Evolving An Artificial Homeostatic System

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. © 2008 Springer Berlin Heidelberg.5249 LNAI278288Cannon, W.B., Organization for physiological homeostasis (1929) Physiological Review, 9, pp. 399-431Pfeifer, R., Scheier, C., (1999) Understanding Intelligence, , MIT Press, CambridgeAshby, W.R., (1952) Design for a Brain: The Origin of Adaptive Behaviour, , Chapman and Hall, LondonDyke, J., Harvey, I.: Hysteresis and the limits of homeostasis: From daisyworld to phototaxis. In: Capcarrére, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), 3630, pp. 332-342. Springer, Heidelberg (2005)Dyke, J.G., Harvey, I.R., Pushing up the daisies (2006) Proc. of Tenth Int. Conf. on the Sim. and Synthesis of Living Systems, pp. 426-431. , MIT Press, CambridgeBesendovsky, H.O., Del Rey, A., Immune-neuro-endocrine interactions: Facts and hypotheses (1996) Endocrine Reviews, 17, pp. 64-102Di Paolo, E.A., Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions (2000) From Animals to Animals, Proc. of the 6th Int. Conf. on the Simulation of Adaptive Behavior, pp. 440-449. , MIT Press, CambridgeHarvey, I., Homeostasis and rein control: From daisyworld to active perception (2004) Proc. of the 9th Int. Conf. on the Simulation and Synthesis of Living Systems, ALIFE9, pp. 309-314. , MIT Press, CambridgeNeal, M., Timmis, J., Timidity: A useful mechanism for robot control (2003) Informatica, 7, pp. 197-203Hoinville, T., Henaff, P., Comparative study of two homeostatic mechanisms in evolved neural controllers for legged locomotion (2004) Proccedings of 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 3, pp. 2624-2629Vargas, P.A., Moioli, R.C., Castro, L.N., Timmis, J., Neal, M., Von Zuben, F.J., Artificial homeostatic system: A novel approach (2005) Proc. of the VIIIth European Conf. on Artificial Life, pp. 754-764Moioli, R.C., Vargas, P.A., Von Zuben, F.J., Husbands, P., Towards the evolution of an artificial homeostatic system (2008) 2008 IEEE Congress on Evolutionary Computation (CEC, pp. 4024-4031Vargas, 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), 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 (2008) Proc. of the XIth Int. Conf. on the Sim. and Synthesis of Living Systems, ALIFE-XI, , Winchester, UK, August 5-8 to appearDi Paolo, E.A., Autopoiesis, adaptivity, teleology, agency (2005) Phenomenology and the Cognitive Sciences, 4 (4), pp. 429-452Storm, T., KiKS, a Khepera simulator for Matlab 5.3 and 6.0, , http://theodor.zoomin.se/index/2866.htmlCollins, 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 (1990) Physica D, 42, pp. 228-234Husbands, P., Smith, T., Jakobi, N., Shea, M.O., Better living through chemistry: Evolving GasNets for robot control (1998) Connection Science, 10, pp. 185-210Nolfi, S., Floreano, D., (2004) Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines, , MIT Press, Cambridg

A Research Agenda for Iterative Approaches to Inverse Problems Using Evolutionary Computation

This position paper addresses the relevance of evolutionary computation for iterative approaches to inverse problems. We focus on a set of six real-world problems selected from the areas of space dynamics, materials science, geophysics, heat transfer, oceanography and meteorology. These problems are far from being trivial and their associated direct models yield a wide structural diversity, thus providing a rich sample of the space of inverse problems. We neither discuss any particular problem in depth, nor present any results obtained so far. Our emphasis is on the research agenda defined by them, for the issue of deriving a generic methodology for approaching inverse problems that has evolutionary computation in its core