Abstract
Theory presented by Ashby states that the process of homeostasis is directly related to intelligence and to the ability of an individual in successfully adapting to dynamic environments or disruptions. This paper presents an artificial homeostatic system under evolutionary control, composed of an extended model of the GasNet artificial neural network framework, named NSGasNet, and an artificial endocrine system. Mimicking properties of the neuro-endocrine interaction, the system is shown to be able to properly coordinate the behaviour of a simulated agent that presents internal dynamics and is devoted to explore the scenario without endangering its essential organization. Moreover, sensorimotor disruptions are applied, impelling the system to adapt in order to maintain some variables within limits, ensuring the agent survival. It is envisaged that the proposed framework is a step towards the design of a generic model for coordinating more complex behaviours, and potentially coping with further severe disruptions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cannon, W.B.: Organization for physiological homeostasis. Physiological Review 9, 399–431 (1929)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)
Ashby, W.R.: Design for a Brain: The Origin of Adaptive Behaviour. Chapman and Hall, London (1952)
Dyke, J., Harvey, I.: Hysteresis and the limits of homeostasis: From daisyworld to phototaxis. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 332–342. Springer, Heidelberg (2005)
Dyke, J.G., Harvey, I.R.: Pushing up the daisies. In: Proc. of Tenth Int. Conf. on the Sim. and Synthesis of Living Systems, pp. 426–431. MIT Press, Cambridge (2006)
Besendovsky, H.O., Del Rey, A.: Immune-neuro-endocrine interactions: Facts and hypotheses. Endocrine Reviews 17, 64–102 (1996)
Di Paolo, E.A.: Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions. In: From Animals to Animals, Proc. of the 6th Int. Conf. on the Simulation of Adaptive Behavior, pp. 440–449. MIT Press, Cambridge (2000)
Harvey, I.: Homeostasis and rein control: From daisyworld to active perception. In: Proc. of the 9th Int. Conf. on the Simulation and Synthesis of Living Systems, ALIFE9, pp. 309–314. MIT Press, Cambridge (2004)
Neal, M., Timmis, J.: Timidity: A useful mechanism for robot control. Informatica 7, 197–203 (2003)
Hoinville, T., Henaff, P.: Comparative study of two homeostatic mechanisms in evolved neural controllers for legged locomotion. In: Proccedings of 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, vol. 3, pp. 2624–2629 (2004)
Vargas, P.A., Moioli, R.C., Castro, L.N., Timmis, J., Neal, M., Von Zuben, F.J.: Artificial homeostatic system: a novel approach. In: Proc. of the VIIIth European Conf. on Artificial Life, pp. 754–764 (2005)
Moioli, R.C., Vargas, P.A., Von Zuben, F.J., Husbands, P.: Towards the evolution of an artificial homeostatic system. In: 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 4024–4031 (2008)
Vargas, P.A., Di Paolo, E.A., Husbands, P.: Preliminary investigations on the evolvability of a non-spatial GasNet model. In: Almeida e Costa, F., Rocha, L.M., Costa, E., Harvey, I., Coutinho, A. (eds.) ECAL 2007. LNCS (LNAI), vol. 4648, pp. 966–975. Springer, Heidelberg (2007)
Vargas, P.A., Di Paolo, E.A., Husbands, P.: A study of gasnet spatial embedding in a delayed-response task. In: Proc. of the XIth Int. Conf. on the Sim. and Synthesis of Living Systems, ALIFE-XI, Winchester, UK, August 5-8 (to appear, 2008)
Di Paolo, E.A.: Autopoiesis, adaptivity, teleology, agency. Phenomenology and the Cognitive Sciences 4(4), 429–452 (2005)
Storm, T.: KiKS, a Khepera simulator for Matlab 5.3 and 6.0, http://theodor.zoomin.se/index/2866.html
Collins, R., Jefferson, D.: Selection in massively parallel genetic algorithms. In: Proc. of the 4th Intl. Conf. on Genetic Algorithms, ICGA 1991, pp. 249–256. Morgan Kaufmann, San Francisco (1991)
Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42, 228–234 (1990)
Husbands, P., Smith, T., Jakobi, N., Shea, M.O.: Better living through chemistry: Evolving GasNets for robot control. Connection Science 10, 185–210 (1998)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moioli, R.C., Vargas, P.A., Von Zuben, F.J., Husbands, P. (2008). Evolving an Artificial Homeostatic System. In: Zaverucha, G., da Costa, A.L. (eds) Advances in Artificial Intelligence - SBIA 2008. SBIA 2008. Lecture Notes in Computer Science(), vol 5249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88190-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-88190-2_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88189-6
Online ISBN: 978-3-540-88190-2
eBook Packages: Computer ScienceComputer Science (R0)