Supporting material for the paper:

The Evolution of Acoustic Communication Between Two Robots

by Elio Tuci and Christos Ampatzis.
April 2007

Submitted to ECAL 2007

Table of Contents
  1. Fitness Issues
  2. Correctness of Signal Sc Table
  3. Failure due to trial-and-error Table
  4. Failure due to Collisions Table
  5. Movies

Fitness Issues:



This file (pdf) gives details of the fitness function by explaining how the average fitness score (F) is computed.

The figure above is the Fitness graph of the best groups at each generation of ten evolutionary runs. Notice that only two evolutionary runs managed to produce groups whose average fitness F is close to the maximum score. However, fitness scores lower than 3.4 might be associated to equally successful alternative strategies. In particular, successful strategies may have the fitness component Fc < 1 in case Mc doesn't correctly set Sc for the entire length of the time interval from tc to T as demanded by the fitness function (see equation 5 and 6 in Sec. 4 of the paper). For a group to be successful, what matters is that (i) Mc is capable of discriminating environment in which the door revolves clockwise from those in which the door revolves anticlockwise; (ii) this discrimination is made available to Mc through the value of Sc; (iii) differences in time of Sc's reading induce different behavioral responses. How these processes are implemented may vary with respect to the nature of the mechanisms found by evolution. Not all the implementations which allow a group to be successful get the highest fitness score.



Correctness of Signal Sc Table



Post-evaluation analysis of the 10 best groups. This table shows the percentage of the correctness of the binary categorisation signal Sc. We can see that it is very high (>95%) for all environments and both robots only for the two successful evolutionary runs g2 and g4.



Failure due to trial-and-error strategy Table



Post-evaluation analysis of the 10 best groups. This table shows the percentage of unsuccessful trials per robot in each type of environment due to trial-and-error strategy. This corresponds to a robot exerting forces in both arms of the revolving door (i.e., west and east of L3) instead of touching the bar on the correct side. In grey the succesful groups.



Failure due to collisions Table



Post-evaluation analysis of the 10 best groups. This table shows the percentage of unsuccessful trials per robot in each type of environment due to collisions. In grey the succesful groups. As we can see from the table, failure due to collisions are very rare.



Movies:


Below are sample movies from the experiments performed in the four environments:
(Movies are encoded in mpeg format)