1. Modelling language origins and evolution IJCAI-05 Demo THSim Tutorial modelling language evolution Paul Vogt

2. Modelling language origins and evolution IJCAI-05 THSim - Talking Heads simulation tool http://www.ling.ed.ac.uk/~paulv/thsim.html

3. Modelling language origins and evolution IJCAI-05 Discrimination World is a collection of objects (shapes on whiteboard) –Represented as features: Red, Green, Blue, Shape (A), X, Y –Context = a set of objects on white board –Topic = one particular object Robots want to build a set of meanings Meaning is a region represented by a prototype –A particular colour, area and location The category of every object is the region represented by its nearest prototype An object is discriminated if its category is different from all the others in the context If discrimination fails, a new category is constructed by taking the topic’s features to form a new prototype

4. Modelling language origins and evolution IJCAI-05 Simplified example CONTEXT: A=(0.1, 0.3) B=(0.3, 0.3) C=(0.25, 0.15) ROBOT’S PROTOTYPES: a=(0.15, 0.25) b=(0.35, 0.3) A is categorised as a B is categorised as b C is categorised as b A is discriminated B and C are not AB C a b

5. Modelling language origins and evolution IJCAI-05 Guessing game Speaker produces an utterance to name the topic. Hearer guesses the reference of the topic by searching its lexicon for most likely interpretation. Speaker provides corrective feedback on the outcome. Agents adapt lexicon: –Speaker may produce new word. –Hearer may adopt utterance. –Successfully used associations reinforced. –Unsuccessful associations inhibited.

6. Modelling language origins and evolution IJCAI-05 Demo I Showing the workings. –Population size: 2 –Used features: Red, Green, Blue. –World: Fixed set of 12 colours. –Nr of games: 500.

7. Modelling language origins and evolution IJCAI-05 Demo II Studying the effect of perceptual noise (pNoise). Each agent sense the objects’ features with added noise: Each feature f i becomes f i ’ = g(,x)  f i –g(,x) = 2-G(,x)) if x<0 G(,x) otherwise. –x is a random value between -0.5 and +0.5. –G(,x) is Gaussian with standard deviation  around the mean 0 – = pNoise Varying pNoise with same settings as in demo I shows that system robust to noise, if noise is not too strong.

8. Modelling language origins and evolution IJCAI-05 Demo III Settings as in Demo I, but instead of fixed set of colours, the colours are generated as random Red, Green and Blue values. (Unselecting fCol) Unstructured is more difficult to learn.

9. Modelling language origins and evolution IJCAI-05 Demo IV Settings as in Demo I. Adding features Shape (A), X and Y one by one. Shows that system is robust under increasing complexity of meanings, though learning takes longer.

10. Modelling language origins and evolution IJCAI-05 Demo V Back to settings in Demo I. Increasing population sizes and nr. of language games. Again language converges, though learning takes longer.

11. Modelling language origins and evolution IJCAI-05 Demo VI Settings as in Demo I. Varying game type, comparing –Guessing game (based on corrective feedback) –Observational game (based on joint attention) –Selfish game (based on cross-situational statistical learning – guessing, but no corrective feedback) Default update rule for association score sij between meaning m i and word w j : –s ij = s ij + (1-) X ij Where  is a learning rate and X ij =1 if used successful, and X ij =0 if used unsuccessful. Selfish game works ‘only’ on Bayesian statistics, i.e. –s ij = P(m i |w j ) = use(m i & w i )/use(w i ) Where P(m|w) is the conditional probability that m is observed when w occurs, and use(x) is the number of times x is used.