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.
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
Overview
State of WP3 Language games Preliminary results Social learning of skills Outlook final year Conclusions
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
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)
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
Excluding learning mechanisms
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
Standard No Feedback No Principleof Contrast
No Cross-situationallearning
No Pointing
Acc
urac
y
Vogt & Divina, Interaction Studies, in press
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
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