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-State of the Living – Medical Games & Lifelike Patients - Thomas Talbot, USC Institute for Creative Technologies

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-State of the Living – Medical Games & Lifelike Patients - Thomas Talbot, USC Institute for Creative Technologies

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This seminar discussed tricks of the trade and methods that make patient characters that appear to be living. We will cover methods to achieve biological fidelity, interactivity, graphics and flow with the goal to introduce participants to techniques that deliver the appearance of active biology, a sense of urgency and responsiveness to game choices.

This seminar discussed tricks of the trade and methods that make patient characters that appear to be living. We will cover methods to achieve biological fidelity, interactivity, graphics and flow with the goal to introduce participants to techniques that deliver the appearance of active biology, a sense of urgency and responsiveness to game choices.

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-State of the Living – Medical Games & Lifelike Patients - Thomas Talbot, USC Institute for Creative Technologies

  1. 1. VIRTUAL PATIENTS Important aspects for lifelike characters: Spoken Dialogue Thomas B. Talbot, MD, MS, FAAP Principal Medical Expert Institute for Creative Technologies University of Southern California Talbot@ict.usc.edu Associate Research Professor of Medical Education Keck School of Medicine of USC Serious Play Conf July 10th, 2018
  2. 2. LifeLike Virtual Patients • Graphical Appearance • Scenario Design & Narrative • Interactivity – User Interface – Scenario Logic – Biological Fidelity – Speech & Dialogue • Assessment & Feedback
  3. 3. AGENDA • Focus on interactive dialogue in games • Conversational Simulations • Dialogue agents and conversation design patterns • Why this is important for games of the future…
  4. 4. Social Simulations
  5. 5. Dialogue Approaches • Language Parser • Simple Response • Interactive Choice-Based Dialog – Linear – State Based – Branching (Dialogue Tree) • NLU AI Processing – NLRA – Goal
  6. 6. PARSER
  7. 7. Simple Response
  8. 8. Who ate the Doritos?
  9. 9. Choice-based Dialogue
  10. 10. Linear
  11. 11. Branching
  12. 12. Branching Judgment
  13. 13. Linear w/ Choices
  14. 14. Virtual Child Witness
  15. 15. Unlocker
  16. 16. NLRA CONVERSATIONS • Natural Language Random Access • Natural Language Understanding (NLU) AI Dependent – Requires training language – Probabilistic, not deterministic • It is not possible to have a full scope human conversation • Success depends on CONSTRAINTS – Constrain domain: narrower means better performance – Constrain initiative: exchanging initiative difficult – Constrain scope: Go broad or deep, not both • Lots of dialogue interaction patterns • Possible to mix choice-based with NLRA
  17. 17. USC Standard Patient NLRA • 92%+ NLU Performance • Voice Recognition decreases NLU performance • Strong Training Effect • Time/Choice constraints on users IMPROVES performance • People say weird things to computers
  18. 18. Some things never change
  19. 19. Lessons & The Future • Medical Interview Lessons • Assessment Lessons • Live Feedback • Enhancers – Dubbed audio, Scanned characters, custom taxonomy, etc… • Mixing methodologies • Perceptive • MultiState Dialogue Agents (Physiology Construction Set)
  20. 20. 7/30/2018 (C)2017 Medical Mechanica 36
  21. 21. 7/30/2018 (C)2017 Medical Mechanica 37
  22. 22. GAME OVER

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