About

This site serves as a repository of published work in the field of minimal cognition and evolutionary robotics.

The List

• Yamauchi, B. and Beer, R.D. (1994). Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior 2(3):219-246.
• Beer, R.D. (1996). Toward the evolution of dynamical neural networks for minimally cognitive behavior. In P. Maes, M. Mataric, J. Meyer, J. Pollack and S. Wilson (Eds.), From animals to animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (pp. 421-429). MIT Press.
• Slocum, A.C., Downey, D.C. and Beer, R.D. (2000). Further experiments in the evolution of minimally cognitive behavior: From perceiving affordances to selective attention. In J. Meyer, A. Berthoz, D. Floreano, H. Roitblat and S. Wilson (Eds.), From Animals to Animats 6: Proceedings of the Sixth International Conference on Simulation of Adaptive Behavior (pp. 430-439). MIT Press.
• Beer, R.D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3):91-99.
• Mathayomchan, B. and Beer, R.D. (2002). Center-crossing recurrent neural networks for the evolution of rhythmic behavior. Neural Computation 14:2043-2051.
• Beer, R.D. (2003). The dynamics of active categorical perception in an evolved model agent (with commentary and response). Adaptive Behavior 11(4):209-243.
• Goldenberg, E., Garcowski, J. and Beer, R.D. (2004). May we have your attention: Analysis of a selective attention task. In S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam and J.-A. Meyer (Eds.), From Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior (pp. 49-56). MIT Press.
• Phattanasri, P., Chiel, H.J. and Beer, R.D. (2007). The dynamics of associative learning in evolved model circuits. Adaptive Behavior 15(4):377-396.
• Williams, P.L., Beer, R.D., and Gasser, M. (2008). An embodied dynamical approach to relational categorization. In B.C. Love, K. McRae and V.M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 223-228).
• Izquierdo, E., Harvey, I. and Beer, R.D. (2008). Associative learning on a continuum in evolved dynamical neural networks. Adaptive Behavior 16:361-384.
• Beer, R.D. and Williams, P.L. (2009). Animals and animats: Why not both iguanas? (A commentary on “Animals versus animats: or Why not the real iguana?” by Barbara Webb). Adaptive Behavior 17:296-302.
• Williams, P.L. and Beer, R.D. (2010). Information dynamics of evolved agents. In S. Doncieux, B. Girard, A. Guillot, J. Hallam, J.-A. Meyer and J-B. Mouret (Eds.), From Animals to Animats 11: Proceedings of the 11th International Conference on Simulation of Adaptive Behavior (pp. 38-49). Springer.
• I. Harvey (1997): Cognition is not Computation: Evolution is not Optimisation In Artificial Neural Networks – ICANN97, Gerstner, W., Germond, A., Hasler, M, and Nicoud, J-D (eds). Springer-Verlag LNCS 1327 (1997) pp. 685-690. ISBN: 3540636315 Proc. of 7th International Conference on Artificial Neural Networks, 7-10 October 1997, Lausanne, Switzerland.
• I. Harvey (2000): Robotics: Philosophy of Mind using a Screwdriver In Evolutionary Robotics: From Intelligent Robots to Artificial Life, Vol. III, T. Gomi (ed), AAI Books, Ontario, Canada, 2000. pp. 207-230. ISBN 0-9698872-3-X.
• E. Tuci, I. Harvey and P.M. Todd (2002): Using a net to catch a mate: Evolving CTRNNs for the Dowry Problem. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB’02), 4-9 August 2002, Edinburgh, UK. MIT Press, ISBN: 0262582171.
• E. Tuci, I. Harvey and M. Quinn (2002): Evolving integrated controllers for autonomous learning robots using dynamic neural networks. Proceedings of The Seventh International Conference on the Simulation of Adaptive Behavior (SAB’02), 4-9 August 2002, Edinburgh, UK. MIT Press, ISBN: 0262582171.
• E. Tuci, M. Quinn and I. Harvey (2002): Evolving fixed-weight networks for learning robots. Proceedings Congress on Evolutionary Computation (CEC) 2002, Honolulu, Hawaii, May 12-17, 2002. IEEE Press, ISBN: 0780372824.
• Harvey, I., (2004). Homeostasis and Rein Control: From Daisyworld to Active Perception  Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems, ALIFE’9, Pollack, J., Bedau, M,. Husbands, P., Ikegami, T., and Watson, R.A. (eds), pp. 309-314.  MIT Press, Cambridge MA.
• Izquierdo-Torres, E. and Harvey, I. (2006): Learning on a Continuum in Evolved Dynamical Node Networks Artificial Life X, Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems, Proceedings Editors: L.M. Rocha, L.S. Yager, M.A. Bedau, D. Floreano, R.L. Goldstone, A. Vespignani, MIT Press.  ISBN-10: 0-262-68162-5. Pp. 507-512.
• Izquierdo-Torres, E. and Harvey, I. (2007): Hebbian Learning using Fixed Weight Evolved Dynamical ‘Neural’ Networks. In H. A. Abbass et al. (eds.), Proceedings of the First IEEE Symposium on Artificial Life. (IEEE-ALife’07). Pp. 394-401. ISBN: 1-4244-0698-6. IEEE Press.
• Harvey, I. (2008). Misrepresentations. In S. Bullock, J. Noble, R. A. Watson, and M. A. Bedau (Eds.) Proceedings of the Eleventh International Conference on Artificial Life, pp.227-233, MIT Press, Cambridge, MA. ISBN: 978-0-262-75017-2
• Harvey, I., Di Paolo, E., Wood, R., Quinn, M, and E. A., Tuci, (2005).   Evolutionary Robotics: A new scientific tool for studying cognition Artificial Life, 11(1-2), pp. 79-98.
• Di Paolo, E. A. and Harvey, I., (2003). Decisions and noise: the scope of evolutionary synthesis and dynamical analysis. Adaptive Behavior 11(4): 284-288.
• E. Tuci, M. Quinn and I. Harvey (2003): An evolutionary ecological approach to the study of learning behaviour using a robot based model Adaptive Behavior 10(3/4):201-222, 2003.
• Di Paolo, E. A. (2000). Behavioral coordination, structural congruence and entrainment in a simulation of acoustically coupled agents. Adaptive Behavior 8:1. 25 – 46.
• Vickerstaff, R. J., and Di Paolo, E. A., (2005). Evolving neural models of path integration. Journal of Experimental Biology, 208, pp. 3349 – 3366.
• Macinnes, I., and Di Paolo, E. A., (2006). The advantages of evolving perceptual cues. Adaptive Behavior 14(2), 147 – 156.
• Iizuka, H., and Di Paolo, E. A. (2007). Toward Spinozist robotics: Exploring the minimal dynamics of behavioural preference. Adaptive Behavior, 15, 359-376.
• Di Paolo, E. A., Rohde, M. and Iizuka, H. (2008). Sensitivity to social contingency or stability of interaction? Modelling the dynamics of perceptual crossing New Ideas in Psychology , 26: 278 – 294,
• Froese, T. and Di Paolo, E. A. (2010) Modelling social interaction as perceptual crossing: an investigation into the dynamics of the interaction process, Connection Science, 22(1): 43 – 68.
• Di Paolo, E. A., Noble, J. & Bullock, S. (2000). Simulation models as opaque thought experiments.  Artificial Life VII: The Seventh International Conference on the Simulation and Synthesis of Living Systems, Reed College, Portland, Oregon, USA, 1-6 August, 2000.
• Di Paolo, E. A., (2000). Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions. From Animals to Animals, Proc. of the Sixth International Conference on the Simulation of Adaptive Behavior, SAB’2000, Paris, J-A. Meyer, A. Berthoz, D. Floreano, H. Roitblat and S W. Wilson (eds), MIT Press, pp. 440-449
• Izquierdo-Torres, E., and Di Paolo, E. A., (2005). Is an embodied system ever purely reactive? Advances in Artificial Life, Proceedings ECAL 2005, 8th European Conference, Canterbury UK, M. S. Capcarrere et al. (eds), Springer, Berlin Heidelberg, LNAI 3630, pp 252-261.
• Iizuka, H., and Di Paolo, E. A., (2006). Are you interacting with me? The embodied dynamics of minimal agency detection. Last minute abstract From Animats to Animals 9 The Ninth International Conference on the Simulation of Adaptive Behavior (SAB’06) 25 – 29 September 2006, CNR, Roma, Italy .
• Rohde, M and Di Paolo, E. A. (2006) An evolutionary robotics simulation of human minimal social interaction. Workshop on Behaviour and Mind as a Complex Adaptive System, SAB’06, Roma, Italy, 30 September 2006.
• Fine, P., Di Paolo, E. A., Izquierdo, E. (2007). Adapting to your body. Proceedings of the 9th European Conference on Artificial life ECAL 2007. Springer-Verlag.
• Rohde, M. and Di Paolo, E. A. (2007). Adaptation to sensory delays. An evolutionary robotics model of an empirical study. Proceedings of the 9th European Conference on Artificial life ECAL 2007. Springer-Verlag.
• Iizuka, H. and Di Paolo, E. A. (2007). Minimal agency detection of embodied agents. Proceedings of the 9th European Conference on Artificial life ECAL 2007. Springer-Verlag.
• Wood, R. and Di Paolo, E. A. (2007). New models for old questions: Evolutionary robotics and the ‘A not B’ error. In Proceedings of the 9th European Conference on Artificial life ECAL 2007. Springer-Verlag.
• Iizuka, H. and Di Paolo, E. A. (2008). Extended homeostatic adaptation: Improving the link between internal and behavioural stability. In From Animats to Animals 10, The Tenth International Conference on the Simulation of Adaptive Behavior, Osaka, Japan, July 7-10, 2008
• Rohde, M. and Di Paolo, E. A. (2008). Embodiment and perceptual crossing in 2D: A comparative evolutionary robotics study. In From Animats to Animals 10, The Tenth International Conference on the Simulation of Adaptive Behavior, Osaka, Japan, July 7-10, 2008.
• Buckley, C., Fine, P. Bullock, S. and Di Paolo, E. A. (2008). Monostable controllers for adaptive behaviour. In From Animats to Animals 10, The Tenth International Conference on the Simulation of Adaptive Behavior, Osaka, Japan, July 7-10, 2008.
• Froese, T. and Di Paolo, E. A. (2008). Stability of coordination requires mutuality of interaction in a model of embodied agents. In From Animats to Animals 10, The Tenth International Conference on the Simulation of Adaptive Behavior, Osaka, Japan, July 7-10, 2008.
• Froese, T. and Di Paolo, E. A. (2009) Toward minimally social behavior: Social psychology meets evolutionary robotics, in Kampis, G., Karsai, I and Szathmary, E. (eds) Advances in Artificial Life Proceedings of the 10th European Conference on Artificial Life, ECAL09, Budapest, September 13-16, 2009, LNAI 5777, Springer Verlag, pp. 420 – 427.
• Quinn, M. (2001) Evolving Communication without Dedicated Communication Channels. In J. Kelemen and P. Sosík, editors, ECAL01, pages 357–366. Prague: Springer.
• Izquierdo, E.J., and Buhrmann, T. (2008) Analysis of a dynamical recurrent neural network evolved for two qualitatively different tasks: Walking and chemotaxis. In S. Bullock et al. (Eds.) Proceedings of the 11th International Conference on Artificial Life (pp. 257-264). MIT Press.