Artificial Intelligence, Cognition and  Interaction Kurs: Språk, kommunikation och tänkande Program: Kognitionsvetenskap [email_address]
Topics AI - artificial intelligence Cognition Language Dialogue and Interaction AEI - artificial emotional intelligence: Emotive agents ACI - artificial communicative intelligence Non-verbal interactive behavior
Human Brain
Virtual Task Areas Cognition and emotion  (ICT/ISI Emotion)   Speech recognition  (IMSC-NL) Integration and Scenario Development (SASO-ST) Human embodiment  (ISI-Vhuman)  Dialogue management  (ICT-NL)  Speech understanding  and generation (ISI-NL)
AI-DEMO
Kognition Uppmärksamhet - semiotisk organisation av info, färg, proximitet, sekvenser, ordning Perception Minne Recall Recognition scan Läsa, skriva, tala Planera, problemlösning, tänkande, beslutsfattande Inlärning - dynalinking Känslor
Precortex Används för mål strukturering Koordinering av mål och planer Förutseende
Inlärning Instruktion Immitation - analogi, metafor, exempel Upptäckt, sökning Längre inlärningstid - stabil inlärning Kort inlärningstid - kan glömmas lätt
Mänskliga kriterier för val av problemlösningstillgång Backup avoidance - undvika operatörer som förstör effekten av gammal operatör Difference reduction - söka kortaste lösning Mean-ends analysis - skapa nytt mål för att kunna tillämpa en operatör, Tower of Hanoi
Mänskliga problemlösnings effekter Funktionell fixering Einstellingseffekten, mekaniskt tänkande Inkubationseffekten - glöm dåliga lösningar Aha effekten - inte medvetna om lösningens närhet
Slutledning och beslutstagande Logik =/ mänskligt tänkade Deduktion Induktion Abduktion (tolking av probabilitet)
Teorier och modeller om  känslor Darwinism, biologi, fysiologi (William) Jamesianism, psykologi, neurologi Kognitivism Social konstruktivism, antropologi, sociologi
James, Damasio  psykologi, neurologi Upplevelser av känslor Kroppens tillstånd påverkar eller orsakar känslan som påverkar tänkandet
Damasio:   Descartes' Error Phineas Gage: - impaired ability to feel emotion  - intelligence remained intact after the accident - severely handicapped ability to take rational  decisions Damasio: - emotions could no longer be engaged in the decision process - rationality stems from our emotions - our emotions stem from our bodily senses - state of mind is identical to state of feeling, which is a reflection of state of body
Limbic system
Affective Computing Emotionellresponsteknologi Mål Att motivera användning, försäljning, inlärning, lek, kreativitet, handling, verksmahet, förtröende
Emotive Agents/Känsloteknologi Frambringa känslor Uttrycka känslor Virtuella agenter, Pets-Robotar uttrycker känslor Användare: Emoticones, emoljud Hantera frustrerade användare Antropomorfism Trovärdighet
ACI Design of virtual agents’ behavior:  competence visualization 2.  Choice of features 3.  Definition of parameters’ values 4.  Evaluation of cultural salience to users
Zero Hypothesis Culture-specific behavior is not detectable via virtual simulations.
Modifiable Computational Model Competence Probabilistic Cognitive Models Almost all decision involves some chance Visualization (ready made) Body Movements  Body Positions
Simulation Setting Outdoor standing groups of 6 men: Mexican Spanish speakers American English speakers Arabic Levantine speakers
Simulated Common Behavior Features Turn-taking (Padilla & Carletta’02) Dynamic turn-taking (Jan & Traum’05) Movement forces (Jan & Traum’07) Gaze  (random and probabilistic: listener, addressee, speaker)
talkativeness:  likelihood of wanting to talk. transparency:  likelihood of producing explicit positive and negative feedback, and turn-claiming signals, even gestures. confidence:  likelihood of interrupting and continuing to speak during simultaneous talk. interactivity:  the mean length of turn segments between TRPs. verbosity:  likelihood of continuing the turn after a TRP at which no one is self-selected. Personality Parameters Random choices related to defined parameters
Each reason for movement has a force associated to it (Jan & Traum’07) : F speaker  : attractive force towards speaker F noise  : repelling force from outside noise F proximity  : repelling force from characters that are too close F convex  : force towards convex hull of all conversation participants Movement Controlling Forces
Simulated Modified Behavior Features Proxemics Timing between turns Gaze
Literature on Proxemics  (eg.Shuter’76)   83cm 79 cm 75 cm 60 cm 56 cm 51 cm 83 cm 71 cm 75 cm 65 cm 50 cm 61 cm M-M M-F F-F M-M M-F F-F Anglo Anglo Anglo Mexican Mexican Mexican Outdoor Adults Indoor Adults Gender Ethnic Groups
Simulated Proxemics (Hall’68)  > 2.7 1.2-2.7 0.45-1.2 < 0.45  American English > 1.5 0.7-1.5 0.45-0.7 < 0.45 Arabic Levantine > 2.0 1.0-2.0 < 0.45-1.0 < 0.45 Mexican Spanish Public (m) Social  (m) Personal (m) Intimate (m) Proxemic   zone/ Ethnic group
Amount of Gaze:  Triads and Dyads  (Exline’60, Argyle & Ingham’72,’76) 65.7 77.9 47.9 37.9 56.1 73.8 31.1 23.4 37.3 42.4 36.9 7.5 23.2 29.8 25.6 3.0 Mean gaze While listening While talking Mutual gaze Female dyads Male dyads Female triads Male triads Gender/ Gaze (%)
Simulated Gaze Arabic Levantine American English Mexican Spanish Ethnic group 80% 60 % 80% Chance to gaze at speaker
Proxemics Input to movement algorithm Force toward/away from agents Interacts with other factors such as audibility and group formation to direct agent movement < Proxemics > < IntimateZone >0.45</ IntimateZone > < PersonalZone >1.2</ PersonalZone > < SocialZone >2.7</ SocialZone > </ Proxemics >
Gaze Replace random gaze of original algorithm with gaze based on cultural parameters < Gaze > < GazingAtMeFactor >1.5</ GazingAtMeFactor > < Speaker > < Attending > < Addressee >6.0</ Addressee > < Random >2.0</ Random > < Away >2.0</ Away > </ Attending > < NonAttending > < Addressee >1.0</ Addressee > < Random >8.0</ Random > < Away >1.0</ Away > </ NonAttending > < Away > < Random >9.0</ Random > < Away >1.0</ Away > </ Away > </ Speaker > < Addressee > < Speaker >8.0</ Speaker > < Random >1.0</ Random > < Away >1.0</ Away > </ Addressee > < Listener > < Speaker >6.0</ Speaker > < Addressee >2.0</ Addressee > < Random >1.0</ Random > < Away >1.0</ Away > </ Listener > </ Gaze >
Pause and Overlap Time between speech end and speech start of new speaker in case where the new speaker is taking the turn Replace uniform distribution with distribution based on cultural parameters < Silence > < StartOffset >0.0</ StartOffset > < StartVariation >0.5</ StartVariation > </ Silence >
Anglo-American 1
Mexican 1
Arabic 1
Evaluation Subjects evaluate realism in  6 movies, 2 min each 20 American speakers 20 Mexicans speakers 12 Arabic speakers
Quantitative Results The t-test shows no significant difference in cross-cultural evaluation of gaze and turn-taking There are differences in evaluation of proxemics: Arabic subjects found Arabic proxemics and animation realistic Mexican and Ango-American subjects found no significant cultural differences according to proxemics and overall animation Zero hypothesis is violated:  Subtle culture-specific behavior is detectable via virtual simulations.
Proxemics Ratings Mexicans rate American video 1 as most realistic animation Mexicans rate American 2  as least realistic Americans thought Mexican 1 is most realistic and Mexican 2 - least realistic Americans don't see differences in cultural proxemics Arabic speakers rate Arab 1 as most realistic , Arab 2 -least realistic
Promising Integration Cultural awareness can be studied by virtual simulations of interactive behavior Simulations trigger research on generalizable parameters and values related to proxemics, gaze and turn-taking AEI - triggers development of cognitive-emotion theories  ACI - triggers development ethics theories of communication

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Sample PPT for testing

  • 1. Artificial Intelligence, Cognition and Interaction Kurs: Språk, kommunikation och tänkande Program: Kognitionsvetenskap [email_address]
  • 2. Topics AI - artificial intelligence Cognition Language Dialogue and Interaction AEI - artificial emotional intelligence: Emotive agents ACI - artificial communicative intelligence Non-verbal interactive behavior
  • 4. Virtual Task Areas Cognition and emotion (ICT/ISI Emotion) Speech recognition (IMSC-NL) Integration and Scenario Development (SASO-ST) Human embodiment (ISI-Vhuman) Dialogue management (ICT-NL) Speech understanding and generation (ISI-NL)
  • 6. Kognition Uppmärksamhet - semiotisk organisation av info, färg, proximitet, sekvenser, ordning Perception Minne Recall Recognition scan Läsa, skriva, tala Planera, problemlösning, tänkande, beslutsfattande Inlärning - dynalinking Känslor
  • 7. Precortex Används för mål strukturering Koordinering av mål och planer Förutseende
  • 8. Inlärning Instruktion Immitation - analogi, metafor, exempel Upptäckt, sökning Längre inlärningstid - stabil inlärning Kort inlärningstid - kan glömmas lätt
  • 9. Mänskliga kriterier för val av problemlösningstillgång Backup avoidance - undvika operatörer som förstör effekten av gammal operatör Difference reduction - söka kortaste lösning Mean-ends analysis - skapa nytt mål för att kunna tillämpa en operatör, Tower of Hanoi
  • 10. Mänskliga problemlösnings effekter Funktionell fixering Einstellingseffekten, mekaniskt tänkande Inkubationseffekten - glöm dåliga lösningar Aha effekten - inte medvetna om lösningens närhet
  • 11. Slutledning och beslutstagande Logik =/ mänskligt tänkade Deduktion Induktion Abduktion (tolking av probabilitet)
  • 12. Teorier och modeller om känslor Darwinism, biologi, fysiologi (William) Jamesianism, psykologi, neurologi Kognitivism Social konstruktivism, antropologi, sociologi
  • 13. James, Damasio psykologi, neurologi Upplevelser av känslor Kroppens tillstånd påverkar eller orsakar känslan som påverkar tänkandet
  • 14. Damasio: Descartes' Error Phineas Gage: - impaired ability to feel emotion - intelligence remained intact after the accident - severely handicapped ability to take rational decisions Damasio: - emotions could no longer be engaged in the decision process - rationality stems from our emotions - our emotions stem from our bodily senses - state of mind is identical to state of feeling, which is a reflection of state of body
  • 16. Affective Computing Emotionellresponsteknologi Mål Att motivera användning, försäljning, inlärning, lek, kreativitet, handling, verksmahet, förtröende
  • 17. Emotive Agents/Känsloteknologi Frambringa känslor Uttrycka känslor Virtuella agenter, Pets-Robotar uttrycker känslor Användare: Emoticones, emoljud Hantera frustrerade användare Antropomorfism Trovärdighet
  • 18. ACI Design of virtual agents’ behavior: competence visualization 2. Choice of features 3. Definition of parameters’ values 4. Evaluation of cultural salience to users
  • 19. Zero Hypothesis Culture-specific behavior is not detectable via virtual simulations.
  • 20. Modifiable Computational Model Competence Probabilistic Cognitive Models Almost all decision involves some chance Visualization (ready made) Body Movements Body Positions
  • 21. Simulation Setting Outdoor standing groups of 6 men: Mexican Spanish speakers American English speakers Arabic Levantine speakers
  • 22. Simulated Common Behavior Features Turn-taking (Padilla & Carletta’02) Dynamic turn-taking (Jan & Traum’05) Movement forces (Jan & Traum’07) Gaze (random and probabilistic: listener, addressee, speaker)
  • 23. talkativeness: likelihood of wanting to talk. transparency: likelihood of producing explicit positive and negative feedback, and turn-claiming signals, even gestures. confidence: likelihood of interrupting and continuing to speak during simultaneous talk. interactivity: the mean length of turn segments between TRPs. verbosity: likelihood of continuing the turn after a TRP at which no one is self-selected. Personality Parameters Random choices related to defined parameters
  • 24. Each reason for movement has a force associated to it (Jan & Traum’07) : F speaker : attractive force towards speaker F noise : repelling force from outside noise F proximity : repelling force from characters that are too close F convex : force towards convex hull of all conversation participants Movement Controlling Forces
  • 25. Simulated Modified Behavior Features Proxemics Timing between turns Gaze
  • 26. Literature on Proxemics (eg.Shuter’76) 83cm 79 cm 75 cm 60 cm 56 cm 51 cm 83 cm 71 cm 75 cm 65 cm 50 cm 61 cm M-M M-F F-F M-M M-F F-F Anglo Anglo Anglo Mexican Mexican Mexican Outdoor Adults Indoor Adults Gender Ethnic Groups
  • 27. Simulated Proxemics (Hall’68) > 2.7 1.2-2.7 0.45-1.2 < 0.45 American English > 1.5 0.7-1.5 0.45-0.7 < 0.45 Arabic Levantine > 2.0 1.0-2.0 < 0.45-1.0 < 0.45 Mexican Spanish Public (m) Social (m) Personal (m) Intimate (m) Proxemic zone/ Ethnic group
  • 28. Amount of Gaze: Triads and Dyads (Exline’60, Argyle & Ingham’72,’76) 65.7 77.9 47.9 37.9 56.1 73.8 31.1 23.4 37.3 42.4 36.9 7.5 23.2 29.8 25.6 3.0 Mean gaze While listening While talking Mutual gaze Female dyads Male dyads Female triads Male triads Gender/ Gaze (%)
  • 29. Simulated Gaze Arabic Levantine American English Mexican Spanish Ethnic group 80% 60 % 80% Chance to gaze at speaker
  • 30. Proxemics Input to movement algorithm Force toward/away from agents Interacts with other factors such as audibility and group formation to direct agent movement < Proxemics > < IntimateZone >0.45</ IntimateZone > < PersonalZone >1.2</ PersonalZone > < SocialZone >2.7</ SocialZone > </ Proxemics >
  • 31. Gaze Replace random gaze of original algorithm with gaze based on cultural parameters < Gaze > < GazingAtMeFactor >1.5</ GazingAtMeFactor > < Speaker > < Attending > < Addressee >6.0</ Addressee > < Random >2.0</ Random > < Away >2.0</ Away > </ Attending > < NonAttending > < Addressee >1.0</ Addressee > < Random >8.0</ Random > < Away >1.0</ Away > </ NonAttending > < Away > < Random >9.0</ Random > < Away >1.0</ Away > </ Away > </ Speaker > < Addressee > < Speaker >8.0</ Speaker > < Random >1.0</ Random > < Away >1.0</ Away > </ Addressee > < Listener > < Speaker >6.0</ Speaker > < Addressee >2.0</ Addressee > < Random >1.0</ Random > < Away >1.0</ Away > </ Listener > </ Gaze >
  • 32. Pause and Overlap Time between speech end and speech start of new speaker in case where the new speaker is taking the turn Replace uniform distribution with distribution based on cultural parameters < Silence > < StartOffset >0.0</ StartOffset > < StartVariation >0.5</ StartVariation > </ Silence >
  • 36. Evaluation Subjects evaluate realism in 6 movies, 2 min each 20 American speakers 20 Mexicans speakers 12 Arabic speakers
  • 37. Quantitative Results The t-test shows no significant difference in cross-cultural evaluation of gaze and turn-taking There are differences in evaluation of proxemics: Arabic subjects found Arabic proxemics and animation realistic Mexican and Ango-American subjects found no significant cultural differences according to proxemics and overall animation Zero hypothesis is violated: Subtle culture-specific behavior is detectable via virtual simulations.
  • 38. Proxemics Ratings Mexicans rate American video 1 as most realistic animation Mexicans rate American 2 as least realistic Americans thought Mexican 1 is most realistic and Mexican 2 - least realistic Americans don't see differences in cultural proxemics Arabic speakers rate Arab 1 as most realistic , Arab 2 -least realistic
  • 39. Promising Integration Cultural awareness can be studied by virtual simulations of interactive behavior Simulations trigger research on generalizable parameters and values related to proxemics, gaze and turn-taking AEI - triggers development of cognitive-emotion theories ACI - triggers development ethics theories of communication