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1 The Behavior of Schools Groups of f ishes can behave almost - PDF document

Dec 25, 2023 •287 likes •359 views

Last t ime Self -Organizat ion Aut onomous Agent s Real Ant s Virt ual Ter mit es Virt ual Ant s Ant Algorit hms 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 1 Out line f or t oday Ant algorit hms (cont .)

  1. Last t ime ❒ Self -Organizat ion ❒ Aut onomous Agent s ❒ Real Ant s ❒ Virt ual Ter mit es ❒ Virt ual Ant s ❒ Ant Algorit hms 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 1 Out line f or t oday ❒ Ant algorit hms (cont .) ❒ Assignment s ❒ Proj ect ❒ Schooling of f ish ❒ Boids 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 2 Flocks, Herds, and Schools ❒ ”and t he t housands of f ishes moved as a huge beast , piercing t he wat er. They apperared unit ed, inexorably bound t o a common f at e. How comes t his unit y?” - Anonymous, 17th century 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 3 1

  2. The Behavior of Schools ❒ Groups of f ishes can behave almost like a single or ganism ❒ Traf algar ef f ect ❍ Rapid t r ansf er of inf ormat ion ❍ Can execut e swif t , evaisive maneuvers ❍ React ion propagat es many t imes f ast er t han t he approach of t he pr edat or ❒ Ex: Flash expansion and t he f ount ain ef f ect ❒ Predat ors can also coor dinat e t heir movement s ❍ Ex: P ar abolic f ormat ion of Giant bluef in t una 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 4 The Behavior of Schools ❒ I ndividuals rarely collide, even in f renzy of at t ack or escape ❒ Shape of school is charact erist ic of species, but f lexible ❍ Herring 3:3:1 (rat io of lengt h: widt h: dept h) ❍ Pollack 6:3:1 ❍ Cod 10:4:1 t o 2:4:1 ❒ Arrangement wit hin schools is also charact erist ic of species ❍ Depend also on t he size and t he speed of t he school 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 5 Adapt ive Signif icance ❒ Prey avoiding predat ion ❍ Saf et y in number s ❍ Unit ed err at ic maneouver s ❍ Pat t ern of body colorat ion ❍ Group breaking behaviors ❍ Compact aggr egat ion – pr edat or r isks inj ur y by at t acking 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 6 2

  3. Adapt ive Signif icance ❒ Bet t er predat ion ❍ Coordinat ed movement s – t una ❍ More ef f icient pr edat ion • Killer whales encircle dolphins • Take t urns eat ing ❒ Schooling may increase hydrodynamic ef f iciency ❍ Endurance may be incr eased up t o 6 t imes ❒ V-f ormat ion of geese ❍ Range incr ease 70% ❒ Lobst er line up ❍ Move 40% f ast er – decr eased hydr odynamic drag 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 7 Behavior of I ndividuals wit hin t he School ❒ A balance bet ween at t ract ion and repulsion ❒ Sensory input s ❍ Vision – at t ract ion and alignment ❍ The lat er al line syst em – r epulsion and speed mat ching ❒ Weight ing of inf or mat ion coming simult aneously f rom several f ishes ❍ Most st rongly inf luenced by near est neighbor s 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 8 Alt ernat ives t o Self -Organizat ion ❒ Templat es ❍ No evidence t hat wat er current s, light , chemicals guide collect ive movement ❒ Leaders ❍ No evidence f or leader s ❍ Those in f ront changes ❍ Each adj ust s t o several neighbors ❒ Blueprint or recipe ❍ I mplausible f or coor dinat ion of lar ge schools 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 9 3

  4. Self -Organizat ion Hypot hesis ❒ Simple rules generat e schooling behavior ❍ Posit ive f eedback – br ings individuals t oget her (at t ract ion) ❍ Negat ive f eedback – but not t o close (r epulsion) ❒ Only local inf or mat ion ❍ P osit ions and headings of a f ew near by f ish ❍ No leader, no global plan 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 10 Hut h and Wissel (1992) Model – Basic Assumpt ions ❒ Each f ish f ollows t he same rules ❒ Each f ish uses some f or m or weight ed average of t he posit ion and orient at ion of it s nearest neighbors ❒ Each f ish responds t o it s neighbors in a probabilist ic manner ❍ I mperf ect inf ormat ion gat her ing ❍ I mperf ect execut ion of act ions ❒ No ext ernal inf luenses af f ect ing t he f ish ❍ No curr ent s, obst acles... 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 11 Hut h and Wissel (1992) Model – Behaviors ❒ Repulsion ❒ At t ract ion ❒ Parallel orient at ion ❒ Searching ❒ Ranges of t he basic behavior pat t erns ❒ How t o int egrat e and evaluat e inf ormat ion f rom dif f erent neighbors? ❍ Decision models ❍ Averaging models 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 12 4

  5. Hut h and Wissel (1992) Model – Limit at ions ❒ No adressing of ext ernal inf luences ❒ No obst acle avoidance ❒ No avoidance behaviors such as: ❍ Flash expansion ❍ Fount ain ef f ect ❒ Recent work (1997 – 2000) has adressed some of t hese issues 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 13 Boids ❒ Craig Reynolds ❒ Flocks, Her ds, and Schools: A Dist ribut ed Behavioral Model 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 14 Boids – Separat ion 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 15 5

  6. Boids - Alignment 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 16 Boids - Cohesion 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 17 Boids - Neighborhood 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 18 6

  7. Summar y ❒ Ant algorit hms (cont .) ❒ Assignment s ❒ Proj ect ❒ Schooling of f ish ❒ Boids 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 19 Next t ime ❒ Swarm algorit hms 23/11 - 04 Emergent Systems, Jonny Pettersson, UmU 20 7

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