Evolution and Analysis of Brain-Body-Environment Systems

COGS Q700, Fall 2011

Lecture: Tuesdays and Thursdays, 1:00 - 2:15, 833 Eigenmann
Instructor: Randall D. Beer
Office: 840 Eigenmann
Phone: 856-0873
Email: rdbeer [AT] indiana [DOT] edu

Course Description
Notions of embodiment, situatedness and dynamics are becoming increasingly important in cognitive science, converging on a view in which an agent's behavior and cognition are seen not merely as products of its brain alone, but rather as arising from the interaction between the agent's nervous system, body and environment. In order to evaluate the significance of these ideas, this course will examine the construction and analysis of models of complete brain-body-environment systems, with a particular emphasis on the use of tools from complex systems (including evolutionary algorithms, dynamical systems theory and information theory) to study how such coupled systems work. Behaviors to be studied range from basic motor behavior and sensorimotor learning to categorical perception, selective attention, agency detection and communication.

Grading

1/3 Homework Assignments

1/3 Class Presentation

1/3 Final Project/Paper

Assignments

Assignment 1 (due 9/20/11)

Assignment 2 (due 10/13/11)

Assignment 3 (due 11/1/11)

Assignment 4 (due 11/29/11) - AgentData.zip

Syllabus

Date Speaker Topic Readings

8/30 Beer Course Introduction

9/1 Beer Evolutionary Algorithms Fogel, D.B. (1994). An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks 5:3-14.

9/6 No Class - Spain

9/8 No Class - Spain

9/13 Beer Evolutionary Algorithms Harvey, I.H., Di Paolo, E., Wood, R. and Quinn, M. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11:79-98.

9/15 Beer Dynamical Neural Networks Hopfield, J.J. (1984). Neurons with graded responses have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci 81:3088-3092.

9/20 Beer CTRNNs and Dynamical Systems Theory Beer, R.D. (1995). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior 3(4):471-511.

9/22 Beer CTRNNs and Dynamical Systems Theory Beer, R.D. (2006). Parameter space structure of continuous-time recurrent neural networks. Neural Computation 18:3009-3051.

9/27 Beer Evolution of Walking Beer, R.D. and Gallagher, J.C. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1(1):91-122.

9/29 Beer Dynamical Analysis of Evolved Walkers Chiel, H.J., Beer, R.D. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking I. Dynamical modules. J. Computational Neuroscience 7:(2):99-118.

Beer, R.D., Chiel, H.J. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking II. General principles and individual variability. J. Computational Neuroscience 7(2):119-147.

10/4 Beer Dynamical Analysis of Evolved Walkers Psujek, S., Ames, J. and Beer, R.D. (2006). Connection and coordination: The interplay between architecture and dynamics in evolved model pattern generators. Neural Computation 18:729-747.

10/6 Beer Dynamical Analysis of Evolved Walkers Beer, R.D. (2010). Fitness space structure of a neuromechanical system. Adaptive Behavior 18:93-115.

10/11 Santosh Manicka Other Sensorimotor Examples 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.

10/13 Richard Betzel Other Sensorimotor Examples Izquierdo, E.J. and Lockery S.R. (2010). Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans. Journal of Neuroscience 30:12908-12917.

10/18 Beer Evolution and Analysis of Learning 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.

10/20 Alexander Gates Other Learning Examples Izquierdo, E., Harvey, I. and Beer, R.D. (2008). Associative learning on a continuum in evolved dynamical neural networks. Adaptive Behavior 16:361-384.

10/25 Jonathan Frankel Other Learning Examples Di Paolo, E.A. (2003). Evolving spike-timing-dependent plasticity for single-trial learning in robots. Phil. Trans. R. Soc. Lond. A 361:2299-2319.

10/27 Beer Evolution of Minimally Cognitive Behavior 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.

11/1 Beer Dynamical Analysis of Minimally-Cognitive Behavior Beer, R.D. (2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11(4):209-243.

11/3 Beer Dynamical Analysis of Minimally-Cognitive Behavior 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).

11/8 Beer Information Theory Cover, T.M. and Thomas, J.A. (2006). Elements of Information Theory (Chapter 2). Wiley.

11/10 Beer Information Theoretic Analysis of Minimally-Cognitive Behavior 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.

11/15 Beer Information and Dynamics

11/17 Steven Williams Other Minimally-Cognitive Behavior Examples Ward, R. and Ward, R. (2008). Selective attention and control of action: Comparative psychology of an artificial, evolved agent and people. J. Experimental Psychology: Human Perception and Performance 34:1165-1182.

11/22 Eran Agmon Other Minimally-Cognitive Behavior Examples Gigliotta, O., Pezzulo, G. and Nolfi, S. (2011). Evolution of a predictive internal model in an embodied and situated agent. Theory Biosci. DOI 10.1007/s12064-011-0128-x

11/24 No Class - Thanksgiving Break

11/29 Beer Evolution of Communication Williams, P.L., Beer, R.D., and Gasser, M. (2008). Evolving referential communication in embodied dynamical agents. In S. Bullock, J. Noble, R. Watson and M.A. Bedau (Eds.), Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (pp. 702-709). MIT Press.

12/1 Maxim Bushmakin Other Communication Examples Tuci, E. (2009). An investigation of the evolutionary origin of reciprocal communication using simulated autonomous agents. Biological Cybernetics 101:183-199.

12/6 Scott McCaulay Evolution of Social Behavior Froese, T. and Di Paolo, E. A. (2010). Modeling social interaction as perceptual crossing: An investigation into the dynamics of the interaction process. Connection Science 22(1):43-68.

12/8 Beer Discussion

Date	Speaker	Topic	Readings
8/30	Beer	Course Introduction
9/1	Beer	Evolutionary Algorithms	Fogel, D.B. (1994). An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks 5:3-14.
9/6		No Class - Spain
9/8		No Class - Spain
9/13	Beer	Evolutionary Algorithms	Harvey, I.H., Di Paolo, E., Wood, R. and Quinn, M. (2005). Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11:79-98.
9/15	Beer	Dynamical Neural Networks	Hopfield, J.J. (1984). Neurons with graded responses have collective computational properties like those of two-state neurons. Proc. Natl. Acad. Sci 81:3088-3092.
9/20	Beer	CTRNNs and Dynamical Systems Theory	Beer, R.D. (1995). On the dynamics of small continuous-time recurrent neural networks. Adaptive Behavior 3(4):471-511.
9/22	Beer	CTRNNs and Dynamical Systems Theory	Beer, R.D. (2006). Parameter space structure of continuous-time recurrent neural networks. Neural Computation 18:3009-3051.
9/27	Beer	Evolution of Walking	Beer, R.D. and Gallagher, J.C. (1992). Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1(1):91-122.
9/29	Beer	Dynamical Analysis of Evolved Walkers	Chiel, H.J., Beer, R.D. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking I. Dynamical modules. J. Computational Neuroscience 7:(2):99-118. Beer, R.D., Chiel, H.J. and Gallagher, J.C. (1999). Evolution and analysis of model CPGs for walking II. General principles and individual variability. J. Computational Neuroscience 7(2):119-147.
10/4	Beer	Dynamical Analysis of Evolved Walkers	Psujek, S., Ames, J. and Beer, R.D. (2006). Connection and coordination: The interplay between architecture and dynamics in evolved model pattern generators. Neural Computation 18:729-747.
10/6	Beer	Dynamical Analysis of Evolved Walkers	Beer, R.D. (2010). Fitness space structure of a neuromechanical system. Adaptive Behavior 18:93-115.
10/11	Santosh Manicka	Other Sensorimotor Examples	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.
10/13	Richard Betzel	Other Sensorimotor Examples	Izquierdo, E.J. and Lockery S.R. (2010). Evolution and analysis of minimal neural circuits for klinotaxis in Caenorhabditis elegans. Journal of Neuroscience 30:12908-12917.
10/18	Beer	Evolution and Analysis of Learning	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.
10/20	Alexander Gates	Other Learning Examples	Izquierdo, E., Harvey, I. and Beer, R.D. (2008). Associative learning on a continuum in evolved dynamical neural networks. Adaptive Behavior 16:361-384.
10/25	Jonathan Frankel	Other Learning Examples	Di Paolo, E.A. (2003). Evolving spike-timing-dependent plasticity for single-trial learning in robots. Phil. Trans. R. Soc. Lond. A 361:2299-2319.
10/27	Beer	Evolution of Minimally Cognitive Behavior	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.
11/1	Beer	Dynamical Analysis of Minimally-Cognitive Behavior	Beer, R.D. (2003). The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11(4):209-243.
11/3	Beer	Dynamical Analysis of Minimally-Cognitive Behavior	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).
11/8	Beer	Information Theory	Cover, T.M. and Thomas, J.A. (2006). Elements of Information Theory (Chapter 2). Wiley.
11/10	Beer	Information Theoretic Analysis of Minimally-Cognitive Behavior	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.
11/15	Beer	Information and Dynamics
11/17	Steven Williams	Other Minimally-Cognitive Behavior Examples	Ward, R. and Ward, R. (2008). Selective attention and control of action: Comparative psychology of an artificial, evolved agent and people. J. Experimental Psychology: Human Perception and Performance 34:1165-1182.
11/22	Eran Agmon	Other Minimally-Cognitive Behavior Examples	Gigliotta, O., Pezzulo, G. and Nolfi, S. (2011). Evolution of a predictive internal model in an embodied and situated agent. Theory Biosci. DOI 10.1007/s12064-011-0128-x
11/24		No Class - Thanksgiving Break
11/29	Beer	Evolution of Communication	Williams, P.L., Beer, R.D., and Gasser, M. (2008). Evolving referential communication in embodied dynamical agents. In S. Bullock, J. Noble, R. Watson and M.A. Bedau (Eds.), Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (pp. 702-709). MIT Press.
12/1	Maxim Bushmakin	Other Communication Examples	Tuci, E. (2009). An investigation of the evolutionary origin of reciprocal communication using simulated autonomous agents. Biological Cybernetics 101:183-199.
12/6	Scott McCaulay	Evolution of Social Behavior	Froese, T. and Di Paolo, E. A. (2010). Modeling social interaction as perceptual crossing: An investigation into the dynamics of the interaction process. Connection Science 22(1):43-68.
12/8	Beer	Discussion