@inproceedings{VanDerStraeten&al2003c, title = {Detecting inconsistencies between {U}{M}{L} models using description logic}, author = {Ragnhild {Van Der Straeten} and Jocelyn Simmonds and Tom Mens}, booktitle = {Proc. Int'l Workshop on Description Logics (DL)}, month = {September}, year = 2003, timestamp = {2008.05.15}, owner = {pdeleenh}, modified = {2007-09-25 21:37:24 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2d6ac2b3805bdf23d9bfc3a06f14202ca/pdeleenh}, keywords = {logics, description UML} } @inproceedings{VanDerStraeten&al2003b, title = {Using Description Logics to Maintain Consistency Between {U}{M}{L} Models}, author = {Ragnhild {Van Der Straeten} and Tom Mens and Jocelyn Simmonds and Viviane Jonckers}, booktitle = {{U}{M}{L} 2003 - The Unified Modeling Language}, editor = {Perdita Stevens and Jon Whittle and Grady Booch}, pages = {326--340}, volume = 2863, year = 2003, timestamp = {2008.05.15}, issn = {0302-9743}, isbn = {3-540-20243-9}, owner = {pdeleenh}, biburl = {http://www.bibsonomy.org/bibtex/24d92ab476b77a1c57f04ffbdf5e1e5cd/pdeleenh}, keywords = {UML, description consistency maintenance, logics} } @inproceedings{Vanderstraeten&al2004UML, title = {Supporting Model Refactorings through Behaviour Inheritance Consistencies}, author = {Ragnhild {Van Der Straeten} and Viviane Jonckers and Tom Mens}, booktitle = {{U}{M}{L} 2004 - The Unified Modeling Language}, editor = {Ana Moreira Thomas Baar}, month = {October}, pages = {305--319}, volume = 3273, year = 2004, timestamp = {2008.05.15}, issn = {0302-9743}, owner = {pdeleenh}, biburl = {http://www.bibsonomy.org/bibtex/2efaba6a27bf95c445d2d8092137fc844/pdeleenh}, keywords = {logics, UML, software refactoring, description inconsistency consistency maintenance, model-driven development, management} } @inproceedings{VanDerStraeten2002, title = {Using Description Logic in Object-Oriented Software Development}, author = {Ragnhild {Van Der Straeten}}, booktitle = {Proc. Int'l Workshop on Description Logics (DL)}, year = 2002, timestamp = {2008.05.15}, owner = {pdeleenh}, modified = {2007-09-25 21:37:19 +0200}, biburl = {http://www.bibsonomy.org/bibtex/238be4a512c473457554506ed3b0f65e8/pdeleenh}, keywords = {logics, description object-oriented, UML} } @article{Medvidovic&Taylor2000, title = {A classification and comparison framework for software architecture description languages}, author = {N. Medvidovic and R. N. Taylor}, number = 1, pages = {70--93}, publisher = {IEEE Press}, volume = 26, year = 2000, timestamp = {2008.05.15}, issn = {0098-5589}, owner = {pdeleenh}, biburl = {http://www.bibsonomy.org/bibtex/2cefba077713d56fab63f091cc3f80691/pdeleenh}, keywords = {software architecture, description language architecture} } @article{Medvidovic&Taylor1997, title = {A framework for classifying and comparing architecture description languages}, author = {N. Medvidovic and R. N. Taylor}, editor = {Mehdi Jazayeri and Helmut Schauer}, journal = {SIGSOFT Software Engineering Notes. ESEC/FSE'97}, month = {November}, number = 6, pages = {60--76}, volume = 22, year = 1997, timestamp = {2008.05.15}, owner = {pdeleenh}, biburl = {http://www.bibsonomy.org/bibtex/2e94eff9d38826d0ca6737422b69dfc0f/pdeleenh}, keywords = {software architecture, description language architecture} } @inproceedings{Cali&al2001, title = {Reasoning on {U}{M}{L} Class Diagrams in Description Logics}, author = {Andrea {Cal\'i} and Diego Calvanese and Giuseppe {De Giacomo} and Maurizio Lenzerini}, booktitle = {Proc. IJCAR Workshop on Precise Modelling and Deduction for Object-oriented Software Development (PMD)}, year = 2001, timestamp = {2008.05.15}, owner = {pdeleenh}, modified = {2007-10-10 10:14:45 +0200}, biburl = {http://www.bibsonomy.org/bibtex/23cc8affd3aa735ebee6a31746d62c356/pdeleenh}, keywords = {UML, description logics} } @book{Baader&al2003, title = {The Description Logic Handbook: Theory, Implementation and Applications}, author = {F. Baader and D. McGuinness and D. Nardi and P.F. Patel-Schneider}, publisher = {Cambridge University Press}, year = 2003, timestamp = {2008.05.15}, owner = {pdeleenh}, modified = {2007-09-26 10:31:27 +0200}, biburl = {http://www.bibsonomy.org/bibtex/2915934abb92646a0b520e5d464f0083f/pdeleenh}, keywords = {description logics} } @inproceedings{popp:1998:smc, title = {Automated hardware design using genetic programming, {VHDL}, and {FPGAs}}, address = {San Diego, CA USA}, author = {Robert L. Popp and David J. Montana and Richard R. Gassner and Gordon Vidaver and Suraj Iyer}, booktitle = {IEEE International Conference on Systems, Man, and Cybernetics}, month = {11-14 October}, pages = {2184--2189}, publisher = {IEEE}, volume = 3, year = 1998, notes = {Inspec Accession Number: 6189463}, size = {5 pages}, abstract = {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 date}, biburl = {http://www.bibsonomy.org/bibtex/268879af3375eb231c3fa888955a82307/brazovayeye}, keywords = {evolution, 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} } @article{Zhang:1998:eisnt, title = {Evolutionary Induction of Sparse Neural Trees}, author = {Byoung-Tak Zhang and Peter Ohm and Heinz M{\"u}hlenbein}, journal = {Evolutionary Computation}, number = 2, pages = {213--236}, volume = 5, year = 1997, url = {http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1997.5.2.213}, doi = {doi:10.1162/evco.1997.5.2.213}, size = {31 pages}, abstract = {This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest.}, biburl = {http://www.bibsonomy.org/bibtex/20d10fbb72a3cfacf8b038ab78c4be2f3/brazovayeye}, keywords = {series higher-order principle, neural networks, algorithm prediction, time tree programming, program description genetic breeder Minimum length representation, algorithms, induction,} } @incollection{zhang:1996:aigp2, title = {Adaptive Fitness Functions for Dynamic Growing/Pruning of Program Trees}, address = {Cambridge, MA, USA}, author = {Byoung-Tak Zhang and Heinz M{\"u}hlenbein}, booktitle = {Advances in Genetic Programming 2}, chapter = 12, editor = {Peter J. Angeline and K. E. {Kinnear, Jr.}}, pages = {241--256}, publisher = {MIT Press}, year = 1996, isbn = {0-262-01158-1}, biburl = {http://www.bibsonomy.org/bibtex/2e41de0bff863b81995945e4a61652ccc/brazovayeye}, keywords = {functions adaptive fitness minimum trees, neural (MDL), programming, Occam's description genetic Razor, length algorithms,} } @article{Zhang-Muehlenbein-95-ECJ, title = {Balancing Accuracy and Parsimony in Genetic Programming}, author = {Byoung-Tak Zhang and Heinz M{\"u}hlenbein}, journal = {Evolutionary Computation}, number = 1, pages = {17--38}, volume = 3, year = 1995, url = {http://www.ais.fraunhofer.de/~muehlen/publications/gmd_as_ga-94_09.ps}, doi = {doi:10.1162/evco.1995.3.1.17}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2019982e52422e47b3b08fa3a015fe2e3/brazovayeye}, keywords = {Bayesian networks. model Machine principle, neural learning, comparison, Evolving programming, Tree description genetic Minimum length algorithms, induction,} } @article{yeun_2004_tec, title = {Implementing Linear Models in Genetic Programming}, author = {Yun-Seog Yeun and Won-Sun Ruy and Young-Soon Yang and Nam-Joon Kim}, journal = {IEEE Transactions on Evolutionary Computation}, month = {December}, number = 6, pages = {542--566}, volume = 8, year = 2004, url = {http://members.kr.inter.net/yyshuj/paper/pre-lm-gp.pdf}, doi = {doi:10.1109/TEVC.2004.836818}, size = {25 pages}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/280094438674537218e5664397a516f52/brazovayeye}, keywords = {(MDL) linear (DDBS), Directional minimum smoothing principle, symbolic polynomial, programming, description genetic length processing algorithms, derivative-based model,} } @article{Waltz:2006:IS, title = {{AI}'s 10 to Watch}, author = {David L. Waltz}, journal = {Intelligent Systems}, month = {January-February}, number = 3, pages = {5--14}, volume = 21, year = 2006, issn = {1541-1672}, doi = {10.1109/MIS.2006.40}, size = {10 pages}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2f78d96a0fffbae7a483f2d51e766dac4/brazovayeye}, keywords = {AI, linguistics, cognition, coordination, Semantic the networks, algorithmic human logics, biology, multi-agent intelligence, programming, Web social description genetic algorithms, artificial null human-level} } @article{bb38973, title = {Object Detection via Feature Synthesis Using {MDL}-Based Genetic Programming}, author = {Yingqiang Lin and Bir Bhanu}, journal = {IEEE Transactions on Systems, Man and Cybernetics, Part B}, month = {June}, number = 3, pages = {538--547}, volume = 35, year = 2005, url = {http://ieeexplore.ieee.org/iel5/3477/30862/01430837.pdf}, issn = {1083-4419}, bibsource = {http://iris.usc.edu/Vision-Notes/bibliography/pattern650.html#TT36418}, doi = {doi:10.1109/TSMCB.2005.846656}, size = {10 pages}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2ec795b9d7db4606fcafa1c6aa47bc3a6/brazovayeye}, keywords = {minimum (SAR) synthetic image radar aperture learning, Feature (MDL), programming, image, description genetic length operator, feature algorithms, primitive} } @article{Chen:2000:AOR, title = {Simulating economic transition processes by genetic programming}, author = {Shu-Heng Chen and Chia-Hsuan Yeh}, journal = {Annals of Operations Research}, month = {December}, number = {1-4}, pages = {265--286}, volume = 97, year = 2000, issn = {0254-5330}, doi = {doi:10.1023/A:1018972006990}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/243621b2fa33526227432fa7f8744e16b/brazovayeye}, keywords = {selling minimum principle, short programming, bounded rationality, description genetic length Kolmogorov complexity, algorithms,} } @article{chen:1996:caemh, title = {Toward a Computable Approach to the Efficient Market Hypothesis: An Application of Genetic Programming}, author = {Shu-Heng Chen and Chia-Hsuan Yeh}, journal = {Journal of Economic Dynamics and Control}, month = {1 June}, number = 6, pages = {1043--1063}, volume = 21, year = 1997, url = {http://www.sciencedirect.com/science/article/B6V85-3SWYBJD-P/2/d1bb80ffce780c45697f44001e20f672}, doi = {doi:10.1016/S0165-1889(97)82991-0}, abstract = {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 search. 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.}, biburl = {http://www.bibsonomy.org/bibtex/2d952894c34b9443d5dc27a6d0bc0a380/brazovayeye}, keywords = {Evolutionary percentage principle, absolute market programming, description genetic Minimum error, length Efficient algorithms, hypothesis Mean computation,} } @inproceedings{kourtesis2008combining, title = {Combining SAWSDL, OWL-DL and UDDI for Semantically Enhanced Web Service Discovery}, address = {Berlin, Heidelberg}, author = {Dimitrios Kourtesis and Iraklis Paraskakis}, booktitle = {Proceedings of the 5th European Semantic Web Conference}, editor = {Manfred Hauswirth and Manolis Koubarakis and Sean Bechhofer}, month = {June}, publisher = {Springer Verlag}, series = {LNCS}, year = 2008, url = {http://data.semanticweb.org/conference/eswc/2008/papers/375}, abstract = {UDDI registries are included as a standard offering within the product suite of any major SOA vendor, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes used for retrieving services, prevent UDDI registries from supporting fully automated and thus truly effective service discovery. This paper presents the implementation of a semantically-enhanced registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome the aforementioned limitations. The proposed solution combines the use of SAWSDL for creating semantically annotated descriptions of service interfaces and the use of OWL-DL for modelling service capabilities and for performing matchmaking via DL reasoning.}, biburl = {http://www.bibsonomy.org/bibtex/26b1ee8e572600c6d2efc47c9f58817ee/eswc2008}, keywords = {services wsdl sawsdl semantic web ontology integration description semantic-web-services-1 owl service universal annotations language discovery uddi} } @inproceedings{rosati2008finite, title = {Finite model reasoning in DL-Lite}, address = {Berlin, Heidelberg}, author = {Riccardo Rosati}, booktitle = {Proceedings of the 5th European Semantic Web Conference}, editor = {Manfred Hauswirth and Manolis Koubarakis and Sean Bechhofer}, month = {June}, publisher = {Springer Verlag}, series = {LNCS}, year = 2008, url = {http://data.semanticweb.org/conference/eswc/2008/papers/291}, abstract = {The semantics of OWL-DL and its subclasses are based on the classical semantics of first-order logic, in which the interpretation domain may be an infinite set. This constitutes a serious expressive limitation for such ontology languages, since, in many real application scenarios for the Semantic Web, the domain of interest is actually finite, although the exact cardinality of the domain is unknown. Hence, in these cases the formal semantics of the OWL-DL ontology does not coincide with its intended semantics. In this paper we start filling this gap, by considering the subclasses of OWL-DL which correspond to the logics of the DL-Lite family, and studying reasoning over finite models in such logics. In particular, we mainly consider two reasoning problems: deciding satisfiability of an ontology, and answering unions of conjunctive queries (UCQs) over an ontology. We first consider the description logic DL-Lite_R and show that, for the two above mentioned problems, finite model reasoning coincides with classical reasoning, i.e., reasoning over arbitrary, unrestricted models. Then, we analyze the description logics DL-Lite_F and DL_Lite_A. Differently from DL-Lite_R, in such logics finite model reasoning does not coincide with classical reasoning. To solve satisfiability and query answering over finite models in these logics, we define techniques which reduce polynomially both the above reasoning problems over finite models to the corresponding problem over arbitrary models. Thus, for all the DL-Lite languages considered, the good computational properties of satisfiability and query answering under the classical semantics also hold under the finite model semantics. Moreover, we have effectively and easily implemented the above techniques, extending the DL-Lite reasoner QuOnto with support for finite model reasoning.}, biburl = {http://www.bibsonomy.org/bibtex/2c637e37e6eb5932d4550f10f0e84c7a2/eswc2008}, keywords = {computational reasoning ontologies complexity description formal-languages-2 logics} }