Modeling the Dynamics of Gaze-Contingent Social Behaviors in Human-Agent Interaction

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1. Modeling the Dynamics of Gaze- Contingent Social Behaviors in Human-Agent Interaction University of Augsburg, Germany Human Centered Multimedia Elisabeth André 2. 2…
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  • 1. Modeling the Dynamics of Gaze- Contingent Social Behaviors in Human-Agent Interaction University of Augsburg, Germany Human Centered Multimedia Elisabeth André
  • 2. 2 My Background  Social Robotics and Virtual Agents  European and BMBF Projects on Affective Computing
  • 3. 3 Motivation ? http://www.kreisbote.de/http://ais.badische-zeitung.de/
  • 4. 4 Explicit versus Implicit Interaction with Eye Gaze  Explicit Interaction:  Open interaction with a system where humans intentionally input discrete commands to explicitly express their needs  Implicit Interaction:  Information that people convey indirectly in a conversation, but which may be derived from dialogue and context information.  Unconscious Interaction:  Continuous (often nonverbal) behavior people not voluntarily control, but which may be (but are not necessarily expected to be) interpreted as the implicit expression of a particular need or intention http://www.vision-systems.com/
  • 5. 5 Eye Gaze to Initiate Contact with a Human User Breaking the Ice in Human-Agent Communication: Eye-Gaze Based Initiation of Contact with an Embodied Conversational Agent Tober et al. IVA 2009
  • 6. 6 Five Phases of Flirting [Givens, 1978]  Attention Phase  Men and women arise each other’s attention  Ambivalent non-verbal behavior  Recognition Phase  One interactant recognizes the interest of the other  He or she may then signal readiness to continue the interaction, e.g., by a friendly smile.  Interaction Phase  After mutual interest has been established, the man or woman may be initiated the interaction phase and engage in a conversation  Sexual-Arousal and Resolution Phases are somehow missing relevance to human-agent communication.
  • 7. 7 Attention and Recognition Phase 7
  • 8. 8 Interaction Modes  Interactive version  Non-interactive version with ideal flirt behavior:  In the non-interactive ideal version the virtual agent behaves like in the interactive version except for that it does not respond to the user’s eye gaze behavior, but assumes a perfect eye gaze behavior from the user and thus follows a fixed sequence.  Non-interactive version with anti-flirt behavior:  Duration of mutual gaze is increased from 3 s to 7 s  Facial expression remains neutral (which can be interpreted as a bored attitude towards the user)  Virtual agent looks away upwards after gazing at the user instead of downwards
  • 9. 9 Results 1. In the interactive and the ideal mode, the agent was able to show the users that Alfred had an interest in them and the users also had the feeling that he was flirting with them. 2. We found that the effect was increased when moving from the ideal to the interactive mode. 3. The interactive version contributed to the user’s enjoyment, increased their interest to continue the interaction or even to engage in a conversation with Alfred.
  • 10. 10 Conclusions  Alfred was lacking of attractiveness, but the eye gaze enabled agent improved the flirting interaction.  Flirting tactics as implemented in this work are of benefit to a much broader range of situations with agents than just dating, e.g. initiate human-agent interaction or regulating turn-taking in dialogues.
  • 11. 11 Setting Discovering eye gaze behavior during human-agent conversation an interactive storytelling application. ICMI-MLMI 2010.
  • 12. 12 Gaze Model  Parameters set on the basis of data found in the literature Non-interactive Interactive Looks around 4.0 s (2-6 s) 4.0 s (2-6 s) Gazes at user (Wait for gaze) 2.0 s (1-3 s) 2.0 s (1-3 s) Mutual gaze n/a 1.0 s (0.75-1.25 s)
  • 13. 13 Evaluation  Compared the 2 different gaze behavior models:  non-interactive vs. interactive  Study with 19 subjects  How do people respond to different gaze models?  Does the gaze model affect their sense of social presence?  Order of the 2 gaze models was randomized for each subject to avoid any bias due to ordering effects
  • 14. 14 Synchronized Data Open Source Framework for Social Signal Processing: http://www.openssi.net
  • 15. Starer vs. Non-Starers starers non-starers 15
  • 16. 16 Results  Users looked more at Emma while she was speaking than when the users started to speak
  • 17. 17 Results  In total users were much more looking at Emma compared to human-human interaction Argyle & Cook Kendon Our Study Looking at interlocutor 58% 50% (28% - 70%) 76% (46% - 98%) Looking at interlocutor while listening 75% 81% Looking at interlocutor while speaking 41% 71%
  • 18. 18 Conclusions  Interactive gaze mode led to a better user experience compared to the non-interactive gaze mode  Users adhere to patterns of gaze behaviors for speaker and addressee that are also characteristic of dyadic human-human interactions  They looked more often to the virtual interlocutor than is typical of human-human interactions.
  • 19. 19 Social Robots
  • 20. 20 Empathetic Artificial Listener  Attention: pay attention to the signals produced by a speaker  Perception of signals  Comprehension: understand meaning attached to signals  Internal reaction: the comprehension of the meaning may create cognitive and emotional reaction  Decision: communication or not of the internal reaction  Generation: display behaviors
  • 21. 21 Generation of Facial Expressions  FACS (Facial Action Coding System) can be used to generate and recognize facial expressions.  Action Units are used to describe emotional expressions.  Seven Action Units were identified for the robotic face (out of 40 Action Units for the human face)  Upper face:  inner brows raiser (AU 1),  brown lowerer (AU 4),  upper lid raiser (AU 5)  and eye closure (AU 43).  Lower face:  lip corner puller (AU 12),  lip corner depressor (AU 15)  and lip opening (AU 25).
  • 22. 22 Social Signal Interpretation: SSI by Augsburg University Multiple Sensor Input ECG, Skin Conduction, Blood Glucose Level, Speech, Acceleration, … Preprocessing and Feature Analysis Filtering, Frequency Analysis, … Pattern Recognition Fusion and Final Decision Physiological and Affective State, Context Information SSI is freely available under: http://www.openssi.net Johannes Wagner, Florian Lingenfelser, Tobias Baur, Ionut Damian, Felix Kistler, Elisabeth André: The social signal interpretation (SSI) framework: multimodal signal processing and recognition in real-time. ACM Multimedia 2013: 831-834
  • 23. 23 Generation of Facial Expressions
  • 24. 24 Sensitive Artificial Listener
  • 25. 25 Ideomotorische Empathie  Mirroring of Emotions
  • 26. 26 Affective Empathy  Emotional Reaction to user emotions That is not good!
  • 27. 27 Functional Empathy  Show concern about forgotten medications to increase problem awareness Oh dear!
  • 28. 28 Functional Empathy  Intentional smile to calm down user Please think of it tonight!
  • 29. 29 Multimodal Dialogue with a Robot G. Mehlmann, M. Häring, K. Janowski, T. Baur, P. Gebhard, E. André: Exploring a Model of Gaze for Grounding in Multi- modal HRI. ICMI 2014: 247-254
  • 30. 30 Research Strategy Model of Human Social Behaviors Multimodal Behavior Simulation Corpus on Human Social Behaviors Build Simulate Evaluate Refine Statistics
  • 31. 31 Human-Human Interaction
  • 32. 32 Human-Human Interaction
  • 33. 33 Gaze Recognition  The glasses provide the video image and the gaze coordinates -, - -, - ... 156, 543 189, 527 145, 567 211, 542
  • 34. 34 Gaze-Based Disambiguation 3 1 42 Do you mean this red object there? „Do you mean this red object there?“ 1 1 1 2 2 3 3 3 2 1 1 1 2 3Gaze: Speech: Use this information for disambiguation! Gaze
  • 35. 35 Gaze-Based Disambiguation 0:life 42700:time 0.8:conf circle:shape red:color 3:name :data gaze:mode event:type 3 ... ... [ ]: 3164:life 39021:time redcolor:data prop_quest:fun nginfo_seeki:cat dialog_act:type :data speech:mode event:type 1,3,4 0:life 40100:time 0.6:conf square:shape red:color 1:name :data gaze:mode event:type 1 0:life 40700:time 0.7:conf square:shape green:color 2:name :data gaze:mode event:type 2 „Do you mean this red object there?“ 1 1 1 2 2 3 3 3 2 1 1 1 2 3Gaze: Speech:
  • 36. 36 Robot Behavior  The robot‘s behavior depends on the role. In the speaker role, the robot awaits the dialog manager‘s decision to play a behavior. In the addressee role, the robot shows some idle gaze behavior, occasionally reacting to the users‘ gaze movements, emotional expressions and other cues.
  • 37. 37 Gaze-based Interaction  Object grounding:  The robot follows the user’s hand movements.  The robot follows the user’s gaze.  Social grounding:  The robot seeks and recognizes mutual gaze.  Turn management:  The robot recognizes when the user yields the turn.
  • 38. 38 Gaze-based Interaction
  • 39. 39 Results of a Study  Object grounding was more effective than social grounding.  People were able to interact more efficiently with object grounding.  Social grounding did not improve the perception of the interaction.  Assumption:  People were rather concentrating on the task instead of the social interaction with the robot.
  • 40. 40 Gaze-Aware Robot
  • 41. 41 Bayerischer Forschungsverbund: Gender-specific Attitudes towards Robots in Elderly Care
  • 42. 42 Conclusions  Effect of gaze-aware agents:  Gaze-aware agents have a positive effect on user perception.  Gaze-aware agents improve grounding.  Side effects:  Midas Touch Problem: • The agent should not respond to each detected gaze behavior.  Unnatural user behavior: • Use of gaze as a pointing device  Timing is the key.
  • 43. 43 Thanks you very much!
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