1. WP3: Language Evolution Paul Vogt Federico Divina Tilburg University

2. Objectives (from Annex I) … to design a population such that it is capable of evolving one (or possibly more) languages that enables them to optimize cooperation. A secondary objective is to design the experiment such that the agents will discover communication as a useful strategy and find ways to use this strategy effectively.

3. Tasks Task 3.1 Define (…) the required set-up for evolving language, learning how to use communication and how to react properly on linguistic communication (…). Year 1: M3.1 Task 3.2 Implement the code for under 3.1 defined specifications and integrating the results achieved in tasks 2.2 and 2.3. Year 2: D3.1 Task 3.3 Perform experiments with the system as implemented in task 3.2. Started Year 2 Task 3.4 Report on the experiments performed. Started Year 2

4. Overview State of WP3 Language games Preliminary results Social learning of skills Outlook final year Conclusions

5. Language games Referent Form “ Cabbage ” Category

6. Aspects of language learning Establishing joint attention  pointing Cross-situational learning  statistical co-occurrences across situations Feedback  not reliable Principle of contrast  associations with existing meanings lower initial score

7. Experiments Aim: To test effect of learning mechanisms on language development Conditions:  Fixed controller (no individual learning)  Reproduction, but no evolution  Socialness gene randomly set  Possible actions: move, turn, pick-up, eat, mate, talk & shout  Possible topics: features of one object  Fixed categories  Initial population size = 100  Simulated for 36,500 time steps (~100 NTYears)

8. Some statistics Per time step: ~27 language games initiated (total simulation ~1 million games) ~42% of games accompanied by pointing gesture ~12% of games accompanied by feedback signal ~50% of games no pointing, nor feedback

9. Varying No. of Features Divina & Vogt, Proc. EELC, 2006

10. Excluding learning mechanisms Vogt & Divina, Interaction Studies, in press

11. Social learning Assuming communication has evolved, how can language be used to acquire new skills?

12. Example h f E M T T E LR A1 “hungry,have-food, eat” h T E M A2 EL {h,f,E}

13. Example h f T h T E M A1A2 {h,f,E}“hungry,no-food,talk” {h,¬f,T} E T E M LR EL

14. Example h T E M A2 {h,f,E} {h,¬f,T} EL

15. Example h T E M A2 {h,f,E} {h,¬f,T} f T EL

16. Will it work? Good question, we don’t know... RL has (at least) 2 ways of deciding which nodes to insert  Random insertion  ‘Intelligent’ insertion Our feeling is that second option could be more effective and integrates language evolution & social learning elegantly

17. Outlook final year Integrating social learning (mostly done) – also using ‘telepathy’ Performing experiments to  Improve model regarding accuracy  Evolve language that aids survival & social learning Focus of interest:  Language diffusion  Emergence of dialects  Social learning  (Grammar) Define language specific challenges

18. Conclusions Made great progress Language games work well beyond chance, but could be improved Social learning of skills defined, implemented, but not integrated Still much to do...