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1 The Behavior of Schools Groups of fishes can behave almost like a - PDF document

Aug 22, 2022 •433 likes •507 views

Last time Self-Organization Autonomous Agents Real Ants Virtual Ants Ant Algorithms Assignment 2 Assignment 3 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 1 Outline for today Schooling of fish Boids

  1. Last time � Self-Organization � Autonomous Agents � Real Ants � Virtual Ants � Ant Algorithms � Assignment 2 � Assignment 3 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 1 Outline for today � Schooling of fish � Boids 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 2 Flocks, Herds, and Schools � ”and the thousands of fishes moved as a huge beast, piercing the water. They appeared united, inexorably bound to a common fate. How comes this unity?” - Anonymous, 17th century 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 3 1

  2. The Behavior of Schools � Groups of fishes can behave almost like a single organism � Trafalgar effect � Rapid transfer of information � Can execute swift, evasive maneuvers � Reaction propagates many times faster than the approach of the predator � Ex: Flash expansion and the fountain effect � Predators can also coordinate their movements � Ex: Parabolic formation of Giant bluefin tuna 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 4 The Behavior of Schools � Individuals rarely collide, even in frenzy of attack or escape � Shape of school is characteristic of species, but flexible � Herring 3:3:1 (ratio of length: width: depth) � Pollack 6:3:1 � Cod 10:4:1 to 2:4:1 � Arrangement within schools is also characteristic of species � Depend also on the size and the speed of the school 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 5 Adaptive Significance � Prey avoiding predation � Safety in numbers � United erratic maneuvers � Pattern of body coloration � Group breaking behaviors � Compact aggregation – predator risks injury by attacking 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 6 2

  3. Adaptive Significance � Better predation � Coordinated movements – tuna � More efficient predation • Killer whales encircle dolphins • Take turns eating � Schooling may increase hydrodynamic efficiency � Endurance may be increased up to 6 times � V-formation of geese � Range increase 70% � Lobster line up � Move 40% faster – decreased hydrodynamic drag 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 7 Behavior of Individuals within the School � A balance between attraction and repulsion � Sensory inputs � Vision – attraction and alignment � The lateral line system – repulsion and speed matching � Weighting of information coming simultaneously from several fishes � Most strongly influenced by nearest neighbors 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 8 Alternatives to Self-Organization � Templates � No evidence that water currents, light or chemicals guide collective movement � Leaders � No evidence for leaders � Those in front changes � Each adjusts to several neighbors � Blueprint or recipe � Implausible for coordination of large schools 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 9 3

  4. Self-Organization Hypothesis � Simple rules generate schooling behavior � Positive feedback – brings individuals together (attraction) � Negative feedback – but not to close (repulsion) � Only local information � Positions and headings of a few nearby fish � No leader, no global plan 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 10 Huth and Wissel (1992) Model – Basic Assumptions � Each fish follows the same rules � Each fish uses some form of weighted average of the position and orientation of its nearest neighbors � Each fish responds to its neighbors in a probabilistic manner � Imperfect information gathering � Imperfect execution of actions � No external influences affecting the fish � No currents, obstacles... 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 11 Huth and Wissel (1992) Model – Behaviors � Repulsion � Attraction � Parallel orientation � Searching � Ranges of the basic behavior patterns � How to integrate and evaluate information from different neighbors? � Decision models � Averaging models 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 12 4

  5. Huth and Wissel (1992) Model – Limitations � No addressing of external influences � No obstacle avoidance � No avoidance behaviors such as: � Flash expansion � Fountain effect � Recent work (1997 – 2000) has addressed some of these issues 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 13 Boids � Craig Reynolds � Flocks, Herds, and Schools: A Distributed Behavioral Model 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 14 Boids – Separation 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 15 5

  6. Boids - Alignment 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 16 Boids - Cohesion 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 17 Boids - Neighborhood 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 18 6

  7. Summary � Schooling of fish � Boids 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 19 Next time � Swarm algorithms 15/11 - 05 Emergent Systems, Jonny Pettersson, UmU 20 7

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