Demo THSim

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

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

3. 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 Modelling language origins and evolution IJCAI-05

4. Simplified example CONTEXT: A=(0.1, 0.3) B=(0.3, 0.3) C=(0.25, 0.15) A B b a C 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 Modelling language origins and evolution IJCAI-05

5. 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. Modelling language origins and evolution IJCAI-05

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

7. Demo II • Studying the effect of perceptual noise (pNoise). Each agent sense the objects’ features with added noise: • Each feature fi becomes fi’ = g(,x)  fi • 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. Modelling language origins and evolution IJCAI-05

8. 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. Modelling language origins and evolution IJCAI-05

9. 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. Modelling language origins and evolution IJCAI-05

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

11. 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 mi and word wj: • sij = sij + (1-) Xij • Where  is a learning rate and Xij=1 if used successful, and Xij=0 if used unsuccessful. • Selfish game works ‘only’ on Bayesian statistics, i.e. • sij = P(mi|wj) = use(mi & wi)/use(wi) • 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. Modelling language origins and evolution IJCAI-05