1. Solving the Division of Labour Problem Using Stigmergy and Evolved Heterogeneity Emyr James Dr. Richard Watson Dr. Jason Noble

2. 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.

3. Examples from the Literature 2 Main methods Homogenous teams of clones (Quinn, Floreano) Heterogenous teams built from co-evolved sub- populations (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 ?

4. 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

5. Demonstration

6. Extending the Single Ant Task Team of ants all having same controller tree – purely clonal team. Ants start in centre of grid which is initially filled with food. Bounded grid – if_edge_ahead command Aim is to eat all the food in as few timesteps as possible One timestep corresponds to a move by each ant, they take turns to move. Asynchronicity allows division of labour through stigmergy

7. Removing Stigmergy Task changes from Mowing to Spraying. stigmergy and an identifying tag for each individual (Tanev et al. got there first....) No possibility of stigmergy. Added specialisation command IfAgentn and an identifying tag for each individual (Tanev et al. got there first....)

8. 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 ?

9. 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.

10. Results

11. Fitness

12. Biological Metaphors Two biological metaphors fit this scheme and suggest ways in which to take this work further Two biological metaphors fit this scheme and suggest ways in which to take this work further Multi-cellular organism with differentiated cells. Multi-cellular organism with differentiated cells. Polyphenism Polyphenism

13. 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 co- ordination. Will the degree of specialisation reflect this ? Compare this approach with the purely clonal and purely heterogeneous methods from the literature.

14. 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.