Publications

For most recent publications and an overview please also consider consulting
our Google scholar profiles:

(publications below are in type, year, author order)

Journals

(please send a short request for papers that are not linked)

2022

Achimova, A., Scontras, G., Stegemann-Philipps, C., Lohmann, J., & Butz, M. V. (2021). Learning about others: Pragmatic social inference through ambiguity resolution. Cognition, 218, 104862. doi 10.1016/j.cognition.2021.104862

2021

Adam, M., Gumbsch, C., Butz, M. V., & Elsner, B. (2021). The impact of action effects on infants’ predictive gaze shifts for a non-human grasping action at 7-, 11-, and 18 months. Frontiers in Psychology, Cognition, 12, 695550. doi: 10.3389/fpsyg.2021.69555

Butz, M. V. (2021). Towards strong AI. Künstliche Intelligenz, 35, 91–101. doi: 10.1007/s13218-021-00705-x

Butz, M. V., Achimova, A., Bilkey, D., & Knott, A. (2021). Event‐Predictive Cognition: A Root for Conceptual Human Thought. Topics in Cognitive Science, 13, 10–24. doi: 10.1111/tops.12522

Gumbsch, C., Adam, M., Elsner, B., & Butz, M. V. (2021). Emergent goal-anticipatory gaze in infants via event-predictive learning and inference. Cognitive Science, 45 (e13016). doi: 10.1111/cogs.13016

Koryakin, D., Otte, S., & Butz, M.V. (2021). Inference of time-series components by means of online co-evolution, Genetic Programming and Evolvable Machines. doi 10.1007/s10710-021-09408-6

2020

Otte, S., Karlbauer, M., & Butz, M. V. (2020). Active Tuning. arXiv preprint arXiv:2010.03958

2019

Butz, M., Bilkey, D., Humaidan, D., Knott, A., & Otte, S. (2019). Learning, planning, and control in a monolithic neural event inference architecture. Neural Networks, 117, 135-144.

Gumbsch, C., Butz, M. V., & Martius, G. (2019). Autonomous identification and goal-directed invocation of event-predective behavioral primitives. IEEE Transactions on Cognitive and Developmental Systems. doi: 10.1109/TCDS.2019.2925890

Lohmann, J., Belardinelli, A., Butz, M. V. (2019). Hands ahead in mind and motion: Active inference in Peripersonal Hand Space. Vision 2019, 3(2), 15

Sering, K., Stehwien, N., Gao, Y., Butz, M. V., & Baayen, H. (2019). Resynthesizing the geco speech corpus with vocaltractlab.Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2019, 95-102

Hinterecker, T., Leroy. C., Kirschhock, M. E., Zhao, M., Butz, M. V., & Bülthoff, H. H. (2019). Spatial memory for vertical locations. Journal of Experimental Psychology: Learning, Memory, and Cognition 45 (7), 1205

Karlbauer, M., Otte, S., Lensch, H., Scholten, T., Wulfmeyer, V., & Butz, M. V. (2019). A distributed neural network architecture for robust non-linear spatio-temporal prediction. arXiv preprint arXiv:1912.11141

2018

Achimova, A., Deprez, V., Musolino, J. Structural asymmetry in question/ quantifier interactions. In Katalin E. Kiss & Tamás Zétényi (Eds.). Linguistic and cognitive aspects of quantification p. 13 – 30, Studies in Theoretical Psycholinguistics, 47, Springer, Cham.

Belardinelli, A., Lohmann, J., Farnè, A., & Butz, M. V. (2018). Mental space maps into the future. Cognition, 176, July 2018, 65-73.

Lohmann, J., Schroeder, P. A., Nuerk, H. C., Plewnia, C., & Butz, M. V. (2018). How Deep Is Your SNARC? Interactions Between Numerical Magnitude, Response Hands, and Reachability in Peripersonal Space. Frontiers in Psychology, 9(622). doi:10.3389/fpsyg.2018.00622

2017

Lohmann, J., & Butz, M. V. (2017). Lost in space: multisensory conflict yields adaptation in spatial representations across frames of reference. Cognitive Processing, 18, 211-228. doi:10.1007/s10339-017-0798-5

Lohmann, J., Rolke, B., & Butz, M. V. (2017). In Touch with Mental Rotation: Interactions between Mental and Tactile Rotations and Motor Responses. Experimental Brain Research. doi:10.1007/s00221-016-4861-8

Schrodt, F., Kneissler, J., Ehrenfeld, S., & Butz, M. V. (2017). Mario Becomes Cognitive. Topics in Cognitive Science, 9(2), 1–31. doi:10.1111/tops.12252

2016

Belardinelli, A., Barabas, M., Himmelbach, M., & Butz, M. V. (2016). Anticipatory eye fixations reveal tool knowledge for tool interaction. Experimental Brain Research, 234, 2415-2431. doi: 10.1007/s00221-016-4646-0

Belardinelli, A., Stepper, M. Y., & Butz, M. V. (2016): It's in the eyes: Planning precise manual actions before execution. Journal of Vision, 16. doi: 10.1167/16.1.18

Butz, M. V. (2016). Towards a unified sub-symbolic computational theory of cognition. Frontiers in Psychology 7. doi: 10.3389/fpsyg.2016.00925

Jung, E., Takahashi, K., Watanabe, K., de la Rosa, St., Butz, M. V., Bülthoff, H. H., & Meilinger, T. (2016). The influence of human body orientation on distance judgments. Frontiers in Psychology, 7. doi: 10.3389/fpsyg.2016.00217

Otte, S., Butz, M. V., Koryakin, D., Becker, F., Liwicki, M., & Zell, A. (2016). Optimizing recurrent reservoirs with neuro-evolution. Neurocomputing, 128-138. doi: 10.1016/j.neucom.2016.01.088

Schrodt, F., & Butz, M. V. (2016). Just imagine! Learning to emulate and infer actions with a stochastic generative architecture. Frontiers in Rob0tic and AI, 3. doi: 10.3389/frobt.2016.00005

2015

Belardinelli, A., & Butz, M. V. (2015). Anticipatory object interaction: Perceptual and motor aspects. Cognitive Processing, 16, Suppl 1, 14-15.

Belardinelli, A., & Butz, M. V. (2015). Action in the eye of the beholder: Goal-oriented gaze strategies. Cognitive Processing, 16, Suppl 1, 15-16.

Belardinelli, A., & Butz, M. V. (2015). Planning with the eyes: End state comfort effects in gaze behaviour. Cognitive Processing, 16, Suppl 1, 65.

Belardinelli, A., Herbort, O., & Butz, M. V. (2015). Goal-oriented gaze strategies afforded by object interaction. Vision Research, 106, 47-57. doi:10.1016/j.visres.2014.11.003

Herbort, O., & Butz, M. V. (2015). Planning grasps for object manipulation: integrating internal preferences and external constrains. Cognitive Processing, 16, Suppl 1, 249-253. doi: 10.1007/s10339-015-0703-z

Kneissler, J., Drugowitsch, J., Friston, K., & Butz, M. V. (2015). Simultaneous learning and filtering without delusions: A Bayes-optimal derivation of combining predictive inference and adaptive filtering. Frontiers in Computational Neuroscience. doi: 10.3389/fncom.2015.00047

Schrodt, F., Layher, G., Neumann, H., & Butz, M. V. (2015). Embodied learning of a generative neural model for biological motion perception and inference. Frontiers in Computational Neuroscience, 9. doi: 10.3389/fncom.2015.00079

Schroeder, P. A., Lohmann, J., Butz, M. V., & Plewnia, C. (2015). Behavioral bias for food reflected in hand movements: A preliminary study with healthy subjects. Cyberpsychology, Behavior, and Social Networking, 19, 120-126. doi: 10.1089/cyber.2015.0311

2014

Butz, M. V., Kutter, E., & Lorenz, C. (2014). Rubber hand illusion affects joint angle perception, PLoS ONE , 9. doi:10.1371/journal.pone.0092854

Herbort, O., Butz, M. V., & Kunde, W. (2014). The contribution of cognitive, kinematic, and dynamic factors to anticipatory grasp selection. Experimental Brain Research. 232, 1677-1688. doi:10.1007/s00221-014-3849-5

Kneissler, J., Stalph, P. O., Drugowitsch, J., & Butz, M. V. (2014). Filtering sensory information with XCSF: Improving learning robustness and robot arm control performance. Evolutionary Computation. doi: 100.1162/EVCO_a_00108

Layher, G., Schrodt, F., Butz, M. V., & Neumann, H. (2014). Adaptive learning in a compartmental model of visual cortex - how feedback enables stable category learning and refinement. Frontiers in Psychology, 5, 1287. doi: 10.3389/fpsyg.2014.01287

2013

Belardinelli, A.,Carbone, A., & Schneider, W. X. (2013). Classification of multiscale spatiotemporal energy features or video segmentation and dynamic objects prioritisation. Pattern Recognition Letters, 34, Issue 7, May 2013, 713-722. doi: 10.1016/j.patrec.2012.09.005

Butz, M. V. (2013). Separating goals from behavioral control: Implications from learning predictive modularizations. New Ideas in Psychology, 31, 302-312. doi: 10.1016/j.newideapsych.2013.04.001

Ehrenfeld, S., & Butz, M. V. (2013). The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion. Biological Cybernetics, 107, 61-82. doi: 10.1007/s00422-012-0526-2

Ehrenfeld, S., Herbort, O., & Butz, M. V. (2013). Modular neuron-based body estimation: Maintaining consistency over different limbs, modalities, and frames of reference. Frontiers in Computational Neuroscience, 7, 148. doi: 10.3389/fncom.2013.00148

Lohmann, J., Herbort, O., & Butz, M. V. (2013). Modeling the temporal dynamics of visual working memory. Cognitive Systems Research, 24, 80-86. doi: 10.1016/j.cogsys.2012.12.009

Sigaud, O., Butz, M. V., Pezzulo, G., & Herbort, O. (2013). The anticipatory construction of reality as a central concern for psychology and robotics. New Ideas in Pschology, 31, 217 - 220. doi: 10.1016/j.newideapsych.2012.12.004

2012

Butz, M. V., Belardinelli, A., & Ehrenfeld, S. (2012). Modeling body state-dependent multisensory integration. Cognitive Processing, 13(1), 113-116. doi: 10.1007/s10339-012-0471-y

Butz, M. V., Goldberg, D. E., Llorà, X., & Stalph, P. (2012). Resource management and scalability of the XCSF learning system. Theoretical Computer Science, 425, 126-141. doi: 10.1016/j.tcs.2010.07.007

Butz, M. V., & Sigaud, O. (2012). XCSF with local deletion: preventing detrimental forgetting. Evolutionary Intelligence, 5, 117-127. Berlin Heidelberg: Springer. doi:10.1007/s12065-012-0077-4

Endler, A., Rey, G. D., & Butz, M. V. (2012). Towards motivation-based adaptation of difficulty in e-learning programs. Australasian Journal of Educational Technology, 28, 1119-1135.

Herbort, O. (2012). Where to grasp a tool? Task-dependent adjustments of tool transformations by tool users. Journal of Psychology, 220(1), 37-43. doi: 10.1027/2151-2604/a000089

Herbort, O., & Butz, M. V. (2012). The continuous end-state comfort effect: Weighted integration of multiple biases. Psychological Research, 76, 345-363. doi: 10.1007/s00426-011-0334-7

Herbort, O., & Butz, M. V. (2012). Too good to be true? Ideomotor theory from a computational perspective. Frontiers in Psychology, 3, 494. doi: 10.3389/fpsyg.2012.00494

Herbort, O., Koning, A., van Uem, J., & Meulenbroek, R. (2012). The end-state comfort effect facilitates joint action. Acta Psychologica, 193(3), 404-416. doi:10.1016/j.actpsy.2012.01.001

Kirsch, W., Herbort, O., Butz, M. V., & Kunde, W. (2012). Influence of motor planning on distance perception within the peripersonal space. PLoS ONE 7(4): e34880, doi:10.1371/journal.pone 0034880 pdf

Koryakin, D., Lohmann, J., & Butz, M. V. (2012). Balanced echo state networks. Neural Networks, 36, 35-45. doi: 10.1016/j.neunet.2012.08.008

Stalph, P., & Butz, M. V. (2012). Learning local linear Jacobians for flexible and adaptive robot arm control. Genetic Programming and Evolvable Machines, 1-21. doi: 10.1007/s10710-011-9147-0

Stalph, P., Rubinsztajn, J., Sigaud, O., & Butz, M. V. (2012). Function approximation with LWPR and XCSF: A comparative study. Evolutionary Intelligence, 5, 103-116. doi: 10.1007/s12065-012-0082-7

2011

Butz, M. V., Linhardt, M. J., & Lönneker, T. D. (2011). Effective racing on partially observable tracks: Indirectly coupling anticipatory egocentric sensors with motor commands. IEEE Transactions on Computational Intelligence and AI in Games, 3, 31-42. doi:10.1109/TCIAIG.2010.2096426

Herbort, O., & Butz, M. V. (2011). Habitual and goal-directed factors in (everyday) object handling. Experimental Brain Research, 213, 371-382. doi: 10.1007/s00221-011-2787-8

Sugita, Y., Tani, J., & Butz, M. V. (2011). Simultaneously emerging Braitenberg codes and semantic compositionality. Adaptive Behavior, 19, 295-316. doi: 10.1177/1059712311416871

2010

Butz, M. V., Shirinov, E., & Reif, K. (2010). Self-organizing sensorimotor maps plus internal motivations yield animal-like behavior. Adaptive Behavior, 18, 315-337. doi: 10.1177/1059712310376842

Butz, M. V., Thomaschke, R., Linhardt, M. J., & Herbort, O. (2010). Remapping motion across modalities: Tactile rotations influence visual motion judgments. Experimental Brain Research, 207, 1-11. doi: 10.1007/s00221-010-2420-2

Herbort O., & Butz , M. V. (2010). Planning and control of hand orientation in grasping movements. Experimental Brain Research, 202, 867-878. doi: 10.1007/s00221-010-2191-9

Loiacono, D., Lanzi, P. L., Togelius, J., Onieva, E., Pelta, D. A., Butz, M. V., Lönneker, T. D., Cardamone, L., Perez, D., Sáez, Y., Preuss, M., & Quadflieg, J. (2010). The 2009 simulated car racing championship. IEEE Transactions on Computational Intelligence and AI in Games, 2, 131-147. doi: 10.1109/TCIAIG.2010.2050590

2009

Butz, M. V., & Lanzi, P. L. (2009). Sequential problems that test generalization in learning classifier systems. Evolutionary Intelligence, 2, 141-147. doi:10.1007/s12065-009-0019-y

Herbort, O., & Butz, M. V. (2009). Anticipatory planning of sequential hand and finger movements. Journal of Motor Behavior, 41, 561-569. doi: 10.3200/35-09-003-RA

2008

Butz, M. V. (2008). How and why the brain lays the foundations for a conscious self. Constructivist Foundations, 4, 1-42.

Butz, M. V. (2008). Intentions and mirror neurons: From the individual to overall social reality. Commentary. Constructivist Foundations, 3, 87-89.

Butz, M. V. (2008). Sensomotorische Raumrepräsentationen. Informatik-Spektrum, 31, 237-240. doi: 10.1007/s00287-008-0243-3

Butz, M. V., Lanzi, P. L., & Wilson, S. W. (2008). Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation, 12, 355-376. doi: 10.1109/TEVC.2007.903551

2007

Butz, M. V., Herbort, O., & Hoffmann, J. (2007). Exploiting redundancy for flexible behavior: unsupervised learning in a modular sensorimotor control architecture. Psychological Review, 114, 1015-1046. doi: 10.1037/0033-295X.114.4.1015

Butz, M. V., Goldberg, D. E., Lanzi, P. L., & Sastry, K. (2007). Problem solution sustenance in XCS: Markov chain analysis of niche support distributions and consequent computational complexity. Genetic Programming and Evolvable Machines, 8, 5-37. doi: 10.1007/s10710-006-9012-8

Hoffmann, J., Berner, M., Butz, M. V., Herbort, O., Kiesel, A., Kunde, W., & Lenhard, A. (2007). Explorations of anticipatory behavioral control (ABC): A report from the cognitive psychology unit of the University of Würzburg. Cognitive Processing, 8, 133-142. doi: 10.1007/s10339-007-0166-y

Hoffmann, J., Butz, M. V., Herbort, O., Kiesel, A., & Lenhard, A. (2007). Spekulationen zur Struktur ideo-motorischer Beziehungen. Zeitschrift für Sportpsychologie, 14, 95-104. doi: 10.1026/1612-5010.14.3.95

2006

Butz, M. V., Pelikan, M., Llorà, X., & Goldberg, D. E. (2006). Automated global structure extraction for effective local building block processing in XCS. Evolutionary Computation, 14, 345-380. doi: 10.1162/evco.2006.14.3.345

2005

Butz, M. V., Goldberg, D. E., & Lanzi, P. L. (2005). Gradient descent methods in learning classifier systems: Improving XCS performance in multistep problems. IEEE Transactions on Evolutionary Computation, 9, 452-473. doi: 10.1109/TEVC.2005.850265

Butz, M. V., Sastry, K., & Goldberg, D. E. (2005). Strong, stable, and reliable fitness pressure in XCS due to tournament selection. Genetic Programming and Evolvable Machines, 6, 53-77. doi: 10.1007/s10710-005-7619-9

2004

Butz, M. V. (2004). Anticipation for learning, cognition, and education. On the Horizon, 12, 111-116. doi: 10.1108/10748120410555359

Butz, M. V., Kovacs, T., Lanzi, P. L., & Wilson, S. W. (2004). Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation, 8, 28-46. doi: 10.1109/TEVC.2003.818194

2003

Butz, M. V., Goldberg, D. E., & Tharakunnel, K. (2003). Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation, 11, 239-277. doi: 101162/106365603322365298

2002

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2002). The anticipatory classifier system and genetic generalization. Natural Computing, 1, 427-467. doi: 10.1023/A:1021330114221

Butz, M. V., & Hoffmann, J. (2002). Anticipations control behavior: Animal behavior in an anticipatory learning classifier system. Adaptive Behavior, 10, 75-96. doi: 10.1177/1059712302010002001

Butz, M. V., & Wilson, S. W. (2002). An algorithmic description of XCS. Soft Computing, 6, 144-153. doi: 10.1007/s005000100111

Other Publications

(Conference Papers, Workshop Papers, Book Chapters & Preprints)

2021

Traub, M., Butz, M. V., Legenstein, R., & Otte, S. (2021). Dynamic Action Inference with Recurrent Spiking Neural Networks. International Conference on Artificial Neural Networks, 233-244

Ciurletti, M., Traub, M., Karlbauer, M., Butz, M. V., & Otte, S. (2021). Signal Denoising with Recurrent Spiking Neural Networks and Active Tuning. International Conference on Artificial Neural Networks, 220-232

Fabi, S., Otte, S., & Butz, M. V. (2021). Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge. International Conference on Artificial Neural Networks, 525-536

Sadeghi, M., Schrodt, F., Otte, S., & Butz, M. V. (2021). Binding and perspective taking as inference in a generative neural network model. International Conference on Artificial Neural Networks, 3-14

Karlbauer, M., Menge, T., Otte, S., Lensch, H., Scholten, T., Wulfmeyer, V., & Butz, M. V. (2021). Latent State Inference in a Spatiotemporal Generative Model. International Conference on Artificial Neural Networks, 384-395

Sadeghi, M., Schrodt, F., Otte, S., & Butz, M. V. (2021). Gestalt Perception of Biological Motion: A Generative Artificial Neural Network Model. IEEE International Conference on Development and Learning (ICDL), 1-7

Stegemann-Philipps, C., Butz, M. V. (2021). Learn It First: Grounding Language in Compositional Event-Predictive Encodings. IEEE International Conference on Development and Learning (ICDL), 1-6

Stegeman-Philipps, C., Butz, M. V., Winkler, S., & Achimova, A. (2021). Speakers Use More Informative Referring Expressions to Describe Surprising Events. Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43)

Gumbsch, C., Adam, M., Elsner, B., & Butz, M. V. (2021). I see where this is going: Modeling the development of infants' goal-predictive gaze. Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43)

Lohmann, J., Butz, M. V. (2021).Can Action Bias the Perception of Ambiguous Auditory Stimuli? Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43)

Weigert, P., Lohmann, J., & Butz, M. V. (2021). Modeling the Anticipatory Remapping of Spatial Body Representations: A Free Energy Approach. Proceedings of the Annual Meeting of the Cognitive Science Society 43 (43)

Fabi, S., Otte, S., & Butz, M. V. (2021). Compositionality as Learning Bias in Generative RNNs solves the Omniglot Challenge. Learning to Learn-Workshop at ICLR 2021

Praditia, T., Karlbauer, M., Otte, S., Oladyshkin, S, Butz, M. V., & W. Nowak (2021). Finite Volume Neural Network: Modeling Subsurface contaminant Transport. Workshop paper at ICLR 2021 SimDL Workshop

Butz, M. V. (2021). Towards strong AI. KI-Künstliche Intelligenz 35 (1), 91-101

2020

Lohmann, J., Butz, M. V. (2020). Hands in Thought and Motion. CogSci

Hobbhahn, M., Butz, M. V., Fabi, S., & Otte, S. (2020). Sequence Classification using Ensembles of Recurrent Generative Expert Modules. ESANN, 333-338

Sering, K., Schmidt-Barbo, P., Otte, S., Butz, M. V., & Baayen, H. (2020). Recurrent gradient-based motor inference for speech resynthesis with a vocal tract simulator. 12th International Seminar on Speech Production

Achimova, A., Eisemann, E. I., & Butz, M. V. (2020). Bayesian inference in dialogue. CogSci

Humaidan, D., Otte, S., Butz, M. V. (2020). Fostering event compression using gated surprise. International Conference on Artificial Neural Networks, 155-167

Traub, M., Butz, M. V., Baayen, R. H., & Otte, S. (2020). Learning Precise Spike Timings with Eligibility Traces. International Conference on Artificial Neural Networks, 659-669

Karlbauer, M., Otte, S., Lensch, H., Scholten, T., Wulfmeyer, V., & Butz, M. V. (2020). Inferring, predicting, and denoising causal wave dynamics. International Conference on Artificial Neural Networks, 566-577

2019

Gumbsch, C., Butz, M. V., & Martius, G. (2019). Autonomous identification and goal-directed invocation of event-predictive behavioral primitives. IEEE Transactions on Cognitive and Developmental Systems

Butz, M. V., Menge, T., Humaidan, D., & Otte, S. (2019). Inferring event-predictive goal-directed object manipulations in REPRISE. Artificial Neural Networks and Machine Learning - ICANN 2019, 639-653.

Lohmann, J. & Butz, M. V. (2019). Unflinching predictions: Anticipatory Crossmodal interactions are unaffected by the current hand posture. Proceedings of the 41st Annual Meeting of the Cognitive Science Society, 692-698.

Otte, S., Rubisch, P., & Butz, M. V. (2019). Gradient-based learning of compositional dynamics with modular RNNs. Artificial Neural Networks and Machine Learning - ICANN 2019, 484-496). (best paper award).

Otte, S., Stoll, J., & Butz, M. V. (2019). Incorporating adaptive RNN-based action inference and sensory perception. Artificial Neural Networks and Machine Learning - ICANN 2019, 543-555.

Scontras, G., Achimova, A., Stegemann, C., & Butz, M. V. (2019). On the purpose of ambiguous utterances. Proceedings of the 41st Annual Meeting of the Cognitive Science Society. 3352

Tsarava, K., Leifheit, L., Ninaus, M., Román-González, M., Butz,. M. V., Golle, J., Trautwein, U., & Möller, K. (2019). Cognitive correlates of computational thinking: Evaluation of a blended unplugged/plugge-in course. Proceedings of the 14th Workshop in Primary and Secondary Computing Education. Glasgow Scotland, Uk: Association for Computing Machinery. doi: 10.1145/3361721.3361729

2018

Butz, M. V., Bilkey, D., Knott, A., & Otte, S. (2018). REPRISE: A retrospective and prospective inference scheme. Proceedings of the 40th Annual Meeting of the Cognitive Science Society.

Butz, M. V., Knott, A., Coopr, R. P., Elman, J. L., McRae, K., Papafragou, A., & Zacks, J. M. (2018). Symposium on event predictive cognition. Proceedings of the 40th Annual Meeting of the Cognitive Science Society.

Gerjets, P., Lachmair, M., Butz, M. V., & Lohmann, J. (2018). Knowledge spaces in VR: Intuitive interfacing with a multiperspective hypermedia environment, 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), 555-556.

Kaiser, J., Melbaum, S., Vasquez Tieck, J. C., Roennau, A., Butz, M. V., & Dillmann, R. (2018). Learning to reproduce visually similar movements by minimizing event-based prediction error. 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 260-267.

Otte, S., Hofmaier, L., & Butz, M. V. (2018). Integrative collision avoidance within RNN-driven many-joint robot arms. Artificial Neural Networks and Machine Learning - ICANN 2018, 748-758.

2017

Butz, M. V. (2017). Which Structures Are Out There? - Learning Predictive Compositional Concepts Based on Social Sensorimotor Explorations. In T. Metzinger & W. Wiese (Eds.). Philosophy and Predictive Processing: 8. Frankfurt am Main: MIND Group. doi: 10.15502/9783958573093

Gumbsch, C., Otte, S., & Butz, M. V. (2017). A Computational Model for the Dynamical of Event Taxonomies. Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 452 - 457.

Otte, S., Butz, M. V. (2017) Differentiable Oscillators in Recurrent Neural Networks for Gradient-based Sequence Modeling. Artificial Neural Networks and Machine Learning - ICANN 2017, 745-746.

Otte, S., Schmitt, T., & Butz, M. V. (2017). Anticipatory Active Inference from Learned Recurrent Neural Forward Models. Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 3803.

Otte, S., Schmitt, T., Friston, K., & Butz, M. V. (2017). Inferring Adaptive Goal-Directed Behavior within Recurrent Neural Networks. Artificial Neural Networks and Machine Learning - ICANN 2016, 2017, 227-235.

Otte, S., Zwiener, A., & Butz, M. V.. Inherently Constraint-Aware Control of Many-Joint robot Arms with Inverse Recurrent Models. Artificial Neural Networks and Machine Learning - ICANN 2017, 262-270.

Tsarava, K., Moeller, K., Pinkwart, N., Butz, M. V., Trautwein, U., & Ninaus, M. (2017). Training Computational Thinking: Game-Based Unplugged and Plugged-in Activities in Primary School. Proceedings of The 11th European Conference on Game-Based Learning ECGBL 2017.

2016

Butz, M. V., & Zöllner, D. (2016). Towards grounding compositional concept structures in self-organizing neural encodings. In: Proceedings in Language and Cognition 1, Sensory Motor Concepts in Language & Cognition, ed. L. Ströbel, (pp 177-192). Düsseldorf: Düsseldorf university press.

Kloss, A., Kappler, D., Lensch, H. P. A., Butz, M. V., Schaal, S., & Blog, J. (2016). Learning where to search using visual attention. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 5238-5245, doi: 10.1109/IROS.2016.7759770

Seth, A. K., Verschure, P. F. M. J., Blanke, O., Butz, M. V., Ford, J. M., Frith, Ch. D., Jacob, P., Kyselo, M., McGann, M., Menary, R., Morsella, E., & O'Regan, J. K. (2016): Action-oriented understanding of consciousness and the structure of experience. In A. K. Engel, K. J. Friston, & D. Kragic (Eds.), The Pragmatic Turn: Toward Action-Oriented Views in Cognitive Science, Strüngmann Forum Reports, vol. 18, J. Lupp, series editor, (pp 261-281). Cambridge, MA: MIT Press, 2016.

2015

Belardinelli, A., & Butz, M. V. (2015). It’s all in the eye: multiple orders of motor planning in gaze control. Proceedings of the 37^th Annual Conference of the Cognitive Science Society, 2851.

Butz, M. V. (2015). Learning classifier systems. In J. Kacprzyk & W. Pedrycz (Eds.) Springer Handbook of Computation Intelligence, (pp. 961-981). Berlin: Springer-Verlag.

Butz, M. V., Geirhos, R., & Kneissler, J. (2015): An automatized Heider-Simmel Story generation tool. In D. C. Noelle, R. Cale, A. S. Warlaumont, J. Yoshimi, T. Mtlock, C. D Jennigs, & P. P. Maglio (Eds). (2015). Proceedings of the 37^th Annual Conference of the Cognitive Science Society: Vol. 2861

Koryakin. D., Schrodt, F., & Butz, M.V. (2015) Ensembles of Neural Oscillators, In Proceedings of Workshop New Challenges in Neural Computation 2015, Vol. 3, pp. 57-64

2014

Belardinelli, A., Butz, M. V (Eds.) (2014). Proceedings of the 12th biannual conference of the German cognitive science society. Cognitive Processing 15, Supp. 1, 1-158.

Belardinelli, A., Kurz, J. M., Kutter, E. F., Neumann, H., Karnath, H. O., & Butz, M. V. (2014). Modeling simultanagnosia. Proceedings of the 36th Annual Conference of the Cognitive Science Society, 1911 - 1916.

Ehrenfeld, S., & Butz, M. V. (2014). An embodied kinematic model for perspective taking. Proceedings of the 12th Biannual Conference of the German Cognitive Science Society (KogWis2014), Suppl. 1, 97-100.

Kneissler, J., & Butz, M. V..(2014). Learning spatial transformations using structured gain-field networks. Prodeedings of the International Conference on Artificial Neural Networks (ICANN 2014), 683-690.

Lohmann, J., & Butz, M. V. (2014). Memory disclosed by motion: predicting visual working memory performance from movement patterns. Proceedings of the 12th Biannual Conference of the German Cognitive Science Society (KogWis 2014), Suppl. 1, 52-53.

Schrodt, F., & Butz, M. V. (2014). Modeling perspective-taking by forecasting 3D biological motion sequences. Proceedings of the 12th Biannual Conference of the German Cognitive Science Society (KogWis 2014), Suppl. 1, 137-139.

Schrodt, F., Layher, G., Neumann, H., & Butz, M. V. (2014). Modeling perspective-taking by correlating visual and proprioceptive dynamics. Proceedings of the 36th Annual Conference of the Cognitive Science Society (CogSci 2014), 1383-1388,

Schrodt, F., Layher, G., Neumann, H., & Butz, M. V. (2014). Modeling perspective-taking upon observation of 3D biological motion. ICDL EpiRob Proceedings, 328-333.

2013

Alin, A., Fritsch, J., & Butz, M. V. (2013). Improved tracking and behavior anticipation by combining street map information with Bayesian filtering. International Conference on Intelligent Transportation Systems, IEEE 2013, pp. 2235-2242.

Belardinelli, A., & Butz, M. V. (2013). Gaze strategies in object identification and manipulation. Proceedings of the 35th annual meeting of the Cognitive Science Society CogSci 2013, 1875-1880.

Butz, M. V. (2013). Motivation. In A. Stephan, & S. Walter (Eds.), Handbuch Kognitionswissenschaft, (pp. 365-373). Stuttgart: J. B. Metzler.

Butz, M. V., Gufler, A., Schmid, K., & Schrodt, F. (2013). Fully self-supervised learning of an arm model. LWA Lernen, Wissen & Adaptivität 2013, Workshop Proceedings, pp. 184-190.

Cowling, P. I, Buro, M., Bida, M., Botea, B., Bouzy, B., Butz, M. V., Hingston, P., Munoz-Avila, H., Nau, D., & Sipper, M. (2013). Search in real-time video games. In S. M. Lucas, M. Mateas, M. Preuss, P. Spronck, & J. Togelius. (Eds.), Artificial and Computational Intelligence in Games (pp. 1-19).

Retrieved from www.dagstuhl.de/dagpub/978-3-939897-62-0

Ehrenfeld, S., Herbort, O., & Butz, M. V. (2013). On modular, multimodal arm control models. In G. Baldassarre, & M. Mirolli (Eds.), Computational and Robotic Models of the Hierarchical Organization of Behavior, Berlin Heidelberg: Springer.

Lohmann, J., & Butz, M. V. (2013). Modeling continuous representations in visual working memory. The Annual Meeting of the Cognitive Science Society, CogSci 2013, 2926-2931.

2012

Gütschow, J., Lohmann, J., Koryakin, D., & Butz, M.V. (2012). Learning Motor Primitives with Echo State Networks, In Proceedings of International Workshop "New Challenges in Neural Computation", Graz, Austria, 2012, Machine Learning Reports, 03/2012, pp. 20-33

Alin, A., Butz, M. V., & Fritsch, J. (2012). Incorporating Environmental Knowledge into Bayesian Filtering using Attractor Functions. IEEE Intelligent Vehicles Symposium (IV), pp. 476-481. doi:10.1109/IVS.2012.6232193

Butz, M.V., & Pezzulo, G. (2012). Anticipatory learning. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning, (pp. 263-266), Berlin Heidelberg: Springer.

Droniou, A., Ivaldi, S., Stalph, P. O., Butz, M., & Sigaud, O. (2012). Learning velocity kinematics: Experimental comparison of on-line regression algorithms. 12th International Conference on Autonomous Robot Systems and Competitions, Robotica 2012, 15-20.
Ehrenfeld, S., & Butz, M.V. (2012). Autonomous failure detection and multimodal sensor fusion in a modular arm model. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, 2186-2191.

Kneissler, J., Stalph, P. O., Drugowitsch, J., & Butz, M. V. (2012). Filtering sensory information with XCSF. Improving learning robustness and control performance. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, ACM, 871-878.

Koryakin, D., & Butz, M. V. (2012). Reservoir sizes and feedback weights interact non-linearly in echo state networks. ICANN International Conference on Artificial Neural Networks, 499-506, Berlin Heidelberg: Springer.

Lohmann, J., Herbort, O., & Butz, M. V. (2012). Modeling the temporal dynamics of visual working memory.

International Conference on Cognitive Modeling (ICCM 2012). www.iccm2012.com/proceedings/papers/0044/index.html

Pezzulo, G., & Butz, M. V. (2012). Schema-based architectures of machine learning. In N. M. Seel (Ed.), Encyclopedia of the Sciences of Learning, (2942-2945), Berlin Heidelberg: Springer.

Stalph, P. O., & Butz, M. V. (2012). Guided Evolution in XCSF. Conference on Genetic and Evolutionary Computation, ACM, 911-918.

2011

Alin, A., Butz, M. V., & Fritsch, J. (2011). Tracking moving vehicles using an advanced drid-based Bayesian filter approach. IEEE Intelligent Vehicles Symposium (IV), 466-472.

Butz, M. V. (2011). Towards Grounding Language in Self-organized Neural Encodings. Sensory-Motor Concepts in Language and Cognition. SMCLC 2011. http://www.sfb991.uni-duesseldorf.de/smclc11.

Butz, M. V., & Sigaud, O. (2011). XCSF with local deletion: Preventing detrimental forgetting. Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, ACM, 383-390.

Butz, M. V., & Stalph, P. O. (2011). Modularization of XCSF for multiple output dimensions. Proceedings of the 13th annual conference on Genetic and evolutionary computation, ACM, 1243-1250.

Butz, M. V. (2011) Extracting adaptation strategies for e-learning programs with XCS. Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, ACM, 743-746.

Ehrenfeld, S., & Butz, M. V. (2011). A modular, redundant, multi-frame of reference representation for kinematic chains. IEEE International Conference on Robotics and Automation, ICRA 2011, 141-147.

Lohmann, J. & Butz, M. V. (2011). Learning a neural multimodal body schema: Linking vision with proprioception. In B. Hammer, & T. Villmann, Workshop New Challenges in Neural Computation 2011, University of Bielefeld, Dept. of Technology CITEC, pp. 53-57.

2010

Butz, M. V. (2010). Curiosity in learning sensorimotor maps. In J. Haack, H. Wiese, A. Abraham, & C. Chiarcos (Eds.). KogWis 2010 - 10. Tagung der Gesellschaft für Kognitionswissenschaft (p.92). Potsdam Cognitive Science Series 2.

Herbort, O., & Butz, M. V. (2010). The continuous endstate comfort effect: The impact of contextual, motor and cognitive factors. 51st Annual Meeting of the Psychonomic Society.
Herbort, O., Butz, M. V., & Pedersen G. (2010). The SURE REACH model for motor learning and control of a redundant arm: From modeling human behavior to applications in robots. In J. Peters and O. Sigaud (Eds.), From motor to interaction learning in robots (pp. 85-106). Berlin Heidelberg: Springer.

Pedersen, G .K. M., & Butz, M. V. (2010). Evolving robust controller parameters using CMA. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, ACM, (pp. 1251-1258).

Pedersen, G. K. M., & Butz, M. V. (2010). Parameter investigation of muscle-like actuators. 1st International Conference on Applied Bionics and Biomechanics. ICABB-2010.

Stalph, P.O., & Butz, M. V. (2010). Current XCSF capabilities and challenges. In J. Bacardit, J. Drugowitsch, W. Browne, E. Bernadó-Mansilla, & M.V. Butz (Eds.), Learning classifier systems, LNCS 6471 (pp 57-69). Berlin Heidelberg: Springer.

Stalph, P. O., & Butz, M. V. (2010). How fitness estimates interact with reproduction rates: Towards variable offspring set sizes in XCSF. In J. Bacardit, J. Drugowitsch, W. Browne, E. Bernadó-Mansilla, & M.V. Butz (Eds.), Learning classifier systems, LNCS 6471 (pp 47-56). Berlin Heidelberg: Springer.

Stalph, P. O., Rubinsztajn, J., Sigaud, O, & Butz, M. V. (2010). A comparative study: Function approximation with LWPR and XCSF. Genetic and Evolutionary Computation Conference, GECCO 2010, IWLCS Workshop Proceedings (pp. 1863-1870).

Sugita, Y, & Butz, M. V. (2010). Towards emergent strong systematicity in a simple dynamical connectionist network. CONAS: Cognitive and Neural Models for Automated Processing of Speech and Text. http://conas.elis.ugent.be.

2009

Butz, M. V. (2009). Sensorimotor self-motivated cognition. In U. Schmid, M. Ragni, & M. Knauff (Eds.). Workshop on Complex Cognition. 32nd Annual Conference on Artificial Intelligence, KI 2009, Workshop Proceedings.

Butz, M. V., & Lönneker, T. (2009). Optimized sensory-motor couplings plus strategy extensions for the TORCS car racing challenge. IEEE Symposium on Computational Intelligence in Games, IEEE CIG 2009, 317-324.
Butz, M. V., & Pedersen, G. K. M. (2009). The scared robot: Motivations in a simulated robot arm. 32nd Annual Conference on Artificial Intelligence, KI 2009, 460-467.
Butz, M. V., Pedersen, G. K. M., & Stalph, P. O. (2009). Learning sensorimotor control structures with XCSF: Redundancy exploitation and dynamic control. Genetic and Evolutionary Computation Conference, GECCO 2009, 1171-1178.
Linhardt, M. J., & Butz, M. V. (2009). NEAT in increasingly non-linear control situations. Genetic and Evolutionary Computation Conference, GECCO 2009, 2091-2095.

Lohmann, J., Herbort, O., Wagener, A., & Kiesel, A. (2009). Anticipations of time spans: New data from the foreperiod paradigm and the adaptation of a computational model. In G. Pezzulo, M. V. Butz, O. Sigaud, G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems: From Psychological Theories to Artificial Cognitive Systems (pp. 170-187). Berlin, Heidelberg: Springer. (book website).

Pelikan, M., Sastry, K., Goldberg, D. E., Butz, M. V., & Hauschild, M. (2009). Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap. Genetic and Evolutionary Computation Conference, GECCO 2009, 851-858.
Pezzulo, G., Butz, M. V., Sigaud, O., & Baldassarre, G. (2009). From sensorimotor to higher-level cognitive processes: An introduction to anticipatory behavior systems. In G. Pezzulo, M. V. Butz, O. Sigaud, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems: From Psychological Theories to Artificial Cognitive Systems, LNAI 5499, (pp 1-9). Berlin Heidelberg: Springer.
Sigaud, O., Butz, M. V., Kozlova, O., & Meyer, C. (2009). Anticipatory learning classifier systems and factored reinforcement learning. In G.Pezzulo, M. V. Butz, O. Sigaud, & G. Baldassarre (Eds.) Anticipatory Behavior in Adaptive Learning Systems: From Psychological Theories to Artificial Cognitive Systems, LNAI 5499, (pp. 321-333). Berlin Heidelberg: Springer.
Stalph, P. O., Butz, M. V., Goldberg, D. E., & Llorà, X. (2009). On the scalability of XCS(F). Genetic and Evolutionary Computation Conference, GECCO 2009, 1315-1322.
Stalph, P. O., Butz, M. V., & Pedersen, G. K. M. (2009). Controlling a four degree of freedom arm in 3D using XCSF. 32nd Annual Conference on Artificial Intelligence, KI 2009, 193-200.
Shirinov, E., & Butz, M. V. (2009). Distinction between types of motivations: Emergent behavior with a neural, model-based reinforcement learning system. 2009 IEEE Symposium Series on Artificial Life (ALIFE 2009) Proceedings, 69-76.

2008

Bacardit, J., Bernadó-Mansilla, E., & Butz, M. V. (2008). Learning classifier systems: Looking back and glimpsing ahead. In J. Bacardit, E. Bernadó-Mansilla, E., M. V. Butz, T.Kovacs, X. Llorà, & K. Takadama (Eds.) Learning Classifier Systems, LNAI 4998, (pp. 1-21). Berlin Heidelberg: Springer

Butz, M. V., Herbort, O., & Pezzulo, G. (2008). Anticipatory, goal-directed behavior. In G. Pezzulo, M. V. Butz, C. Castelfranchi, & R. Falcone (Eds.) The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems, LNAI 5225 (pp. 85-114). Berlin Heidelberg: Springer.
(References)
Butz, M. V., & Pezzulo, G. (2008). Benefits of anticipations in cognitive agents. In G. Pezzulo, M. V. Butz, C. Castelfranchi, & R. Falcone (Eds.) The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems, LNAI 5225 (pp. 45-62). Berlin Heidelberg:Springer.
(References)

Butz, M. V., & Herbort, O. (2008). Context-dependent predictions and cognitive arm control with XCSF. GECCO 2008: Genetic and Evolutionary Computation Conference, 1357-1364 (best paper award).

Butz, M. V., Lanzi, P. L., Llorà, X., & Loiacono, D. (2008). An analysis of matching in learning classifier systems. GECCO 2008: Genetic and Evolutionary Computation Conference, 1349-1356.

Butz, M. V., Reif, K., & Herbort, O. (2008). Bridging the gap: Learning sensorimotor-linked population codes for planning and motor control. International Conference on Cognitive Systems (CogSys 2008), 123-129.

Butz, M. V., Stalph, P., & Lanzi, P. L. (2008). Self-adaptive mutation in XCSF. GECCO 2008: Genetic and Evolutionary Computation Conference, 1365-1372.

Herbort, O., Butz, M. V., & Hoffmann, J. (2008). Multimodal goal representations and feedback in hierarchical motor control. International Conference on Cognitive Systems (CogSys 2008).

Klügl, F., Hatko, R., & Butz, M. V. (2008). Agent learning instead of behavior implementation for simulations ? A case study using classifier systems. 6th German Conference on Multi-Agent System Technologies, MATES 2008, 111-122.

Pezzulo, G., Butz, M.V., & Castelfranchi, C. (2008). The anticipatory approach: Definitions and taxonomies. In Pezzulo, G., Butz, M.V., Castelfranchi, C., & Falcone, R. (Eds.) The challenge of anticipation: A unifying framework for the analysis and design of artificial cognitive systems, LNAI 5225, Springer-Verlag, Berlin Heidelberg, 23-43.
(References)

Pezzulo, G., Butz, M. V., Castelfranchi, C., & Falcone, R. (2008). Introduction: Anticipation in natural and artificial cognition. In G. Pezzulo, M. V. Butz, C. Castelfranchi, & R. Falcone, R. (Eds.) The challenge of anticipation: A unifying framework for the analysis and design of artificial cognitive systems, LNAI 5225 (pp. 3-22). Berlin Heidelberg: Springer.
(References)

Pezzulo, G., Butz, M. V., Castelfranchi, C., Falcone, R., Baldassarre, G., Balkenius, C., Förster, A., Grinberg, M., Herbort, O., Kiryazov, K., Kokinov, B., Johansson, B., Lalev, E., Lorini, E., Martinho, C., Miceli, M., Ognibene, D., Paiva, A., Petkov, G., Piunti, M., & Thorsteinsdottir, V. (2008). Endowing artificial systems with anticipatory capabilities: Success cases. In G. Pezzulo, M. V. Butz, C. Castelfranchi, & R. Falcone (Eds.) The Challenge of Anticipation: A unifying framework for the analysis and design of artificial cognitive systems, LNAI 5225 (pp. 237-254). Berlin Heidelberg: Springer.
(References)

Stalph, P., & Butz, M. V. (2008). Towards increasing learning speed and robustness of XCSF: Experimenting with larger offspring set sizes. GECCO 2008: Genetic and Evolutionary Computation Conference, Workshop Proceedings IWLCS 2008, 2023-2029.

2007

Bacardit, J., & Butz, M. V. (2007). Data mining in learning classifier systems: Comparing XCS with GAssist. In Kovacs, T., Llorà, X., Takadama, K., Lanzi, P. L., Stolzmann, W., & Wilson, S. W. (Eds.) Learning Classifier Systems: International Workshops, IWLCS 2003-2005, LNAI 4399 (pp. 282-290). Berlin Heidelberg: Springer.

Bacardit, J., Goldberg, D. E., & Butz, M. V. (2007). Improving the performance of a Pittsburgh learning classifier system using a default rule. In T. Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.) Learning Classifier Systems: International Workshops, IWLCS 2003-2005, LNAI 4399 (pp. 291-307). Berlin Heidelberg: Springer.

Butz, M. V. (2007). Combining gradient-based with evolutionary online learning: An introduction to learning classifier systems. Seventh International Conference on Hybrid Intelligent Systems (HIS 2007), 12-17.

Butz, M. V. (2007). Documentation of XCSFJava 1.1 plus visualization. Missouri Estimation of Distribution Algorithms Laboratory, MEDAL Report No. 2007008.

Butz, M. V. (2007). The XCSF classifier system in Java. SIGEVOlution, 2, 2, 10-13.

Butz, M. V., Goldberg, D.E., & Lanzi, P.L. (2007). Effect of pure error-based fitness in XCS. In Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.) Learning classifier systems: International Workshops, IWLCS 2003-2005, LNAI 4399 (pp. 104-114). Berlin Heidelberg: Springer.

Butz, M. V., Lenhard, A., & Herbort, O. (2007). Emergent effector-independent internal spaces: Adaptation and intermanual learning transfer in humans and neural networks. International Joint Conference on Neural Networks (IJCNN 2007). 1509-1514.

Butz, M. V., Sigaud, O., Pezzulo, G., & Baldassarre, G. (2007). Anticipations, brains, individual and social behavior: An introduction to anticipatory systems. In M. V. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.), Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior. LNAI 4520 (pp. 1-18), Berlin Heidelberg: Springer.

Herbort, O., & Butz, M. V. (2007). Encoding complete body models enables task dependent optimal behavior. International Joint Conference on Neural Networks (IJCNN 2007). 1424-1429.

Herbort, O., Ognibene, O., Butz, M. V., & Baldassarre, G. (2007). Learning to select targets within targets in reaching tasks. The 6^th IEEE International Conference on Development and Learning (ICDL2007), 7-12.

Lanzi, P. L., Butz, M. V., & Goldberg, D. E. (2007). Empirical analysis of generalization and learning in XCS with gradient descent. GECCO 2007: Genetic and Evolutionary Computation Conference. 1814-1821.

Pezzulo, G., Baldassarre, G., Butz, M. V., Castelfranchi, C., & Hoffmann, J. (2007). From actions to goals and vice-versa: Theoretical analysis and models of the ideomotor principle and TOTE. In M. V. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre, G. (Eds.), Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior. LNAI 4520 (pp. 73-93), Berlin Heidelberg: Springer.

2006

Butz, M. V., Lanzi, P. L., & Wilson, S. W. (2006). Hyper-ellipsoidal conditions in XCS: Rotation, linear approximation, and solution structure. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), 1457-1464.

Butz, M. V., & Pelikan, M. (2006). Studying XCS/BOA learning in Boolean functions: Structure encoding and random Boolean functions. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), 1449-1456.

Lima, C. F., Pelikan, M., Sastry, K., Butz, M. V., Goldberg, D. E., & Lobo, F. G. (2006). Substructural neighborhoods for local search in the Bayesian optimization algorithm. Parallel Problem Solving from Nature - PPSN IX, 232-241.

Pelikan, M., Sastry, K., Butz, M. V., & Goldberg, D. E. (2006). Performance of evolutionary algorithms on random decomposable problems. Parallel Problem Solving from Nature - PPSN IX, 788-797.

Pezzulo, G., Baldassarre, G., Butz, M. V., Castelfranchi, C., & Hoffmann, J. (2006). An analysis of the ideomotor principle and TOTE. In M. V. Butz, O. Sigaud, G. Pezzulo, & G. Baldassarre (Eds.) Proceedings of the Third Workshop on Anticipatory Behavior in Adaptive Learning Systems (ABiALS 2006).

2005

Butz, M. V. (2005). Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), 1835-1842.

Butz, M. V., Goldberg, D. E., & Lanzi, P. L. (2005). Computational complexity of the XCS classifier system. In L Bull, & T. Kovacs (Eds.) Foundations of Learning Classifier Systems, 91-126.

Butz, M. V., Pelikan, M., Llorà, X., & Goldberg, D. E. (2005). Extracted global structure makes local building block processing effective in XCS . Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005), 655-662.

Herbort, O., Butz, M. V., & Hoffmann, J. (2005). Towards an adaptive hierarchical anticipatory behavioral control system. In C. Castelfranchi, C. Balkenius, M. V. Butz, & A. Ortony (Eds.) From Reactive to Anticipatory Cognitive Embodied Systems: Papers from the AAAI Fall Symposium, AAAI Press, 2005, 83-90.

Herbort, O., Butz, M. V., & Hoffmann, J. (2005). Towards the advantages of hierarchical anticipatory behavioral control. In K. Opwis, & I. Penner (Eds.) Proceedings of the KogWis05. The German Cognitive Science Conference, Schwabe, 2005, 77.

2004

Bacardit, J., Goldberg, D. E., Butz, M. V., Llorà, X., & Garrell, J. M. (2004). Speeding-up Pittsburgh learning classifier systems: Modelling time and accuracy. Parallel Problem Solving from Nature - PPSN VIII, LNCS 3242, 1021-1031.

Butz, M. V., Goldberg, D. E., & Lanzi, P. L. (2004). Bounding learning time in XCS. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), LNCS 3103, 739-750.

Butz, M. V., Goldberg, D. E., & Lanzi, P. L. (2004). Gradient-based learning updates improve XCS performance in multistep problems. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), LNCS 3103, 751-762.

Butz, M. V., Lanzi, P. L., Llorà, X., & Goldberg, D. E. (2004). Knowledge extraction and problem structure identification in XCS. Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, LNCS 3242, 1051-1060.

Butz, M. V., Swarup, S., & Goldberg, D. E. (2004). Effective online detection of task-independent landmarks. Online Proceedings for the ICML'04 Workshop on Predictive Representations of World Knowledge.

2003

Butz, M. V., & Goldberg, D. E. (2003). Bounding the population size in XCS to ensure reproductive opportunities. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003). LNCS 2724, 1844-1856.

Butz, M.V., & Ray, S. (2003). Bidirectional ARTMAP: An artificial mirror neuron system. Proceedings of the International Joint Conference on Artificial Neural Networks (IJCNN 2003). 1417-1422.

Butz, M. V., Sastry, K., & Goldberg, D. E. (2003). Tournament selection in XCS. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003). LNCS 2724, 1857-1869. (Best paper award)

Tharakunnel, K., Butz, M. V., & Goldberg, D. E. (2003). Towards building block propagation in XCS: A negative result and its implications. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003). LNCS 2724, 1906-1917.

2002

Butz, M. V. (2002). Biasing exploration in an anticipatory learning classifier system. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.) Advances in Learning Classifier Systems: Fourth International Workshop (IWLCS 2001), LNAI 2321 (pp. 3-22). Berlin Heidelberg: Springer.

Butz, M. V., & Goldberg, D. E. (2002). Generalized state values in an anticipatory learning classifier system. Seventh International Conference on Simulation of Adaptive Behavior: From animals to animats. Adaptive Behavior in Anticipatory Learning Systems Workshop Proceedings. 78-96.

Butz, M. V., Sigaud, O., & Gérard, P. (2002). Internal models and anticipations in adaptive learning systems. Seventh International Conference on Simulation of Adaptive Behavior: From animals to animats. Adaptive Behavior in Anticipatory Learning Systems Workshop Proceedings. 1-20.

Butz, M. V., & Stolzmann, W. (2002). An algorithmic description of ACS2. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.) Advances in Learning Classifier Systems: Fourth International Workshop (IWLCS 2001), LNAI 2321 (pp. 211-230). Berlin Heidelberg: Springer.

2001

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2001). Probability-enhanced predictions in the anticipatory classifier system. In P. L. Lanzi, W. Stolzmann, & S. W. Wilson (Eds.) Advances in Learning Classifier Systems: Third International Workshop (IWLCS 2000), LNAI 1996 (pp. 37-52). Berlin Heidelberg: Springer.

Butz, M. V., Kovacs, T., Lanzi, P. L., & Wilson, S. W. (2001). How XCS evolves accurate classifiers. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 927-934

Butz, M. V., & Pelikan, M. (2001). Analyzing the evolutionary pressures in XCS. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 935-942.

Butz, M. V., & Wilson, S. W. (2001). An algorithmic description of XCS. In Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.) Advances in Learning Classifier Systems: Third International Workshop (IWLCS 2000), LNAI 1996 (pp. 253-272). Berlin Heidelberg: Springer.

2000

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2000). Introducing a genetic generalization pressure to the anticipatory classifier system: Part 1 - theoretical approach. Proceedings of the Second Genetic and Evolutionary Computation Conference (GECCO-2000), 34-41.

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2000). Introducing a genetic generalization pressure to the anticipatory classifier system: Part 2 - performance analysis. Proceedings of the Second Genetic and Evolutionary Computation Conference (GECCO-2000), 42-49.

Butz, M. V., Goldberg, D. E., & Stolzmann, W. (2000). Investigating genetic generalization in the anticipatory classifier system. Parallel problem solving from nature (PPSN VI), 735-744.

Stolzmann, W., & Butz, M. V. (2000). Latent learning and action planning in robots with anticipatory classifier systems. In Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.) Learning Classifier Systems: From Foundations to Applications, LNAI 1813 (pp. 301-317). Berlin Heidelberg: Springer.

Stolzmann, W., Butz, M. V., Hoffmann, J., & Goldberg, D. E. (2000). First cognitive capabilities in the anticipatory classifier system. Sixth International Conference on Simulation of Adaptive Behavior: From animals to animats. (SAB VI), 287-296.

1999

Butz, M. V., & Stolzmann, W. (1999). Action-planning in anticipatory learning classifier systems. 2^nd International Workshop on Learning Classifier Systems (IWLCS-99). Genetic and Evolutionary Computation Conference (GECCO 1999) Workshop Program, 242-249.

Books

Butz, M. V., & Kutter, E. F. (2017). How the mind comes into being: Introducing Cognitive Science from a functional and computational perspective. Oxford University Press.

Bacardit, J., Browne, W., Drugowitsch, J., Bernadó-Mansilla, E., & Butz, M. V. (Eds.) (2010). Learning classifier systems: 11th international workshop, IWLCS 2008, Atlanta, GA, USA, July 13, 2008 and 12th international workshop, IWLCS 2009 Montreal, QC, Canada, July 9, 2009 revised selected papers, LNCS 6471. Berlin Heidelberg: Springer.

Pezzulo, G., Butz, M. V., Sigaud, O., & Baldassarre G. (Eds.) (2009). Anticipatory Behavior in Adaptive Learning Systems: From Psychological Theories to Artificial Cognitive Systems, LNAI 5499 (State-of-the-Art Survey). Berlin Heidelberg: Springer.

Bacardit, J., Bernadó-Mansilla, E., Butz, M. V., Kovacs, T., Llorà, X., & Takadama, K. (Eds.) (2008). Learning Classifier Systems:10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 2006 and 11th International Workshop, IWLCS 2007, London, UK, July 2007 Revised Selected Papers, LNAI 4998. Berlin Heidelberg: Springer.

Pezzulo, G., Butz, M. V., Castelfranchi, C. & Falcone, R. (Eds.) (2008). The Challenge of Anticipation: A Unifying Framework for the Analysis and Design of Artificial Cognitive Systems, LNAI 5225 (State-of-the-Art Survey). Berlin Heidelberg: Springer.

Butz, M. V., Sigaud, O., Pezzulo, G., & Baldassarre, G. (Eds.) (2007). Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior, LNAI 4520 (State-of-the-Art Survey). Berlin-Heidelberg: Springer.

Butz, M. V. (2006). Rule-based evolutionary online learning systems: A principled approach to LCS analysis and design. Studies in Fuzziness and Soft Computing Series. Berlin Heidelberg: Springer.

Keijzer, M., Cattolico, M., Arnold, D., Babovic, V., Blum, C., Bosman, P., Butz, M. V., Coello Coello, C., Dasgupta, D., Ficici, S. G., Foster, J., Hernandez-Aguirre, A., Hornby, G., Lipson, H., McMinn, P., Moore, J., Raidl, G., Rothlauf, F., Ryan, C., & Thierens, D. (Eds.) (2006). GECCO: Proceedings of the 8th annual conference on genetic and evolutionary computation. Seattle, WA, USA: ACM Press.

Butz, M. V., Sigaud, O., & Gerard, P. (Eds.). (2003). Anticipatory Behavior in Adaptive Learning Systems: Foundations, Theories, and Systems, LNAI 2684 (State-of-the-Art Survey). Berlin Heidelberg: Springer.

Butz, M. V. (2002). Anticipatory learning classifier systems. Boston, MA: Kluwer Academic Publishers.