@article{HamerichKoenigHennecke05ki,
	title = {{Sprachdialogsysteme im Kfz}},
	author = {Stefan W. Hamerich and Lars König and Marcus E. Hennecke},
	journal = {KI -- Künstliche Intelligenz},
	number = {3},
	pages = {29-34},
	volume = {2005},
	year = {2005},
	biburl = {http://www.bibsonomy.org/bibtex/267d17ecaa6651cada7da6fa05a482d9b/flint63},
	abstract = {Sprachdialogsysteme für Kfz gibt es bereits seit 1996 und sie
	sind gegenwärtig in vielen Mittel- und Oberklassenfahrzeugen
	zu finden. Die Bandbreite heute erhältlicher Systeme reicht dabei
	von sprachbedienbaren Freisprecheinrichtungen über Nachrüstgeräte
	bis hin zu ab Werk erhältlichen vollintegrierten Infotainmentsystemen.
	
	Dieser Artikel beschreibt die Besonderheiten von Sprachdialogsystemen
	im automobilen Umfeld. Im Hinblick auf die Dialogmodellierung wird
	der Stand der gegenwärtigen Technik aufgezeigt und es werden
	Perspektiven diskutiert.},
	timestamp = {2008.02.19}, issn = {0933-1875}, owner = {flint},
	keywords = {ai dialog language paper processing speech traffic v0805 }
}

@incollection{EngelnKoenigEtAl04Vp,
	title = {{SENECA: Evaluation einer Sprachbedienung im Kfz}},
	address = {Lengerich},
	author = {Arnd Engeln and Winfried König and Thomas Wittig},
	booktitle = {{Verkehrspsychologie: Mobilität -- Sicherheit -- Fahrerassistenz}},
	editor = {Bernhard Schlag},
	pages = {371-383},
	publisher = {Pabst},
	year = {2004},
	biburl = {http://www.bibsonomy.org/bibtex/2cda4e4572767e0a30648f161f4ca19a0/flint63},
	timestamp = {2008.01.20}, isbn = {3-89967-107-4}, owner = {flint},
	keywords = {assist cognitive interface paper science speech test traffic user v0805 }
}

@inproceedings{conf/or/Konig06,
	title = {Traffic Optimization Under Route Constraints with Lagrangian Relaxation and Cutting Plane Methods.},
	author = {Felix König},
	booktitle = {OR},
	crossref = {conf/or/2006},
	editor = {Karl-Heinz Waldmann and Ulrike M. Stocker},
	pages = {53-59},
	url = {http://dblp.uni-trier.de/db/conf/or/or2006.html#Konig06},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/26547f856bed420f1762174c45e52346b/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1007/978-3-540-69995-8_8}, isbn = {978-3-540-69994-1}, date = {2008-07-04},
	keywords = {dblp }
}

@inproceedings{conf/ipps/AhronovitzKS06,
	title = {A distributed method for dynamic resolution of BGP oscillations.},
	author = {Ehoud Ahronovitz and Jean-Claude König and Clement Saad},
	booktitle = {IPDPS},
	crossref = {conf/ipps/2006},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/ipps/ipdps2006.html#AhronovitzKS06},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/26030dda5be645a182f2aec1069cbc941/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/IPDPS.2006.1639340}, date = {2008-07-03},
	keywords = {dblp }
}

@book{wendt1995,
	title = {Tourenplanung durch Einsatz naturanaloger Verfahren : Integration von Genetischen Algorithmen und Simulated Annealing},
	address = {Dt. Univ.-Verl. [u.a.]},
	annote = {XXVI, 219 S},
	author = {Oliver Wendt and Wolfgang König},
	howpublished = {Wiesbaden},
	year = {1995},
	biburl = {http://www.bibsonomy.org/bibtex/254115c4d6219e34e9fda75123ec61316/apo},
	isbn = {3-8244-6181-1},
	keywords = {da2 tps }
}

@inproceedings{conf/his/RodriguesL06,
	title = {Genetic Programming with Incremental Learning for
                 Grammatical Inference},
	address = {Auckland, New Zealand},
	author = {Ernesto Rodrigues and Heitor Silverio Lopes},
	booktitle = {Sixth International Conference on Hybrid Intelligent
                 Systems and Fourth Conference on Neuro-Computing and
                 Evolving Intelligence (HIS-NCEI 2006)},
	editor = {Andreas Konig and Ajith Abraham and Qun Song},
	month = {13-15 December},
	pages = {47},
	publisher = {IEEE Computer Society},
	url = {http://doi.ieeecomputersociety.org/10.1109/HIS.2006.29},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2d0e7c438755ffcfde21ef6d3ba89586e/brazovayeye},
	abstract = {We present an evolutionary algorithm for the inference
                 of context-free grammars from positive and negative
                 examples. The algorithm is based on genetic programming
                 and uses a local optimisation operator that is capable
                 of improving the learning task. Ordinary genetic
                 operators are modified so as to bias the search. The
                 system was evaluated using Tomita's language examples
                 and results were compared with another similar
                 approach. Results show that the proposed approach is
                 promising and more robust than the other one.},
	bibsource = {DBLP,
                 http://dblp.uni-trier.de/db/conf/his/his2006.html#RodriguesL06}, bibdate = {2007-01-30}, isbn = {0-7695-2662-4}, doi = {doi:10.1109/HIS.2006.264930},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Konig:2007:cec,
	title = {Genetic Programming - {A} Tool for Flexible Rule
                 Extraction},
	address = {Singapore},
	author = {R. Konig and U. Johansson and L. Niklasson},
	booktitle = {2007 IEEE Congress on Evolutionary Computation},
	editor = {Dipti Srinivasan and Lipo Wang},
	month = {25-28 September},
	organization = {IEEE Computational Intelligence Society},
	pages = {1304--1310},
	publisher = {IEEE Press},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/270b1720366aa87cff98e74cd2c01ae30/brazovayeye},
	abstract = {Although data mining is performed to support decision
                 making, many of the most powerful techniques, like
                 neural networks and ensembles, produce opaque models.
                 This lack of interpretability is an obvious
                 disadvantage, since decision makers normally require
                 some sort of explanation before taking action. To
                 achieve comprehensibility, accuracy is often sacrificed
                 by the use of simpler, transparent models, such as
                 decision trees. Another alternative is rule extraction;
                 i.e. to transform the opaque model into a
                 comprehensible model, keeping acceptable accuracy. We
                 have previously suggested a rule extraction algorithm
                 named G-REX, which is based on genetic programming. One
                 key property of G-REX, due to the use of genetic
                 programming, is the possibility to use different
                 representation languages. In this study we apply G-REX
                 to estimation tasks. More specifically, three
                 representation languages are evaluated using eight
                 publicly available data sets. The quality of the
                 extracted rules is compared to two standard techniques
                 producing comprehensible models; multiple linear
                 regression and the decision tree algorithm C&RT.
                 The results show that G-REX outperforms the standard
                 techniques, but that the choice of representation
                 language is important.},
	file = {1989.pdf}, isbn = {1-4244-1340-0}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Johansson:2006:CEC,
	title = {Building Neural Network Ensembles using Genetic
                 Programming},
	address = {Vancouver},
	author = {Ulf Johansson and Tuve Lofstrom and Rikard Konig and Lars Niklasson},
	booktitle = {Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation},
	editor = {Gary G. Yen and Lipo Wang and Piero Bonissone and Simon M. Lucas},
	month = {6-21 July},
	pages = {2239--2244},
	publisher = {IEEE Press},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2020e09b8c49e392a0ad27f30c982dcc4/brazovayeye},
	abstract = {In this paper we present and evaluate a novel
                 algorithm for ensemble creation. The main idea of the
                 algorithm is to first independently train a fixed
                 number of neural networks (here ten) and then use
                 genetic programming to combine these networks into an
                 ensemble. The use of genetic programming makes it
                 possible to not only consider ensembles of different
                 sizes, but also to use ensembles as intermediate
                 building blocks. The final result is therefore more
                 correctly described as an ensemble of neural network
                 ensembles. The experiments show that the proposed
                 method, when evaluated on 22 publicly available data
                 sets, obtains very high accuracy, clearly outperforming
                 the other methods evaluated. In this study several
                 micro techniques are used, and we believe that they all
                 contribute to the increased performance. One such micro
                 technique, aimed at reducing overtraining, is the
                 training method, called tombola training, used during
                 genetic evolution. When using tombola training,
                 training data is regularly resampled into new parts,
                 called training groups. Each ensemble is then evaluated
                 on every training group and the actual fitness is
                 determined solely from the result on the hardest
                 part.},
	size = {6 pages}, isbn = {0-7803-9487-9}, notes = {WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D},
	keywords = {algorithms, genetic programming }
}

@inproceedings{Johansson:2006:ICAISC,
	title = {Genetically Evolved Trees Representing Ensembles},
	address = {Zakopane, Poland},
	author = {Ulf Johansson and Tuve Lofstrom and Rikard Konig and Lars Niklasson},
	booktitle = {Proceedings 8th International Conference on Artificial
                 Intelligence and Soft Computing {ICAISC}},
	editor = {Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A. Zadeh and Jacek Zurada},
	month = {June 25-29},
	pages = {613--622},
	publisher = {Springer-Verlag},
	series = {Lecture Notes on Artificial Intelligence (LNAI)},
	volume = {4029},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/263f5342017bfb7d888910f4d2f40b01b/brazovayeye},
	abstract = {We have recently proposed a novel algorithm for
                 ensemble creation called GEMS (Genetic Ensemble Member
                 Selection). GEMS first trains a fixed number of neural
                 networks (here twenty) and then uses genetic
                 programming to combine these networks into an ensemble.
                 The use of genetic programming makes it possible for
                 GEMS to not only consider ensembles of different sizes,
                 but also to use ensembles as intermediate building
                 blocks. In this paper, which is the first extensive
                 study of GEMS, the representation language is extended
                 to include tests partitioning the data, further
                 increasing flexibility. In addition, several micro
                 techniques are applied to reduce overfitting, which
                 appears to be the main problem for this powerful
                 algorithm. The experiments show that GEMS, when
                 evaluated on 15 publicly available data sets, obtains
                 very high accuracy, clearly outperforming both
                 straightforward ensemble designs and standard decision
                 tree algorithms.},
	size = {10 pages}, isbn = {3-540-35748-3}, doi = {doi:10.1007/11785231_64},
	keywords = {algorithms, genetic programming }
}

@article{journals/mscs/EhrigK06,
	title = {Deriving bisimulation congruences in the DPO approach to graph rewriting with borrowed contexts.},
	author = {Hartmut Ehrig and Barbara König},
	journal = {Mathematical Structures in Computer Science},
	number = {6},
	pages = {1133-1163},
	url = {http://dblp.uni-trier.de/db/journals/mscs/mscs16.html#EhrigK06},
	volume = {16},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/27b555b90718330f2dc502911cee37f8d/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1017/S096012950600569X}, date = {2008-06-19},
	keywords = {dblp }
}