The interaction of brain, body, and environment can result in complex behavior with rich dynamics, even for relatively simple agents. Such dynamics are, however, often difficult to analyze. In this article, we explore the case of a simple simulated robotic agent, equipped with a reactive neurocontroller and an energy level, which the agent has been evolved to recharge. A dynamical systems analysis shows that a non-neural internal state (energy level), despite its simplicity, dynamically modulates the behavioral attractors of the agent—environment system, such that the robot's behavioral repertoire is continually adapted to its current situation and energy level. What emerges is a dynamic, non-deterministic, and highly self-organized action selection mechanism, originating from the dynamical coupling of four systems (non-neural internal states, neurocontroller, body, and environment) operating at very different timescales.
Ashby, W.R. (1960). Design for a brain: The origin of adaptive behavior . London: Chapman and Hall.
2.
Avila-García, O., & Cañamero, L. (2004). Using hormonal feedback to modulate action selection in a competitive scenario. In S. Schaal, A. Ijspeert, S. Billard, & J. Vijayakumar (Eds.), From Animals to Animats 8: Proceedings of the 8th International Conference on Simulation of Adaptive Behaviour (pp. 243—252). Cambridge, MA: MIT Press.
3.
Beer, R.D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173—215.
4.
Cañamero, D. (1997). Modeling motivations and emotions as a basis for intelligent behavior. In W. L. Johnson (Ed.), Proceedings of the 1st International Conference on Autonomous Agents (pp. 148—155). New York: ACM Press.
5.
Cañamero, L. (2003). Designing emotions for activity selection in autonomous agents. In R. Trappl, P. Petta, & S. Payr (Eds.), Autonomous agents (pp. 115—148). Cambridge, MA: MIT Press.
6.
Chiel, H., & Beer, R.D. (1997). The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences, 20(12), 553—557.
7.
Chrisley, R., & Ziemke, T. (2002). Embodiment. In Encyclopedia of cognitive science (pp. 1102—1108). London: Macmillan.
8.
Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, MA: MIT Press .
9.
Clark, A. (2003). Natural-born cyborgs: Minds, technologies and the future of human intelligence. New York: Oxford University Press.
10.
Damasio, A. (1994). Descartes' error: Emotion, reason, and the human brain. New York: G. P. Putnam's Sons.
11.
Di Paolo, E. (2003). Organismically-inspired robotics: Homeostatic adaptation and natural teleology beyond the closed sensorimotor loop. In K. Murase & T. Asakura (Eds.), Dynamical systems approach to embodiment and sociality (pp. 19—42). Adelaide: Advanced Knowledge International.
12.
Gadanho, S.C., & Hallam, J. (2001). Robot learning driven by emotions. Adaptive Behavior, 9(1), 42—64.
13.
Haykin, S., & Van Veen, B. (2003). Signals and systems. Upper Saddle River, NJ: Wiley.
14.
Herrera, C., Montebelli, A., & Ziemke, T. (2007). The role of internal states in the emergence of motivation and preference: A robotics approach. In A. Paiva, R. Prada, & R. W. Picard (Eds.), Affective computing and intelligent interaction (pp. 739—740). Berlin: Springer.
15.
Herrera, C., Ziemke, T., & Moffat, D. (2006). Emotions as a bridge to the environment. In S. Nolfi et al. (Eds.), From Animals to Animats 9: Proceedings of the 9th International Conference on Simulation of Adaptive Behaviour (pp. 3—16). Berlin: Springer.
16.
Hills, T.T. (2006). Animal foraging and the evolution of goal-directed cognition. Cognitive Science, 30, 3—41.
17.
Ito, M., Noda, K., Hoshino, Y., & Tani, J. (2006). Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Networks, 19(3), 323—337.
18.
Ito, M., & Tani, J. (2004). On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system. Adaptive Behavior, 12(2), 93—115.
19.
Kelso, J.A.S. (1995). Dynamic patterns: The self-organization of brain and behavior. Cambridge, MA: MIT Press.
20.
Lakoff, G., & Núñez, R. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. New York: Basic Books.
21.
Lowe, R., Herrera, C., Morse, A., & Ziemke, T. (2008). The embodied dynamics of emotion, appraisal and attention. In L. Paletta & E. Rome (Eds.), Attention in cognitive system (Vol. 4840, pp. 1—20). Berlin: Springer.
22.
Maes, P. (1991). A bottom-up mechanism for behavior selection in an artificial creature. In J. A. Meyer & S. W. Wilson (Eds.), From Animals to Animats: Proceedings of the 1st International Conference on Simulation of Adaptive Behavior (pp. 238—246). Cambridge, MA: MIT Press.
23.
Meyer, J.A. (1995). The animat approach to cognitive science. In H. L. Roitblat & J. A. Meyer (Eds.), Comparative approaches to cognitive science (pp. 27—44). Cambridge, MA: MIT Press.
24.
Montebelli, A., Herrera, C., & Ziemke, T. (2007). An analysis of behavioral attractor dynamics. In F. Almeida e Costa (Ed.), Advances in Artificial Life: Proceedings of the 9th European Conference on Artificial Life (ECAL 2007), Lecture notes in computer science (Vol. 4648, pp. 213— 222). Berlin: Springer.
25.
Nicholls, J.G., Martin, A.R., Wallace, B.G., & Fuchs, P.A. (2001). From neuron to brain. Sunderland, MA: Sinauer Associates.
26.
Nolfi, S. (1997). Using emergent modularity to develop control systems for mobile robots. Adaptive Behavior, 5(3—4), 343—363.
27.
Nolfi, S. (1998). Evolutionary robotics: exploiting the full power of self-organization. Connection Science, 10(3—4), 167—183.
28.
Nolfi, S. (2002). Power and limits of reactive agents. Neuro-computing, 42(1—4), 119—145.
29.
Nolfi, S., & Floreano, D. (2000). Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines. Cambridge, MA: MIT Press.
30.
Parisi, D. (2004). Internal robotics. ConnectionScience, 16(4), 325—338.
31.
Pfeifer, R. (1999). Dynamics, morphology, and materials in the emergence of cognition. In S. Biundo, T. Frühwirth, & G. Palm (Eds.), Advances in Artificial Intelligence: Proceedings of the 23rd Annual German Conference on Artificial Intelligence (pp. 27—44). Berlin: Springer .
32.
Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press.
33.
Prescott, T., Redgrave, P., & Gurney, K. (1999). Layered control architectures in robots and vertebrates . Adaptive Behavior, 7(1), 99—127.
34.
Prinz, J. (2004). Embodied emotions. In R. C. Solomon (Ed.), Thinking about feeling: Contemporary philosophers on the emotions (pp. 44—59). New York: Oxford University Press.
Tani, J. (2003). Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Networks, 16, 11—23.
37.
Tani, J., & Ito, M. (2002). Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment. IEEE Transactions on Systems, Man, and Cybernetics. Part A: Systems and Humans, 33(4), 481—488.
38.
Turner, J.S. (2002). The extended organism. Cambridge, MA: Harvard University Press.
39.
Van Gelder, T. (1998). The dynamical hypothesis in cognitive science . Behavioral and Brain Sciences, 21, 615—628.
40.
Ziemke, T. (2000). On “parts” and “wholes” of adaptive behavior: Functional modularity and diachronic structure in recurrent neural robot controllers. In J. A. Meyer , A. Berthoz, D. Floreano, H. L. Roitblat, & S. W. Wilson (Eds.), From Animals to Animats 6: Proceedings of the 6th International Conference on Simulation of Adaptive Behaviour (pp. 115—124). Cambridge, MA: MIT Press.
41.
Ziemke, T., Zlatev, J., & Frank, R. M. (Eds.). ( 2007). Body, language and mind: Embodiment (Vol. 1). Berlin: Mouton de Gruyter.
