fac28d4/kirylenkakirylenka2008-11-05T17:59:31+01:00Description CDWA wismasys0809 <span style="color:#555555;">Murtha <a href="http://www.bibsonomy.org/author/Baca">Baca</a> and Patricia <a href="http://www.bibsonomy.org/author/Harpring">Harpring</a> (eds.). </span><em>J. Paul Getty Trust &amp; College Art Association, Inc., </em>(<em>2008</em>)Wed Nov 05 17:59:31 CET 2008Categories for the Description of Works of Art (CDWA). 2008Description CDWA wismasys0809 http://www.bibsonomy.org/bibtex/278aa652e6dc362722684b8546fac28d4/roborobo2008-11-05T17:55:51+01:00work Description CDWA art wismasys0809 <span style="color:#555555;">Murtha <a href="http://www.bibsonomy.org/author/Baca">Baca</a> and Patricia <a href="http://www.bibsonomy.org/author/Harpring">Harpring</a> (eds.). </span><em>J. Paul Getty Trust &amp; College Art Association, Inc., </em>(<em>2008</em>)Wed Nov 05 17:55:51 CET 2008Categories for the Description of Works of Art (CDWA). 2008work Description CDWA art wismasys0809 http://www.bibsonomy.org/bibtex/2fa9f0499f0df0f5c993aa8d61b413868/p.maghferatp.maghferat2008-10-29T18:15:37+01:00description wismasys0809' logics <span style="color:#555555;">Daniele <a href="http://www.bibsonomy.org/author/Nardi">Nardi</a> and Ronald J. <a href="http://www.bibsonomy.org/author/Brachman">Brachman</a> </span><em>The Description Logic Handbook. Theory, Implementation and Applications, </em><em>Cambridge University Press, </em>(<em>2003</em>)Wed Oct 29 18:15:37 CET 2008The Description Logic Handbook. Theory, Implementation and Applications1-40An introduction to description logics2003description wismasys0809' logics This introduction presents the main motivations for the development of Description Logics (DL) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier seman- tic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subse- quently, we introduce the basic features of Description Logic languages and related reasoning techniques. Description Logic languages are then viewed as the core of knowledge represen- tation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Com- puter Science. We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.http://www.bibsonomy.org/bibtex/2fa9f0499f0df0f5c993aa8d61b413868/kirylenkakirylenka2008-10-29T18:03:19+01:00description logics wismasys0809 <span style="color:#555555;">Daniele <a href="http://www.bibsonomy.org/author/Nardi">Nardi</a> and Ronald J. <a href="http://www.bibsonomy.org/author/Brachman">Brachman</a> </span><em>The Description Logic Handbook. Theory, Implementation and Applications, </em><em>Cambridge University Press, </em>(<em>2003</em>)Wed Oct 29 18:03:19 CET 2008The Description Logic Handbook. Theory, Implementation and Applications1-40An introduction to description logics2003description logics wismasys0809 This introduction presents the main motivations for the development of Description Logics (DL) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it. We first address the relationship between Description Logics and earlier seman- tic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subse- quently, we introduce the basic features of Description Logic languages and related reasoning techniques. Description Logic languages are then viewed as the core of knowledge represen- tation systems, considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed. Finally, we address the relationship of Description Logics to other fields of Com- puter Science. We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.http://www.bibsonomy.org/bibtex/278aa652e6dc362722684b8546fac28d4/juverjuver2008-10-27T22:39:47+01:00CDWA description art wismasys0809 Categories <span style="color:#555555;">Murtha <a href="http://www.bibsonomy.org/author/Baca">Baca</a> and Patricia <a href="http://www.bibsonomy.org/author/Harpring">Harpring</a> (eds.). </span><em>J. Paul Getty Trust &amp; College Art Association, Inc., </em>(<em>2008</em>)Mon Oct 27 22:39:47 CET 2008Categories for the Description of Works of Art (CDWA). 2008CDWA description art wismasys0809 Categories http://www.bibsonomy.org/bibtex/2915934abb92646a0b520e5d464f0083f/pdeleenhpdeleenh2008-07-07T16:45:32+02:00description logics <span style="color:#555555;">F. <a href="http://www.bibsonomy.org/author/Baader">Baader</a> and D. <a href="http://www.bibsonomy.org/author/McGuinness">McGuinness</a> and D. <a href="http://www.bibsonomy.org/author/Nardi">Nardi</a> and P.F. <a href="http://www.bibsonomy.org/author/Patel-Schneider">Patel-Schneider</a> </span><em>Cambridge University Press, </em>(<em>2003</em>)Mon Jul 07 16:45:32 CEST 2008The Description Logic Handbook: Theory, Implementation and Applications2003description logics http://www.bibsonomy.org/bibtex/23cc8affd3aa735ebee6a31746d62c356/pdeleenhpdeleenh2008-07-07T16:45:32+02:00UML, description logics <span style="color:#555555;">Andrea <a href="http://www.bibsonomy.org/author/{Cal\&#039;i}">Cal'i</a> and Diego <a href="http://www.bibsonomy.org/author/Calvanese">Calvanese</a> and Giuseppe De <a href="http://www.bibsonomy.org/author/Giacomo}">Giacomo</a> and Maurizio <a href="http://www.bibsonomy.org/author/Lenzerini">Lenzerini</a> </span><em>Proc. IJCAR Workshop on Precise Modelling and Deduction for Object-oriented Software Development (PMD), </em>(<em>2001</em>)Mon Jul 07 16:45:32 CEST 2008Proc. IJCAR Workshop on Precise Modelling and Deduction for Object-oriented Software Development (PMD)Reasoning on {U}{M}{L} Class Diagrams in Description Logics2001UML, description logics http://www.bibsonomy.org/bibtex/2e94eff9d38826d0ca6737422b69dfc0f/pdeleenhpdeleenh2008-07-07T16:45:32+02:00software architecture, description language architecture <span style="color:#555555;">N. <a href="http://www.bibsonomy.org/author/Medvidovic">Medvidovic</a> and R. N. <a href="http://www.bibsonomy.org/author/Taylor">Taylor</a> </span><em>SIGSOFT Software Engineering Notes. ESEC/FSE'97</em><em>22(6):60--76</em><em>November1997. </em>Mon Jul 07 16:45:32 CEST 2008SIGSOFT Software Engineering Notes. ESEC/FSE'97November660--76A framework for classifying and comparing architecture description languages221997software architecture, description language architecture http://www.bibsonomy.org/bibtex/2cefba077713d56fab63f091cc3f80691/pdeleenhpdeleenh2008-07-07T16:45:32+02:00software architecture, description language architecture <span style="color:#555555;">N. <a href="http://www.bibsonomy.org/author/Medvidovic">Medvidovic</a> and R. N. <a href="http://www.bibsonomy.org/author/Taylor">Taylor</a> </span><em>26(1):70--93</em>(<em>2000</em>)Mon Jul 07 16:45:32 CEST 2008170--93A classification and comparison framework for software architecture description languages262000software architecture, description language architecture http://www.bibsonomy.org/bibtex/238be4a512c473457554506ed3b0f65e8/pdeleenhpdeleenh2008-07-07T16:45:32+02:00logics, description object-oriented, UML <span style="color:#555555;">Ragnhild Van Der <a href="http://www.bibsonomy.org/author/Straeten}">Straeten</a> </span><em>Proc. Int'l Workshop on Description Logics (DL), </em>(<em>2002</em>)Mon Jul 07 16:45:32 CEST 2008Proc. Int'l Workshop on Description Logics (DL)Using Description Logic in Object-Oriented Software Development2002logics, description object-oriented, UML http://www.bibsonomy.org/bibtex/2efaba6a27bf95c445d2d8092137fc844/pdeleenhpdeleenh2008-07-07T16:45:32+02:00logics, UML, software refactoring, description inconsistency consistency maintenance, model-driven development, management <span style="color:#555555;">Ragnhild Van Der <a href="http://www.bibsonomy.org/author/Straeten}">Straeten</a> and Viviane <a href="http://www.bibsonomy.org/author/Jonckers">Jonckers</a> and Tom <a href="http://www.bibsonomy.org/author/Mens">Mens</a> </span><em>UML 2004 - The Unified Modeling Language, </em><em>3273, </em><em>page305--319. </em><em>October2004. </em>Mon Jul 07 16:45:32 CEST 2008{U}{M}{L} 2004 - The Unified Modeling LanguageOctober305--319Supporting Model Refactorings through Behaviour Inheritance Consistencies32732004logics, UML, software refactoring, description inconsistency consistency maintenance, model-driven development, management http://www.bibsonomy.org/bibtex/24d92ab476b77a1c57f04ffbdf5e1e5cd/pdeleenhpdeleenh2008-07-07T16:45:32+02:00UML, description consistency maintenance, logics <span style="color:#555555;">Ragnhild Van Der <a href="http://www.bibsonomy.org/author/Straeten}">Straeten</a> and Tom <a href="http://www.bibsonomy.org/author/Mens">Mens</a> and Jocelyn <a href="http://www.bibsonomy.org/author/Simmonds">Simmonds</a> and Viviane <a href="http://www.bibsonomy.org/author/Jonckers">Jonckers</a> </span><em>UML 2003 - The Unified Modeling Language, </em><em>2863, </em><em>page326--340. </em>(<em>2003</em>)Mon Jul 07 16:45:32 CEST 2008{U}{M}{L} 2003 - The Unified Modeling Language326--340Using Description Logics to Maintain Consistency Between {U}{M}{L} Models28632003UML, description consistency maintenance, logics http://www.bibsonomy.org/bibtex/2d6ac2b3805bdf23d9bfc3a06f14202ca/pdeleenhpdeleenh2008-07-07T16:45:32+02:00logics, description UML <span style="color:#555555;">Ragnhild Van Der <a href="http://www.bibsonomy.org/author/Straeten}">Straeten</a> and Jocelyn <a href="http://www.bibsonomy.org/author/Simmonds">Simmonds</a> and Tom <a href="http://www.bibsonomy.org/author/Mens">Mens</a> </span><em>Proc. Int'l Workshop on Description Logics (DL), </em><em>September2003. </em>Mon Jul 07 16:45:32 CEST 2008Proc. Int'l Workshop on Description Logics (DL)SeptemberDetecting inconsistencies between {U}{M}{L} models using description logic2003logics, description UML http://www.bibsonomy.org/bibtex/268879af3375eb231c3fa888955a82307/brazovayeyebrazovayeye2008-06-19T17:46:40+02:00evolution, automated hardware logic, optimisation, languages, design, software programming, programmable description variants CAD, algorithms, management, VHSIC Description Language, arrays, FPGA, gate field system Hardware genetic requirements, circuit configuration VHDL, design <span style="color:#555555;">Robert L. <a href="http://www.bibsonomy.org/author/Popp">Popp</a> and David J. <a href="http://www.bibsonomy.org/author/Montana">Montana</a> and Richard R. <a href="http://www.bibsonomy.org/author/Gassner">Gassner</a> and Gordon <a href="http://www.bibsonomy.org/author/Vidaver">Vidaver</a> and Suraj <a href="http://www.bibsonomy.org/author/Iyer">Iyer</a> </span><em>IEEE International Conference on Systems, Man, and Cybernetics, </em><em>3, </em><em>page2184--2189. </em><em>San Diego, CA USA, </em><em>IEEE, </em><em>11-14 October1998. </em>Thu Jun 19 17:46:40 CEST 2008San Diego, CA USAIEEE International Conference on Systems, Man, and Cybernetics11-14 October2184--2189Automated hardware design using genetic programming, {VHDL}, and {FPGAs}31998evolution, automated hardware logic, optimisation, languages, design, software programming, programmable description variants CAD, algorithms, management, VHSIC Description Language, arrays, FPGA, gate field system Hardware genetic requirements, circuit configuration VHDL, design We have developed a completely automated approach to hardware design based on integrating three core technologies into one comprehensive system, namely genetic programming (GP), the VHSIC Hardware Description Language (VHDL) and field programmable gate arrays (FPGAs). Our system uses an automated GP engine, as opposed to a human designer, to evolve a hardware design composed of one or more FPGAs that will maximally achieve an application's software requirements. Several variants of our system exist; other variants are currently under development. The focus of this paper is to describe our original system design and its most recent revision to datehttp://www.bibsonomy.org/bibtex/2d952894c34b9443d5dc27a6d0bc0a380/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00Evolutionary percentage principle, absolute market programming, description genetic Minimum error, length Efficient algorithms, hypothesis Mean computation, <span style="color:#555555;">Shu-Heng <a href="http://www.bibsonomy.org/author/Chen">Chen</a> and Chia-Hsuan <a href="http://www.bibsonomy.org/author/Yeh">Yeh</a> </span><em>Journal of Economic Dynamics and Control</em><em>21(6):1043--1063</em><em>1 June1997. </em>Thu Jun 19 17:35:00 CEST 2008Journal of Economic Dynamics and Control1 June61043--1063Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming211997Evolutionary percentage principle, absolute market programming, description genetic Minimum error, length Efficient algorithms, hypothesis Mean computation, From a computation-theoretic standpoint, this paper formalises the notion of unpredictability in the efficient market hypothesis (EMH) by a biological-based search program, i.e., genetic programming (GP). This formalization differs from the traditional notion based on probabilistic independence in its treatment of <I>search</I>. Compared with the traditional notion, a GP-based search provides an explicit and efficient search program upon which an objective measure for predictability can be formalized in terms of search intensity and chance of success in the search. This will be illustrated by an example of applying GP to predict chaotic time series. Then the EMH based on this notion will be exemplified by an application to the Taiwan and US stock market. A short-term sample of TAIEX and S&P 500 with the highest complexity defined by Rissanen's minimum description length principle (MDLP) is chosen and tested. It is found that, while linear models cannot predict better than the random walk, a GP-based search can beat random walk by 50%. It, therefore, confirms the belief that while the short-term nonlinear regularities might still exist, the search costs of discovering them might be too high to make the exploitation of these regularities profitable, hence the efficient market hypothesis is sustained.http://www.bibsonomy.org/bibtex/243621b2fa33526227432fa7f8744e16b/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00selling minimum principle, short programming, bounded rationality, description genetic length Kolmogorov complexity, algorithms, <span style="color:#555555;">Shu-Heng <a href="http://www.bibsonomy.org/author/Chen">Chen</a> and Chia-Hsuan <a href="http://www.bibsonomy.org/author/Yeh">Yeh</a> </span><em>Annals of Operations Research</em><em>97(1-4):265--286</em><em>December2000. </em>Thu Jun 19 17:35:00 CEST 2008Annals of Operations ResearchDecember1-4265--286Simulating economic transition processes by genetic programming972000selling minimum principle, short programming, bounded rationality, description genetic length Kolmogorov complexity, algorithms, Recently, genetic programming has been proposed to model agents' adaptive behaviour in a complex transition process where uncertainty cannot be formalised within the usual probabilistic framework. However, this approach has not been widely accepted by economists. One of the main reasons is the lack of the theoretical foundation of using genetic programming to model transition dynamics. Therefore, the purpose of this paper is two-fold. First, motivated by the recent applications of algorithmic information theory in economics, we would like to show the relevance of genetic programming to transition dynamics given this background. Second, we would like to supply two concrete applications to transition dynamics. The first application, which is designed for the pedagogic purpose, shows that genetic programming can simulate the non-smooth transition, which is difficult to be captured by conventional toolkits, such as differential equations and difference equations. In the second application, genetic programming is applied to simulate the adaptive behavior of speculators. This simulation shows that genetic programming can generate artificial time series with the statistical properties frequently observed in real financial time series.http://www.bibsonomy.org/bibtex/2ec795b9d7db4606fcafa1c6aa47bc3a6/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00minimum (SAR) synthetic image radar aperture learning, Feature (MDL), programming, image, description genetic length operator, feature algorithms, primitive <span style="color:#555555;">Yingqiang <a href="http://www.bibsonomy.org/author/Lin">Lin</a> and Bir <a href="http://www.bibsonomy.org/author/Bhanu">Bhanu</a> </span><em>IEEE Transactions on Systems, Man and Cybernetics, Part B</em><em>35(3):538--547</em><em>June2005. </em>Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Systems, Man and Cybernetics, Part BJune3538--547Object Detection via Feature Synthesis Using {MDL}-Based Genetic Programming352005minimum (SAR) synthetic image radar aperture learning, Feature (MDL), programming, image, description genetic length operator, feature algorithms, primitive we use genetic programming (GP) to synthesise composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labelling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection.http://www.bibsonomy.org/bibtex/2f78d96a0fffbae7a483f2d51e766dac4/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00AI, linguistics, cognition, coordination, Semantic the networks, algorithmic human logics, biology, multi-agent intelligence, programming, Web social description genetic algorithms, artificial null human-level <span style="color:#555555;">David L. <a href="http://www.bibsonomy.org/author/Waltz">Waltz</a> </span><em>Intelligent Systems</em><em>21(3):5--14</em><em>January-February2006. </em>Thu Jun 19 17:35:00 CEST 2008Intelligent SystemsJanuary-February35--14{AI}'s 10 to Watch212006AI, linguistics, cognition, coordination, Semantic the networks, algorithmic human logics, biology, multi-agent intelligence, programming, Web social description genetic algorithms, artificial null human-level The recipients of the IEEE Intelligent Systems 10 to Watch award--Eyal Amir, Regina Barzilay, Jennifer Golbeck, Tom Griffiths, Steve Gustafson, Carsten Lutz, Pragnesh Jay Modi, Marta Sabou, and Richard A. Watson--discuss their current research and their visions of AI for the future. This article is part of a special issue on the Future of AI.http://www.bibsonomy.org/bibtex/280094438674537218e5664397a516f52/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00(MDL) linear (DDBS), Directional minimum smoothing principle, symbolic polynomial, programming, description genetic length processing algorithms, derivative-based model, <span style="color:#555555;">Yun-Seog <a href="http://www.bibsonomy.org/author/Yeun">Yeun</a> and Won-Sun <a href="http://www.bibsonomy.org/author/Ruy">Ruy</a> and Young-Soon <a href="http://www.bibsonomy.org/author/Yang">Yang</a> and Nam-Joon <a href="http://www.bibsonomy.org/author/Kim">Kim</a> </span><em>IEEE Transactions on Evolutionary Computation</em><em>8(6):542--566</em><em>December2004. </em>Thu Jun 19 17:35:00 CEST 2008IEEE Transactions on Evolutionary ComputationDecember6542--566Implementing Linear Models in Genetic Programming82004(MDL) linear (DDBS), Directional minimum smoothing principle, symbolic polynomial, programming, description genetic length processing algorithms, derivative-based model, We deal with linear models of genetic programming (GP) for regression or approximation problems when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by a symbolic processing algorithm. The major advantage of a linear model in GP is that its parameters can be estimated by the ordinary least square (OLS) method and a good model can be selected by applying the modern minimum description length (MDL) principle, while the nonlinearity necessary to handle the given problem is effectively maintained by indirectly evolving and finding various forms of base functions. In addition to a standard linear model consisting of mathematical functions, one variant of a linear model, which can be built using low-order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. With small samples, GP frequently shows the abnormal behaviors such as extreme large peaks or odd-looking discontinuities at the points away from sample points. To overcome this problem, a directional derivative-based smoothing (DDBS) method, which is incorporated into the OLS method, is introduced together with the fitness function that is based on MDL, reflecting the effects of DDBS. Also, two illustrative examples and three engineering applications are presented.http://www.bibsonomy.org/bibtex/2019982e52422e47b3b08fa3a015fe2e3/brazovayeyebrazovayeye2008-06-19T17:35:00+02:00Bayesian networks. model Machine principle, neural learning, comparison, Evolving programming, Tree description genetic Minimum length algorithms, induction, <span style="color:#555555;">Byoung-Tak <a href="http://www.bibsonomy.org/author/Zhang">Zhang</a> and Heinz <a href="http://www.bibsonomy.org/author/M{\&#034;u}hlenbein">M&#252;hlenbein</a> </span><em>Evolutionary Computation</em><em>3(1):17--38</em>(<em>1995</em>)Thu Jun 19 17:35:00 CEST 2008Evolutionary Computation117--38Balancing Accuracy and Parsimony in Genetic Programming31995Bayesian networks. model Machine principle, neural learning, comparison, Evolving programming, Tree description genetic Minimum length algorithms, induction, Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks.