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Episodic Memory for Virtual Humans and Virtual Humans for Episodic Memory Cyril Brom et al. Faculty of Mathematics and Physics, Charles University in Prague, CZ brom@ksvi.mff.cuni.cz Team Jakub Gemrot, Michal Bda, Ond ej Burkert,


  1. Episodic Memory for Virtual Humans and Virtual Humans for Episodic Memory Cyril Brom et al. Faculty of Mathematics and Physics, Charles University in Prague, CZ brom@ksvi.mff.cuni.cz

  2. Team � Jakub Gemrot, Michal Bída, Ond ř ej Burkert, Klára Pešková, Tomáš Korenko, Jan Vyhnánek, Rudolf Kadlec, Jakub Kotrla, Lucie Ku č erová, Lukáš Zem č ák � Funding agencies: IS, MSMT

  3. Virtual humans for episodic memory

  4. Neuro/psychological perspective � Memory for personally personally � Place cells, grid cells, experienced past events head - d irection cells... (episodes) � Morris water pool, ... � Recall of an event in the spatio-temporal context in which it was experienced � H.M. � Tulving (1983) � Baddley (2000, 2001) � Forensic psychology

  5. Types of research � Animal research � spatial cognition � episodic-like memory � Casuistries � “Laboratory” cognitive psychology � world-list memorising etc. � Forensic psychology � Computational modelling � robotic artifacts

  6. State of the art � A couple of computational models � fundamental research vs. engineering � These models: � tend to focus only on a narrow range of phenomena, � most importantly: space vs. events � exceptions are rare � typically oriented on short-term intervals, � do not support representation of do not support representation of � complex plausible human - like episodes . complex plausible human - like episodes

  7. Main methodological proposal � This state of affairs can be improved using the technology of virtual virtual environments inhabited by intelligent intelligent environments virtual agents as a test test- -bed bed for virtual agents developing, investigating, and integrating various neuro-/psychologically plausible models of episodic and/or spatial memory.

  8. Complex episodes � Interaction with multiple objects � e.g. cooking � Large spaces � e.g. a city � Long periods of time � e.g. months, years

  9. Main advantages of virtual worlds � Features: � more technically accessible and cheaper then robots � low-level (e.g. distances, pixels) vs. high-level (e.g. affordances) � ecologically plausible models of real worlds (to some extent) � speed-up time � Robots or virtual agents? � trade-off: crippled worlds with “natural” noise vs. large worlds � the “robotic risk” : you see the phenomena through the hardware limitations � the “virtual agents risk”: you see the phenomena without any limitations � Main Pros: � integration of “single-phenomena” models � combination of levels of abstraction � large environments � longitudinal studies in virtual reality settings with human subjects

  10. Pogamut

  11. Integrated Development Environment

  12. Integrated Development Environment Development Debugging Support for experimenting - parallel computing Education

  13. Episodic memory for virtual humans

  14. Our episodic memory research � Purpose: Hybrid � agents for serious games � neuro-/psychologically plausible � methodological: a vehicle for other models � Main focus � complex episodes � large spaces � long periods of time � integration of perception of space and events

  15. Our episodic memory research � Main issues � Representations of complex episodes, large spaces, life-time intervals � Plausible forgetting � blending of episodes, false memories � perception of time, time as a cue � Context abstraction � Techniques � symbolic � connectionist

  16. Our agent architecture ENV STM Goals Structure objects tasks Task select ion Attention Filter PF <invoke TF a subgoal> DRIVES MF Conflict resolution <search> <remember mechanism location of an object> <remember <recall> episode> ACTION Emotions <influence> LTSM LTEM ENV Lingv. module VERBAL OUTCOME

  17. Example – – Example a virtual shaman a virtual shaman � Autobiographic memory with forgetting � 5 - d ays � Dynamic scenario � Debriefing “I was doing SearchRandom for smokeability because of Smoke. I was doing go from room 1 to room 2 because of SearchRandom. I was doing look in environment because of SearchRandom. I was doing go from room 2 to room 5 because of SearchRandom. I was doing pick up Calumet1 because of Smoke. I was doing Smoke.”

  18. Forgetting – example (no “emotions”)

  19. Forgetting – example (with “emotions”)

  20. Positions of objects in large worlds � Searching strategies: � where are the glasses? � Could be at your bedside table. concrete � If not, could be next to the TV. � If not, scrutinise your study room. abstract � If not, scrutinise the whole flat.

  21. End We have a poster! We have a poster!

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