TR available

[stefano@kant.irmkant.rm.cnr.it]

FTP-host: archive.cis.ohio-state.edu
FTP-filename: /pub/neuroprose/nolfi.plastic.ps.Z
FTP-filename: /pub/neuroprose/nolfi.erobot.ps.Z

Two papers are now available for copying from the Neuroprose repository. 
Hardcopies are not available. Comments are welcome.

PHENOTYPIC PLASTICITY IN EVOLVING NEURAL NETWORKS 
/pub/neuroprose/nolfi.plastic.ps.Z  12-pages
To appear In: Proceedings of the First Conference
              From Perception to Action, 5-9 September, Lausanne.

HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS
/pub/neuroprose/nolfi.erobot.ps.Z 9-pages
To appear in: Proceedings of ALIFEIV Conference, Cambridge, MA, 7-9 July. 


            PHENOTYPIC PLASTICITY IN EVOLVING NEURAL NETWORKS
   
           Stefano Nolfi, Orazio Miglino, and Domenico Parisi

We present a model based on genetic algorithm and neural networks. The neural 
networks develop  on  the  basis  of  an  inherited genotype  but  they  show 
phenotypic plasticity,  i.e.  they  develop  in  ways that are adapted to the 
specific environment. The genotype-to-phenotype  mapping  is  not  abstractly 
conceived as taking place in a single instant but  is a temporal process that 
takes a substantial portion  of  an  individual's lifetime to complete and is 
sensitive to the particular environment  in  which  the individual happens to 
develop. Furthermore,  the  respective  roles  of  the  genotype  and  of the 
environment  are  not decided a  priori but are part of what evolves. We show 
how such a model is able to evolve control systems for autonomous robots that 
can adapt to different types of environments.


HOW TO EVOLVE AUTONOMOUS ROBOTS: DIFFERENT APPROACHES IN EVOLUTIONARY ROBOTICS

      Stefano Nolfi, Dario Floreano, Orazio Miglino, and Francesco Mondada

A methodology for evolving  the  control systems  of autonomous robots has not
yet been well established. In this paper  we will  show  different examples of
applications  of  evolutionary robotics  to  real robots  by  describing three
different approaches to  develop neural controllers for  mobile robots. In all
the experiments described real robots are involved and are indeed the ultimate
means of evaluating  the  success and  the results of the procedures employed.
Each approach will be compared with the others and the relative advantages and
drawbacks will be discussed. Last, but not least, we will try  to tackle a few
important issues related to the design of the hardware and of the evolutionary
conditions in which the control system of the autonomous agent should evolve. 


Stefano Nolfi
Institute of Psychology
National Research Council
Viale Marx,15
00137 Rome, Italy
e-mail:stefano at kant.irmkant.rm.cnr.it