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Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto

Artificial Fishes: Physics, Locomotion, Perception, Behavior. Mar. 30, 2001. Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto. Presentation by Siddharth Dalal. Intro & Background. What do fish do? eat, survive, when compelled by their libidos….

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Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto

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  1. Artificial Fishes: Physics, Locomotion, Perception, Behavior Mar. 30, 2001 Xiaoyuan Tu and Demetri Terzopoulos, Dept. of CS, University of Toronto Presentation by Siddharth Dalal

  2. Intro & Background • What do fish do? • eat, survive, when compelled by their libidos…. • Physics based graphic modeling • Worm Dynamics, facial model • more sophisticated spring mass model • advanced behavioral animation Any fish is good if caught on the hook.

  3. Overview Intention focuses sensory data causing behavior

  4. Fishics 1 - Mechanics • Spring Mass Model m = mass x = position q = damping factor w = net force due to springs f = external force

  5. Fishics 2 - Hydrodynamics • Swimming - Muscles + Hydrodynamics

  6. Fishics 3 - Motor Controllers • Swim MC • Left and right MC • Anterior and Posterior of fish - r1, s1, r2, s2 • Max params scaled from 0 - 1 to produce varying speeds

  7. Sensory Perception • Two on board environment sensors: • Vision Sensor - extracts information from scene geometry, object database, physical simulation. Cyclopean(?) vision - 300o viewing angle. • Temperature sensor - senses ambient temp. at center of body

  8. Behavio(u)r 1 • Intention based on • Habits • Mental State • Incoming Sensory Information • decides behavior routine • incremental - needs memory

  9. Behavior 2 - Habits and Mind • Habits - does fish like brightness, schooling, male or female (yes this is in habits) • Mental State • Three mental states - HLF - hunger, libido, fear • H= min[1-n(t)R(Δt)/α, 1] • L=min[s(Δt)(1-H(t)), 1] • F=min[Σf, 1], f=min[D/d(t), 1] (Fish like sex after dinner )

  10. Intentions 1 • Intentions • avoid, • escape • school • eat • mate • leave • wander

  11. Intentions 2 • Features of Generator • Persistence in intentions - no dithering • focusser - focus on most important intention • Create ‘abnormal fish’ • warp intentions

  12. Intentions 3 • Behavior routines: • eight - avoid static obstacle, avoid fish, eat, mate, leave, wander, escape, school • chasing target subroutine • other subroutines - looping?, circling, ascending?, nuzzling

  13. Fish Type = Warped Intentions • Artificial Fish Types • Predators • don’t escape, mate or school • always cruise, so don’t leave

  14. Fish Type = Prey Fish Grey Fish • Artificial Fish Types • Prey • school • evade predators

  15. Pacifists • Artificial Fish Types • Pacifist • no school, no escape • just mate • complex mating behavior implemented… • fish i chooses partner j • criteria if i is female/male • looping, circling, chasing-target, nuzzling • etc.

  16. Result • 10 fish, 15 food particles, 5 static obstacles at 4fps on SGI R4400 Indigo2 • Future: • reproduction • other work

  17. Links • http://www.dgp.toronto.edu/people/tu/tu.html • http://citeseer.nj.nec.com/tu94artificial.html • http://www.cs.toronto.edu/~dt/

  18. Guests and fish start to stink after two days.

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