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Simulating the Usage Acquisition of Two-Word Sentences with a First- or Second-Person Subject and Verb

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Presentation at BICA 2017
http://bica2017.bicasociety.org
Paper: http://bica2017.bicasociety.org/wp-content/uploads/2017/08/BICA_2017_paper_4.pdf

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Simulating the Usage Acquisition of Two-Word Sentences with a First- or Second-Person Subject and Verb

  1. 1. 2017-08 Dwango AI Lab. 02017-08 Dwango AI Lab. Simulating the Usage Acquisition of Two-Word Sentences with a First- or Second-Person Subject and Verb ARAKAWA, Naoya Dwango AI Laboratory 2017-08-04 BICA 2017
  2. 2. 2017-08 Dwango AI Lab. 1 Outline 1. Introduction 2. The Experiment 3. Discussion 4. Conclusion
  3. 3. 2017-08 Dwango AI Lab. 2 Introduction The paper shows: A simplistic simulated agent can learn the use of ‘I’ & ‘you’ in two-word (subject-verb) sentences interacting with a caretaker agent, while babbling, observing utterances & behavior, and obtaining rewards.
  4. 4. 2017-08 Dwango AI Lab. 3 Background • Previous Works Learning 1st & 2nd person pronouns observing more than one caretakers’ language use E.g., Oshima-Takane+, Gold & Scasselati • Question: Can one learn them from a single caretaker? • Answer: Yes (from this experiment)
  5. 5. 2017-08 Dwango AI Lab. 4 The Experiment 1. The World 2. The Language 3. The Caretaker 4. The Learner 5. Results Luca gira. An Image…
  6. 6. 2017-08 Dwango AI Lab. 5 The World of the Experiment Two rambling agents –A Caretaker Uses the language of the experiment –A Language Learner Learns the language Each knows its & the other’s utterance/action given in symbolic forms. (No symbol grounding issue involved here.) Three kinds of action: {come, go, turn}
  7. 7. 2017-08 Dwango AI Lab. 6 The Language • Two-word Sentences: Subject+Verb • Subject: {I, You, Luca, Mario} – Luca: Language Learner – Mario: Caretaker • Verb: {come, go, turn} • A sentence is used: – To describe • Utterer’s own action • The other’s action – To ‘give instruction’ to the other.
  8. 8. 2017-08 Dwango AI Lab. 7 The Caretaker • Executes action {come, go, turn} randomly • Describes its action in 2-word sentences • Or, instructs the learner to act {come, go, turn} with a 2-word sentence • Rewards the Learner when: – Learner describes its own or caretaker’s action correctly. – Learner acts following instruction.
  9. 9. 2017-08 Dwango AI Lab. 8 Language Learner Three Modes: Reaction Mode / Spontaneous Action Mode / Direction Mode Has Caretaker acted or uttered? Reaction Mode Acts & Utters Reaction Mode Utters Random Spontaneous Action Mode: Acts & Utters. Direction Mode: Utters. based on ‘internal representation’ of Caretaker’s action No Only Uttered Acted
  10. 10. 2017-08 Dwango AI Lab. 9 Learner’s Utterance/Action • Produced with information: – Mode – Its own action (in spontaneous action mode) – Caretaker’s action/utterance (in reaction mode) • Choice of Subjects, Verbs & Actions – Reinforced by Rewards • Given by Caretaker • Internal Reward: when Caretaker follows direction – Random choice: Babbling • Naïve Bayes + Dirichlet Dist. (dice throwing based on reward average)
  11. 11. 2017-08 Dwango AI Lab. 10 Results • 2,500 interactions between Caretaker & Learner • Success rate = reward rate • After 1,200 interactions, Learner learned to utter & act at a 90% rate of correctness.
  12. 12. 2017-08 Dwango AI Lab. 11 Success rate of Subject Selection The success rate of the reaction mode was better since it had more choices than the other modes. S react S sp. act. S direction
  13. 13. 2017-08 Dwango AI Lab. 12 Success rate of Action Selection
  14. 14. 2017-08 Dwango AI Lab. 13 Example Interaction LL: Language Learner (Luca), CT: Caretaker (Mario) Utt. Utterance, Rew.: Reward The language for utterances is Interlingua (ia).
  15. 15. 2017-08 Dwango AI Lab. 14 Discussion & Conclusion 1. The World 2. The Language 3. Learning Conclusion
  16. 16. 2017-08 Dwango AI Lab. 15 Discussion – The Result The experiment showed: • One can learn 1st & 2nd person pronouns from a single caretaker. • Playing a minimal language game • Without grounded concept: object, other, etc…
  17. 17. 2017-08 Dwango AI Lab. 16 Discussion – The Language • Semantics – Programmed in Caretaker’s Language Use • Human Language Acquisition – Learners are only presented examples in interactions with Caretakers • Two-word Sentences – cf. 1 or 2 word sentence period in infants’ language acquisition. (not always subject-verb, though)
  18. 18. 2017-08 Dwango AI Lab. 17 Discussion – Learning • Approval as Reward – In human learning: Smiling, etc. • Internal Reward – When Caretaker follows Learner’s direction ⇔ Goal Achieved • Babbling (random choice) was necessary & reinforced. • Modes {reaction, spontaneous action, and direction} could be learned – But not in the scope of the current experiment
  19. 19. 2017-08 Dwango AI Lab. 18 Conclusion • Related Research – Language Emergence with Artificial Agents • Steels, Vogt, Sugita, et al. • The current experiment is rather learning existing language. • Further directions – More realistic experiments would require reference to actual human language acquisition. – Symbol grounding problem – Learning language models • E.g., LSTM • Language use as System of choices cf. Functional Grammar (MAK Halliday)
  20. 20. 2017-08 Dwango AI Lab. 19 EOP Thank you very much for your attention!

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