University of Groningen Generative AI Zant, Colin Martijn van der

University of Groningen
Generative AI
Zant, Colin Martijn van der
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to
cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date:
2010
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Zant, C. M. V. D. (2010). Generative AI: a neo-cybernetic analysis Groningen: s.n.
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the
author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately
and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the
number of authors shown on this cover page is limited to 10 maximum.
Download date: 12-07-2017
Bibliography
Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino,
M., and Yoshida, C. (2009). Cognitive developmental robotics: a survey. IEEE
Transactions on Autonomous Mental Development, 1(1):12–34. 2, 21
Asada, M., MacDorman, K. F., Ishiguro, H., and Kuniyoshi, Y. (2001.). Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robotics
and Autonomous System, Vol. 37:185–193. 2, 21
Balakirsky, S. (2006). Usarsim: Providing a framework for multi-robot performance
evaluation. In Proceedings of the Performance Metrics for Intelligent Systems Workshop (PerMIS’06), pages 98–102. 120
Balch, T. and Yanco, H. A. (2002). Ten years of the aaai mobile robot competition and
exhibition: looking back and to the future. AI Magazine, 23(1):13–22. 125, 126
Baltes, J. (2000). A benchmark suite for mobile robots. In Proceedings of IROS-2000.
124
Belle, V., Deselaers, T., and Schiffer, S. (2008). Randomized trees for real-time onestep face detection and recognition. In Proceedings of the 19th International Conference on Pattern Recognition (ICPR’08). IEEE Computer Society. 153
171
172
BIBLIOGRAPHY
Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford University Press,
Inc., New York, NY, USA. 99
Bradski, G. R. and Pisarevsky, V. (2000). Intel’s computer vision library: Applications
in calibration, stereo, segmentation, tracking, gesture, face and object recognition.
In CVPR, volume 2, pages 796–797. IEEE Computer Society. 120
Brooks, R. (1986). A robust layered control system for a mobile robot. IEEE Journal
of Robotics and Automation. xii
Bruyninckx, H. (2001). Open robot control software: the orocos project. In ICRA,
pages 2523–2528. IEEE. 120
Bulacu, M. and Schomaker, L. (2006). Combining multiple features for textindependent writer identification and verification. In Proc. of 10th International
Workshop on Frontiers in Handwriting Recognition (IWFHR 2006), pages 281–286,
La Baule, France. 36
Bulacu, M., van Koert, R., Schomaker, L., and van der Zant, T. (2007). Layout analysis
of handwritten historical documents for searching the archive of the cabinet of the
dutch queen. In Proc. of 9th Int. Conf. on Document Analysis and Recognition
(ICDAR 2007), Curitiba, Brazil. IEEE Computer Society. 82
Caillault, E. and Viard-Gaudin, C. (2006). Using segmentation constraints in an implicit scheme for on-line word recognition. In Tenth International Workshop on
Frontiers in Handwriting Recognition, pages 607–612, La Baule, France. 77, 81
Calisi, D., Iocchi, L., and Nardi, D. (2008). A unified benchmark framework for autonomous Mobile robots and Vehicles Motion Algorithms (MoVeMA benchmarks).
In RSS Workshop on Experimental Methodology and Benchmarking in Robotics Research. 124
Changeux, J., Courrege, P., and Danchin, A. (1973). A theory of epigenesis of neural networks by selective stabilization of synapses. In Proceedings of the National
Acadamy of Sciences USA, volume 70, pages 2974–2978. 19
Changeux, J. and Danchin, A. (1976). Selective stabilization of developing synapses
as a mechanism for the specification of neural networks. Nature, 264:705–712. 19
BIBLIOGRAPHY
173
Clark, A. (2003). Natural-Born Cyborgs: Minds, Technologies, and the Future of
Human Intelligence. Oxford University Press. xiv
Correa, M., Ruiz-del-Solar, J., and Bernuy, F. (2008). Face recognition for humanrobot interaction applications: A comparative study. In Proceedings of the International RoboCup Symposium 2008 (CD-ROM Proceedings). 153
Dasarathy, B., editor (1990). Nearest neighbor patter classification techniques. IEEE.
22
De Jong, K. A. (2002). Evolutionary Computation. The MIT Press. 6, 7, 11, 21, 39
de Landa, M. (1991). War in the Age of Intelligent Machines. Zone Books, New York.
xiii, xiv, 2, 4, 10, 11, 27
De Landa, M. (2000). A Thousand Years of Nonlinear History. Zone Books. 12
Deacon, T. W. (1998). The Symbolic Species: The Co-Evolution of Language and the
Brain. W. W. Norton & Company. 17
del Pobil, A. (2006). Why do We Need Benchmarks in Robotics Research? In Proc. of
the Workshop on Benchmarks in Robotics Research, IEEE/RSJ International Conference on Intelligent Robots and Systems. 124
Deleuze, G. and Guattari, F. (1976). Rhizome: Introduction. Les Editions de Minuit,
Paris. 56
Deleuze, G., Guattari, F., and Massumi, B. (1987). A Thousand Plateaus: Capitalism
and Schizophrenia. University of Minnesota Press. xiv, 2, 4, 65
Doostdar, M., Schiffer, S., and Lakemeyer, G. (2008). Robust Speech Recognition
for Service Robotics Applications. In Proceedings of the International RoboCup
Symposium 2008, LNCS. Springer. 153
Drury, J. L., Yanco, H. A., and Scholtz, J. (2005). Using competitions to study humanrobot interaction in urban search and rescue. interactions, 12(2):39–41. 127
Duda, R. and Hart, P. (1973). Pattern Classification and Scene Analysis. Wiley. 7
Elman, J. et al. (1996). Rethinking Innateness: A connectionist perspective on development. Bradford, MIT Press, Cambridge, Massachusetts. 16, 17
174
BIBLIOGRAPHY
Feil-Seifer, D., Skinner, K., and Mataric, M. J. (2007). Benchmarks for evaluating
socially assistive robotics. Interaction Studies, 8(3):423–439. 124
Fikes, R. and Nilsson, N. J. (1971). Strips: A new approach to the application of
theorem proving to problem solving. Artif. Intell., 2(3/4):189–208. 6
Fogel, D. (1994). An introduction to simulated evolutionary optimization. IEEE Trans.
on Neural Networks: Special Issue on Evolutionary Computation, 5:3–14. 7, 21
Fontana, G., Matteucci, M., and Sorrenti, D. G. (2008). The RAWSEEDS proposal for
representation-independent benchmarking of SLAM. In RSS Workshop on Experimental Methodology and Benchmarking in Robotics Research. 124
Francke, H., Ruiz-del-Solar, J., and Verschae, R. (2007). Real-time hand gesture detection and recognition using boosted classifiers and active learning. In Advances in
Image and Video Technology, Second Pacific Rim Symposium (PSIVT 2007), LNCS
4872, pages 533–547. Springer. 153
Gabor, D. (1946). Theory of communication. Journal IEE, 93:429–459. 95, 97
Gerkey, B. P., Vaughan, R. T., and Howard, A. (2003). The player/stage project: Tools
for multi-robot and distributed sensor systems. In In Proceedings of the 11th International Conference on Advanced Robotics, pages 317–323. 120
Gibson, J. J. (1977). Perceiving, Acting, and Knowing: Toward an Ecological Psychology, chapter The Theory of Affordances, pages 67–82. Hillsdale, NJ: Lawrence
Erlbaum. xii, 2
Goodale, M. and Milner, A. (1992). Seperate visual pathways for perception and action. Trends in Neuroscience, 15:20–25. 93
Grey, W. (1950). An imitation of life. Scientific American, pages 42–45. xii
Harvey, I. (2000). Robotics: Philosophy of mind using a screwdriver. In Evolutionary
Robotics: From Intelligent Robots to Artificial Life, Vol. III, pages 207–230. AAI
Books. 5
Heisele, B. et al (2001). Categorization by learning and combining object parts. In
Neural information processing systems (NIPS), pages 1239–1245. 94
BIBLIOGRAPHY
175
Hendriks-Jansen, H. (1996). Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought. MIT Press, Cambridge, MA,
USA. xiv
Holland, J. (1975). Adaptation in Natural and Artificial Systems. The University of
Michigan Press, Ann Arbor. 6
Howard, A. and Roy, N. (2003). The robotics data set repository (radish). 124
Hubel, D. and Wiesel, T. (1962). Receptive fields, binocular interaction, and functional
architecture of the cat’s visual cortex. Journal of Psychology, 160:106–154. 94, 97
Joachims, T. (2005). A support vector method for multivariate performance measures.
In Proceedings of the International Conference on Machine Learning (ICML). 82
Joachims, T. (2006). Training linear svms in linear time. In Proceedings of the ACM
Conference on Knowledge Discovery and Data Mining (KDD). 82
Johnston, J. (2008). The Allure of Machinic Life: Cybernetics, Artificial Life, and the
New AI (Bradford Books). The MIT Press. xiii
Jones, J. and Palmer, L. (1987). An evaluation of the two dimensional gabor filter
model of simple receptive fields in cat striate cortex. Journal of Neurophysiology,
58:1233–1258. 95
Kahn, P. H., Ishiguro, H., Friedman, B., Kanda, T., Freier, N. G., Severson, R. L., and
Miller, J. (2007). What is a human? toward psychological benchmarks in the field
of humanrobot interaction. Interaction Studies, 8(3):363390. 124
Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E., and Matsubara, H. (1997).
RoboCup: A Challenge Problem for AI. AI Magazine, 18(1):73–85. 5, 125
Kitano, H. and Tadokoro, S. (2001). RoboCup Rescue: A Grand Challenge for Multiagent and Intelligent Systems. AI Magazine, 22(1):39–52. 5, 127
Knox, W. B., Lee, J., and Stone, P. (2008). Domestic interaction on a segway base.
In Proceedings of the International RoboCup Symposium 2008 (CD-ROM Proceedings). 153
176
BIBLIOGRAPHY
Koza, J. R. (1989). Hierarchical genetic algorithms operating on populations of computer programs. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence IJCAI-89, Vol. 1:768–774. 6, 12, 21
Koza, J. R. (1992). Genetic Programming. MIT Press. 6
Kugler, N., Kelso, J., and Turvey, M. (1980). On the concept of coordinative structures
as dissipative structures: I. theoretical lines of convergence. Tutorials in Motor
Behavior, pages 1–49. 2
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University Of Chicago
Press, 1rd edition. xiii, 3, 77
Kurzweil, R. (1999). Age of Spiritual Machines: When Computers Exceed Human
Intelligence. Penguin USA, New York, NY, USA. 4
Lambert Schomaker, Marius Bulacu, K. F. (2004). Automatic writer identification using fragmented connected-component contours. Proc. of 9th International Workshop
on Frontiers in Handwriting Recognition (IWFHR 2004), IEEE Computer Society.,
pages 185–190. 59
Lauer, F., Suen, C. Y., and Bloch, G. (2007). A trainable feature extractor for handwritten digit recognition. Pattern Recogn., 40(6):1816–1824. 78
Lavrenko, V., R. T. and R., M. (2004). Holistic word recognition for handwritten
historical documents. In Proc. of the Int. Workshop on Document Image Analysis for
Libraries (DIAL), pages 278–287. 77, 80
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning
applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324. 59,
78
Liwicki, M. and Bunke, H. (2006). Hmm-based on-line recognition of handwritten
whiteboard notes. In Tenth International Workshop on Frontiers in Handwriting
Recognition, pages 595–599, La Baule, France. 77, 78, 81
Loncomilla, P. and Ruiz-del-Solar, J. (2007). Robust object recognition using wide
baseline matching for robocup applications. In RoboCup 2007: Robot Soccer World
Cup XI, LNAI 5001. Springer. 153
BIBLIOGRAPHY
177
Low, L. K. and Cheng, H.-J. (2006). Axon pruning: an essential step underlying the
developmental plasticity of neuronal connections. Phil Trans R Soc B, 361:1531–
1544. 16, 17
Meeden, L., Schultz, A. C., Balch, T. R., Bhargava, R., Haigh, K. Z., Bohlen, M.,
Stein, C., and Miller, D. P. (2000). The aaai 1999 mobile robot competitions and
exhibitions. AI Magazine, 21(3):69–78. 127
Michod, R. (1989). Darwinian selection in the brain. Evolution, 43:694–696. 19
Mikolajczyk, K. and Schmid, C. (2003). A performance evaluation of local descriptors. Proceedings of the international conference on computer vision and pattern
recognition, 2:257–263. 94
Mohan, A., Papageorgiou, C., and Poggio, T. (2001). Example-based object detection
in images by components. IEEE Transactions on Pattern analysis and Machine
Intelligence, 23:349–361. 94
Montemerlo, M., Roy, N., and Thrun, S. (2003). Perspectives on standardization in
mobile robot programming: The carnegie mellon navigation (carmen) toolkit. In
In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS, pages
2436–2441. 120
Munoz, N., Valencia, J., , and Londono, N. (2007). Evaluation of navigation of an
autonomous mobile robot. In Proc. of Performance Metrics for Intelligent Systems
Workshop (PerMIS), page 1521. 124
Pal, U., Roy, K., and Kimura, F. (2006). A lexicon driven method for unconstrained
bangla handwritten word recognition. In Tenth International Workshop on Frontiers
in Handwriting Recognition, pages 601–606, La Baule, France. 77, 81
Park, H.-S. and Lee, S.-W. (1995). Hidden markov mesh random field: theory and its
application to handwritten character recognition. In ICDAR ’95: Proceedings of the
Third International Conference on Document Analysis and Recognition (Volume 1),
page 409, Washington, DC, USA. IEEE Computer Society. 77, 80
Pfeifer, R. and Scheier, C. (2001). Understanding Intelligence. MIT Press, Cambridge,
MA, USA. 30
178
BIBLIOGRAPHY
Poggio, T. and Bizzi, E. (2004). Generalization in vision and motor control. Nature,
431:768–774. 99
Poggio, T. and Edelman, S. (1990). A network that learns to recognize threedimensional objects. Nature, 343:263–266. 83
Powell, M. (1987). Radial basis functions for multivariable interpolation: A review. In
Algorithms for Approximation, pages 143–167, Oxford. Clarendon Press. 95, 99
Prassler, Erwin and Hägele, Martin and Siegwart, Roland (2006). International Contest for Cleaning Robots: Fun Event or a First Step towards Benchmarking Service
Robots. 128
Prigogine, I. (1977). Self-organization in nonequilibrium systems : from dissipative
structures to order through fluctuations. John Wiley & Sons, New York, USA. 7, 27
Prigogine, I. (1981). From Being to Becoming: Time and Complexity in the Physical
Sciences. W H Freeman & Co (Sd). 22
Prigogine, I. (1984). Order out of chaos: Man’s new dialogue with nature. Bantam
Books. xiv, 4, 7, 8, 9, 10, 27, 116
Prigogine, I. (2003). Is Future Given? World Scientific Publishing Company. 7
Rabiner, L. and Juang, B. (1986). An introduction to hidden markov models. ASSP
Magazine, IEEE, 3(1):4–16. 88
Rath, T., Manmatha, R., and Lavrenko, V. (2004). A search engine for historical
manuscript images. In Proc. of the ACM SIGIR 2004 Conf., pages 369–376. 80
Riesenuber, M. and T., P. (1999). Hierarchical models of object recognition in cortex.
Nature Neuroscience, 2(11):1019–1025. 94
Ruiz-del-Solar, J. (2007). Personal robots as ubiquitous-multimedial-mobile web interfaces. In Proc. of 5th Latin American Web Congress (LA-WEB), pages 120–127.
152
Sabanovic, S., Michalowski, M., and Simmons, R. (2006). Robots in the wild: observing human-robot social interaction outside the lab. In 9th IEEE International
Workshop on Advanced Motion Control, pages 596–601. 124
BIBLIOGRAPHY
179
Sarkar, P. and Nagy, G. (2005). Style consistent classification of isogenous patterns.
IEEE Trans. Pattern Anal. Mach. Intell., 27(1):88–98. 85
Savage, J., Ayala, F., Cuellar, S., and Weitzenfeld, A. (2008). The use of scripts based
on conceptual dependency primitives for the operation of service mobile robots. In
Proceedings of the International RoboCup Symposium 2008 (CD-ROM Proceedings). 152
Schiffer, S., Ferrein, A., and Lakemeyer, G. (2006). Football is coming Home. In Proc.
of International Symposium on Practical Cognitive Agents and Robots (PCAR’06).
University of Western Australia Press. 153
Schomaker, L. (2004). Anticipation in cybernetic systems: A case against mindless
anti-representationalism. Proc. of IEEE Systems, Man & Cybernetics (SMC’04),
pages 2037–2045. 2
Schomaker, L. (2007a). Handwriting recognition using an image correlator. Proceedings of the ICDAR 2007. 63, 68
Schomaker, L. (2007b). Retrieval of handwritten lines in historical documents. In
Proceedings of the International Conference on Document Analysis and Recognition
(ICDAR). 82, 85
Schomaker, L. (2008a). Word mining in a sparsely-labeled handwritten collection. In
Proceedings of the conference on Recognition and Retrieval XV, IS&T/SPIE International Symposium on Electronic Imaging. 80
Schomaker, L. (2008b). Word mining in a sparsely-labeled handwritten collection.
In Proceedings of the International Conference on Document Recognition and Retrieval XV (DRR). 82, 86, 88
Schomaker, L. and Bulacu, M. (2004). Automatic writer identification using
connected-component contours and edge-based features of uppercase western script.
IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 26(6):787–798.
36
Schomaker, L., de Leau, E., and Vuurpijl, L. (1999). Using pen-based outlines for
object-based annotation and image-based queries. In VISUAL ’99: Proceedings of
the Third International Conference on Visual Information and Information Systems,
pages 585–592, London, UK. Springer-Verlag. 77
180
BIBLIOGRAPHY
Schomaker, L., Franke, K., and Bulacu, M. (2007). Using codebooks of fragmented
connected-component contours in forensic and historic writer identification. Pattern
Recogn. Lett., 28(6):719–727. 80
Serre, T. et al. (2005). A theory of object recognition: Computations and circuits in
the feedforward path of the visual stream in primate visual cortex. AI Memo 2005036/CBCL Memo 259, Massachusetts Institute of Technology, Cambridge. 84
Serre, T. et al. (2007). Robust object recognition with cortex-like mechanisms. IEEE
Transactions on Pattern Analysis and Machine Intelligence, 29(3):411–426. 7, 83,
84, 94, 95, 102
Simon, H. A. (1969). The Sciences of the Artificial. MIT Press, Cambridge, Massachusetts, first edition. xii
Sims, K. (1991). Artificial evolution for computer graphics. In SIGGRAPH ’91: Proceedings of the 18th annual conference on Computer graphics and interactive techniques, pages 319–328, New York, NY, USA. ACM Press. 37, 40
Spivey, M. J. (2004). The Continuity of Mind. Oxford University Press, Oxford, UK. 5
Steinfeld, A., Fong, T., Kaber, D. B., Lewis, M., Scholtz, J., Schultz, A. C., and
Goodrich, M. A. (2006). Common metrics for human-robot interaction. In Proc.
of HRI, pages 33–40. 127
Sutton, R. and Barto, A. (1998). Reinforcement Learning: an Introduction. MIT Press.
3, 7
T. Serre, L. Wolf and T. Poggio (2005). Object recognition with features inspired by
visual cortex. In Proceedings of Computer Vision and Pattern Recognition (CVPR),
San Diego, USA. 83
Takagi, H. (2001.a). Interactive evolutionary computation: Fusion of the capabilities
of ec optimization and human evaluation. Proceedings of the IEEE, Vol. 89:1275–
1296. 37
Takagi, H. (2001b). Interactive evolutionary computation: Fusion of the capabilities of
ec optimization and human evaluation. Proceedings of the IEEE, 89(9):1275–1296.
39
BIBLIOGRAPHY
181
Ullman, S., Vidal-Naquet, M., and Sali, E. (2002). Visual features of intermediate
complexity and their use in classification. Nature Neuroscience, 5(7):682–687. 94
Ungerleider, L. and Mishkin, M. (1982). The analysis of visual behavior, chapter Two
Cortical Visual Systems. Cambridge, MA: MIT press. 93, 94
Vailaya, A., Figueiredo, M., Jain, A., and Zhang, H. (2001). Image classification for
content-based indexing. IEEE Trans. on image processing, 10(1):117–130. 76
van der Zant, T. (2008). Large scale parallel document image processing. In Proceedings of the International Conference on Document Recognition and Retrieval XV
(DRR). 57, 82, 86
van der Zant, T. and Wisspeintner, T. (2005). RoboCup X: A Proposal for a New
League Where RoboCup Goes Real World. In Bredenfeld, A., Jacoff, A., Noda, I.,
and Takahashi, Y., editors, RoboCup, volume 4020 of Lecture Notes in Computer
Science, pages 166–172. Springer. 117, 122, 129
van der Zant, T. and Wisspeintner, T. (2007).
Robotic Soccer, chapter
RoboCup@Home: Creating and Benchmarking Tomorrows Service Robot Applications, pages 521–528. I-Tech Education and Publishing. 5, 117, 122, 129
Vapnik, V. (1998). Statistical Learning Theory. Wiley. 7
Varela, F. J., Thompson, E. T., and Rosch, E. (1992). The Embodied Mind: Cognitive
Science and Human Experience. The MIT Press. xiv, 4
Weber, M., Welling, W., and Perona, P. (2000). Unsupervised learning if models of
recognition. In European conference of Computer vision, pages 1001–1108. Massachussetts Institute of Technology. 94
Wisspeintner, T. and Novak, W. (2007). VolksBot - A Construction Kit for Multipurpose Robot Prototyping. 120
Xiu, P. and Baird, H. S. (2008). Whole-book recognition using mutual-entropy-driven
model adaptation. In Document Recognition and Retrieval XV. Edited by Yanikoglu,
Berrin A.; Berkner, Kathrin. Proceedings of the SPIE, Volume 6815, pp. 681506681506-10 (2008)., volume 6815 of Presented at the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference. 85
182
BIBLIOGRAPHY
Yanco, H. A., Drury, J. L., and Scholtz, J. (2004). Beyond Usability Evaluation: Analysis of Human-Robot Interaction at a Major Robotics Competition. Human-Computer
Interaction, 19:117–149. 127
Zelkowitz, M., Basili, V., Asgari, S., Hochstein, L., Hollingsworth, J., and Nakamura, T. (2005). Measuring productivity on high performance computers. In METRICS ’05: Proceedings of the 11th IEEE International Software Metrics Symposium
(METRICS’05), page 6, Washington, DC, USA. IEEE Computer Society. 68