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Deictic Pronoun Learning and Mirror Self- Identification

Deictic Pronoun Learning and Mirror Self- Identification. A paper by Kevin Gold and Brian Scassellati. Presented by Paul Dilley. Paper’s focus. Two milestones in human reasoning about the self: Mirror self-identification Using deictic pronouns such as “I” and “You”

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Deictic Pronoun Learning and Mirror Self- Identification

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  1. Deictic Pronoun Learning and Mirror Self-Identification A paper by Kevin Gold and Brian Scassellati Presented by Paul Dilley

  2. Paper’s focus Two milestones in human reasoning about the self: • Mirror self-identification • Using deictic pronouns such as “I” and “You” Building a robot to do these – in order to answer questions about the two milestones

  3. Mirror self-recognition Pigs • Few animals recognise themselves in the mirror • Social intelligence needed to do so – assumption Gorillas Chimpanzees Orangutans Bonbos Humans Orcas Elephants Dolphins Magpies Barn owls

  4. How is the mirror test done? Fin! Mark placed on being secretly Requires “Mirror Test.mp4” to be in the same directory: Mirror placed in front of being Or watch the embedded movie in a media player: It moves limbs to the mark? Click here if the movie does not play

  5. The mirror test and “I” Problems: • Does the mirror test = Social intelligence? • How different is the intelligence of using “I” vs. touching a mark, during a mirror test? “I” and “You” are known as deictic pronouns “Dik-tik”

  6. Build a robot? The paper author’s built a robot that can perform self mirror recognition and apply deictic pronouns to the reflection • Help answer questions on previous slide about human intelligence • Provide an insight for intelligent robot building

  7. How it works Audio is captured using a dual-channel microphone Robot uses cameras to capture images Speech recognition Sound localisation Face recognition Motion Detection Colour Detection Word learning Self Recognition Module Finds motion that coincides with motor control commands

  8. How it works Word learning Property Types Actions – “speaking” Being the target of actions – “addressee” 1. No grammatical parsing, sentences are treated as simple collections of words 2. Each word learnt with confidence adds an associated property to a list of properties to look for in the environment 3. Each agent is then matched The process is done using chi-squares Identify already understood words Match agents with the properties of understood words Eventual learning of “I” and “you”

  9. Self Identification • Exploratory arm movements + studying the time delay for motion detection • These experimental values are put onto a distribution with 95% falling between 0.5 to 1 seconds • Future movements in this time frame had the moving object labelled as “self”

  10. Who has the ball? • The robot goes into speaker mode and prepares to address the questioner • Nico searches for utterances of learnt words, e.g. “got” • This is linked to an associated action “hasBall” • Nico then searches for a property that is unassociated with “hasBall” but true for the associated agent

  11. Implementation The robot learnt words-property pairs observing two people play a game of catch with a dataset of 50 utterances “You” became associated with the addressee and “I” with the speaker

  12. Implementation Nico was placed in front of a mirror, with the ball either at his base or being held by the experimenter Nico correctly answered “I got the ball” 16 out f 20 times. The incorrect answer “You got...” was given twice, and the robot misunderstood twice

  13. Implementation • Nico got confused when the experimenter was holding the ball • He moved within Nico’s arm test confusing the bot • “You got the ball” was only correct 11/20 times • When the bot was told to stop fidgeting the result was much better: 18/20 times correct

  14. Conclusions It’s use of “I” for a mirror image is an exciting step for robotics Bot learns “I” and “you” from observation and applies them correctly The authors believe: Applying words with functions and roles is more useful than with superficial appearance The authors believe: Allowing robots to map human actions and properties onto robotic states in general would be the next step for research Importantly, the bot can associate words with the actions in the environment – rather than just visual properties It does not represent “self-awareness” from a scientific point of view – but is useful for understanding behaviours

  15. Recognising in the mirror • Most crucially: Nico could recognise its own feedback as self-generated – but a pre-programmed kinetic model, when the robot knows where its arm is, would probably suffice. It would require less training • The authors conclude: • The mirror test might be a better test of recognising feedback • It’s not that useful: “why waste neurons on it” – but it has benefits to humans, but may not be as significant to intelligence as previously thought • But asking a robot “Who is that in the mirror?” and answers such as “I” do touch on several different aspects of human intelligence What do I think about the paper?

  16. Most great apes can pass the mirror test by adult hood Thanks for listening!

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