Stochastic Adaptation to Environmental Changes Supported by Endocrine System Principles

Adaptation to a changing environment presents a challenging task for today's electronic systems when operating in dynamic, fluctuating environments. This applies in particular to the systems which are to operate in harsh environments with no possibility for human intervention when the change occurs. As a rule, environmental changes are stochastic and so is the effect they may exhibit on a man-made system. On the other hand, evolution has provided living organisms with in-built mechanisms for adapting to environmental changes. In particular, homeostatic processes are example of such inherent adaptive mechanisms found within human body. Out of many complex and interweaved systems involved in homeostatic processes, hormones and endocrine system are prominent for certain properties they exhibit. In the first place, this refers to communication within the system and control of the regulatory processes, both of which are challenging issues within man-made systems. This paper investigates endocrine system principles applied within adaptive processes in a man-made system when adaptation is of stochastic nature. Presented results refer to applications in systems of modular architecture.

[1]  Jonathan Timmis,et al.  Timidity: A Useful Emotional Mechanism for Robot Control? , 2003, Informatica.

[2]  Jan Feyereisl,et al.  Don't Touch Me, I'm Fine: Robot Autonomy Using an Artificial Innate Immune System , 2006, ICARIS.

[3]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[4]  Hiroyuki Iizuka,et al.  Extended Homeostatic Adaptation: Improving the Link between Internal and Behavioural Stability , 2008, SAB.

[5]  Takashi Ikegami,et al.  Homeodynamics in the Game of Life , 2008, ALIFE.

[6]  Moshe Sipper,et al.  Evolution of Parallel Cellular Machines , 1997, Lecture Notes in Computer Science.

[7]  Andrew M. Tyrrell,et al.  Implementation results for a fault-tolerant multicellular architecture inspired by endocrine communication , 2005, 2005 NASA/DoD Conference on Evolvable Hardware (EH'05).

[8]  Dario Floreano,et al.  Bio-inspired artificial intelligence , 2008 .

[9]  Pauline C. Haddow,et al.  Extreme—Temperature Electronics Adaptability Based on Hormonal System Regulation , 2007 .

[10]  John Russell Fearn Don't Touch Me , 2009 .

[11]  W. Ashby,et al.  Design for a brain; the origin of adaptive behavior , 2011 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  E. D. Paolo,et al.  Organismically-inspired robotics: homeostatic adaptation and teleology beyond the closed sensorimotor loop , 2003 .

[14]  W. Ashby,et al.  Design for a brain: The origin of adaptive behaviour (2nd ed. rev.). , 1960 .

[15]  Inman Harvey,et al.  Hysteresis and the Limits of Homeostasis: From Daisyworld to Phototaxis , 2005, ECAL.

[16]  N J Macias,et al.  Adaptive methods for growing electronic circuits on an imperfect synthetic matrix. , 2004, Bio Systems.

[17]  Jonathan Timmis,et al.  Evolvable Hardware, a Fundamental Technology for Homeostasis , 2007, 2007 IEEE Workshop on Evolvable and Adaptive Hardware (WEAH2007).

[18]  Phil Husbands,et al.  Towards the evolution of an artificial homeostatic system , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[19]  M. Sipper,et al.  The Emergence of Cellular Computing , 1999, Computer.

[20]  Moshe Sipper,et al.  Evolution of Parallel Cellular Machines: The Cellular Programming Approach , 1997 .

[21]  Jonathan Timmis,et al.  Artificial Homeostatic System: A Novel Approach , 2005, ECAL.

[22]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[23]  Wei-Min Shen,et al.  Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots , 2002, IEEE Trans. Robotics Autom..

[24]  Kenneth L. Artis Design for a Brain , 1961 .