Solving the Division of
Labour Problem Using
Stigmergy and Evolved
Heterogeneity
Emyr James
Dr. Richard Watson
Dr. Jason Noble
Research Interest

Evolving Co-operative Teams of Software
Agents

How to represent an agent?

How should we put agents together in a team ?



For a given team composition and agent
representation how should we do the evolution?
Focus on Engineering Methodology
May be able to give insight into some Biological
questions along the way.
Examples from the Literature

2 Main methods





Homogenous teams of clones (Quinn, Floreano)
Heterogenous teams built from co-evolved subpopulations (Uchibe et al.)
Other methods – Chromosomal Model (Andre
and Teller), Legion System (Bongard)
Agent and Team types fixed a priori
Is it possible for the amount of heterogeneity to
be evolved ?
Search for a Suitable Task



Simple team task
based on santa fe ant
in GP
Ant can execute
following
commands...
if_food_ahead,
progn2, progn3, left,
right, move.
Controller is GP tree
made up of these
primitives
Demonstration
Removing Stigmergy


Task changes from
Mowing to Spraying.
No possibility of
stigmergy. Added
specialisation
command IfAgentn
and an identifying tag
for each individual
(Tanev et al. got there
first....)
Experimental Design




Have 2 tasks, analogous to Mowing and
Spraying.
Mowing task allows stigmergy so no need for
specialisation.
Only way to divide labour in Spraying is through
specialisation.
Will evolution use an appropriate amount of
specialisation for the two tasks ?
Method




4 Experiments carried out, two for each task.
Specialisation commands turned on and off.
In each run, population was 1000. All members
initialised with trees of size 1 (i.e. only terminal
commands). Teams of size 6.
Undergo process of evolution utilising crossover
and mutation, underlying GA was Deterministic
Crowding due bloat mitigation
26 runs, each allowed 2 hrs cpu time. Data
analysed to pick out best of run.
Results
Fitness
Biological Metaphors

Two biological metaphors fit this scheme
and suggest ways in which to take this
work further
Multi-cellular organism with
differentiated cells.
 Polyphenism

Further Work




See how far the multicellularity metaphor can
go – change from id for each agent to agent
classes. Hierarchical differentiation ?
Polyphenism – talk by Rob Mills yesterday.
Vary team size. Bigger teams require more coordination. Will the degree of specialisation
reflect this ?
Compare this approach with the purely clonal
and purely heterogeneous methods from the
literature.
Conclusions



Have shown that varying amounts of
specialisation is evolved to suit the task.
This method good for situations where agents
have some common behaviour but need some
specialisation under certain circumstances.
Biological metaphor – clonal differentiated cells
– team can be considered to be a multi-cellular
organism.