WP3: Language Evolution

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. Category Category Language games Form “Cabbage” Referent

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

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

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

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

13. T E M E L R Example A1 A2 h h T E f M T E L {h,¬f,T} “hungry,no-food,talk” {h,f,E}

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

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

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...