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Practical Models of Human Visual Perception for Simulation

Practical Models of Human Visual Perception for Simulation. Chris Darken Assoc. Prof., Computer Science MOVES 11th Annual Research and Education Summit July 12, 2011. 831-656-7582 http:// movesinstitute.org. Outline. Why we care about perception Quick tour of recent work applied to:

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Practical Models of Human Visual Perception for Simulation

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  1. Practical Models of Human Visual Perception for Simulation Chris Darken Assoc. Prof., Computer Science MOVES 11th Annual Research and Education Summit July 12, 2011 831-656-7582http://movesinstitute.org

  2. Outline • Why we care about perception • Quick tour of recent work applied to: • Army’s Combat XXI (joint with TRAC) • ONR’s BASE-IT prototype • Suggested future work

  3. 1. Facilitating Training

  4. 2. Real World Perceptual Tasks

  5. 3. Human Behavior Simulation

  6. Outline: Three Methods • Concealed routes • Pre-cached approximate LOS • Hierarchical threat maps

  7. Background • Two types of visual perception deficits for synthetic entities in combat sims • Complete lack of terrain info visible to the user • Information provided, but not aggregated properly • Significance of visual perception deficits • Hunting and avoiding threats in combat sims requires adequate visual perception representation • This is especially true for analytic simulations of situational awareness enhancing gear • Failure to fix deficits makes accurate behavior either • Impossible • Too computationally inefficient for practical use • Too difficult for the typical scenario developer to implement

  8. Outline: Three Methods • Concealed routes • Pre-cached approximate LOS • Hierarchical threat maps

  9. Concealed Routes • Goal is to provide an automated way for entities to take terrain into account when planning movement. • Conceptually a location that has LOS to a large number of other locations can be said to have “bad” concealment. Good Concealment Bad Concealment

  10. Line of Sight Density Results High LOS Density cost tended to follow rivers and ridges No LOS density cost moves over open terrain Artifact caused by edge of network.

  11. Outline: Three Methods • Concealed routes • Pre-cached approximate LOS • Hierarchical threat maps

  12. Point Based Estimated LOS Accuracy • Used coarse grid of nodes to approximate true LOS • Tested to compare accuracy of estimated LOS to actual LOS. • 16.4 km2 box in Camp Pendleton, 50m nodespacing, about 3% covered with buildings. • False positives 0.1% / False negatives 5.0% • Processing about 35% faster. • 1.7 km2 box in flat fictitious terrain, 20m, about 40% covered with buildings. • False positives 1.0% / False negatives 2.0% • Processing about 25 times faster.

  13. Point Based Pre-calculated LOS Actual LOS Point Based LOS

  14. Outline: Four Methods • Concealed routes • Pre-cached approximate LOS • Hierarchical threat maps

  15. Hierarchical Threat Maps • Threat map: probability distribution providing a subjective estimate of where threats are • Variant of ACQUIRE based on precomputed target size and contrast values is used • Requires lots of computation! • Visibility data at left; threat map at right

  16. Scaling Up 2010 Kilo 2, Pendleton 155,000 m2 2009 Partial Range 200, 29 Palms 13,000 m2 2008 Fictitious terrain 1,700 m2

  17. Solution: Multiple Levels of Resolution • How to use • Coarse resolution far from entity • Fine resolution near • Why to use • Saves computation • Intuitively accurate From Clingman et. al., “Practical Java Game Programming”

  18. Fireteam 1’s Threat Model

  19. Fireteam 2’s Threat Model

  20. Results • Number of nodes in hierarchical vs. original model (left) • Model update time for both models (right)

  21. Conclusions • Representations for concealed movement planning, visual search, and threat location have been presented • Precomputation and muti-resolution spatial data structures make the techniques practical • All have been prototyped either in Combat XXI (TRAC) or the BASE-IT (ONR) prototype sims • Much work remains to be done, both towards validation and obvious extensions of the models

  22. From 2004Problem: Nearly Blind Agents

  23. It Seems That We’re Always Missing Something… • Cover • Lacked obstacle info • Target detection • Lacked exposed surface and contrast info • Threat Location/Visual search • Lacked both above plus shooting position info

  24. …So Why Not Give Just Agents Everything We Give a Human User?

  25. Questions?

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