wp3: language evolution

WP3: Language Evolution

Paul VogtFederico DivinaTilburg University

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

Language games

Referent

Form“Cabbage”

Category

Category

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

Varying No. of Features

Divina & Vogt, Proc. EELC, 2006

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

Social learning

Assuming communication has evolved, how can language be used to acquire new skills?

Example

h

f

E

M

T

T E

L R

A1

“hungry,have-food, eat”

hT E

M

A2

E L

{h,f,E}

Example

h

f

T

hT E

M

A1 A2

{h,f,E}“hungry,no-food,talk” {h,¬f,T}

E

T E

M

L R E L

Example

hT E

M

A2

{h,f,E} {h,¬f,T}

E L

Example

hT E

M

A2

{h,f,E} {h,¬f,T}

f

T

E L

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

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...