boids: modeling and understanding emergent behavior - ee 594 final paper

8/14/2019 Boids: Modeling and understanding emergent behavior - EE 594 Final Paper

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Sudeep Pillai

Boids: Modeling and understanding emergent behavior

IntroductionFlocking of birds has always been a spectacle to all, especially to ornithologists. It has been one of themost intriguing, yet natural behaviors that I’ve ever encountered and it can be hard to explain it just from

its movement. Its magnificence lies in its fluidic motion, but its complexity is noticed in its synchronized behavior. Each discrete bird behaves based on its local perception; however, it is amazing how their interactions produce an overall fluid-like unified motion. What’s more interesting is that such behaviorsoccur even when the bird does not have a global perspective of its goal or movement. Such splendors inlife give rise to several questions in behavioral psychology, distributed behavior control and many other fields including complexity.

In this paper, I would like to address the notion of ‘emergence’ that is pertinent to flocking. Using thisgeneralized theory, I have attempted to understand the behavior of birds in flocks and their reason toflock. This in turn allowed me to simulate a rather simple yet reasonably realistic model of how birdsflock. Implementing such a simulation framework has also compelled me to look into different algorithmsthat have already been implemented and those that I’ve learnt from this class.

MotivationBirds, in general, have always puzzled me. I’m amazed by how birds can so intelligibly interact with eachother to strategize an efficient flying formation that benefits the flock as a whole. Their intricatemaneuverability and their spectacular flocking behavior can be quite startling to many people when theyare used to seeing the common phenomenon of birds hitting windows. Nevertheless, if birds have‘emerged’ over the past years to fabricate an ingenious flying strategy, then how did they possibly do thiswith a low level of intelligence? My interest in this topic gives me a better understanding of distributed

behavior models and allows me to pursue my career in autonomous vehicles and intelligent highwaytraffic where this idea may be relevant.

The ‘Science’ of FlockingEven though several scientists have tried to reason the need to flock, it still remains uncertain why most

birds flock. One reason towards flocking pertains to the need of each individual bird to reduce the dragforce that it experiences compared to if it were flying alone. During flight, vortices generated at thewingtips of each bird produce an upwash that can be beneficial to a second wing flying behind andslightly above the first. A bird flying in this upwash region gains free lift allowing it to reduce its angle of attack, thereby decreasing the drag induced during forward thrust. The birds that flanks a bird ahead of itgains an advantage as it needs lesser energy to fly, allowing it to fly for a longer distance. A commonmisunderstanding is that the bird that leads the flock is at most disadvantage. This is not true since thereduced downwash that is dissipated by the two birds that flank it helps reduce the induced drag that itexperiences. Thus putting both interactions into perspective, birds towards the middle gain considerableoverall advantage over the other birds in the flock. The idea that a “V formation” is produced from suchinteractions is quite acceptable as each bird gains benefit from one another thereby reducing the overallexpenditure in energy.

This interaction brings another problem into play that the flock tackles ever so beautifully. Since the birdleading the flock tires the quickest, they drop out of the lead position and fall further back into one of the



issue. Can we possibly have different permutations of such different behaviors with different ‘influence’terms? Questions such as these are yet to be explained and I hope to continue working on them given thatI finally built a framework for modeling boids.

Future Work

As was evident from the video of my simulation, there exists a fluidic flow even with a few simple rulesthat govern each of the boids. Given the current modeling framework, it is also possible to optimize these

behaviors to produce better results catering to a particular need. A typical implementation is related toforaging for food based on the Ant Colony Optimization [2] techniques (ACO) that have been modeledafter ant behavior. Another technique that is closely related to ACO is the Particle Swarm Optimization(PSO) that share similar evolutionary computation techniques with that of GAs. The PSO algorithm looksfor a single solution i.e. ‘a single bird’ that is closest to the goal and optimizes the solution by requestingthe bird to lead the flock. The fitness function that each bird has is re-evaluated at each time step andoptimized to produce best results.

Another possible solution to realistically modeling of flocks could involve modeling a 3D perspectiveview of each boid and calculating its effects due to other boids and physics. This method, apart from

being relatively complex, can be fairly expensive to compute. However, an alternative to this solution thatcould be more feasible involves projecting each of the effects of the boid onto a plane that is normal tothe heading vector of each boid. This relatively a non-trivial but feasible method that could take intoaccount of aerodynamics and flocking behavior to predict the formations that we see often in real life.

ConclusionsMost of the existing models and techniques that have been developed are based on our perception of howflocks behave and interact, while some others based on optimizing certain tasks such as food foraging.However, there has not been one model that reasonably predicts the realistic behaviors of birds in flocks.Some of the questions and concerns I’ve put forward may be never dealt with, but it doesn’t stop us fromasking ourselves the question, ‘Have flocks emerged completely?’ If not, then, is the “flying V”formation a transitional phase in emergence that took ever so long to perfect that it may take even longer to come up with a more optimum formation?

The transitional phase in the emergence of a flock can also be related to Conway’s glider where thetransition function described produces a sequence of changes in pattern over successive time-steps.Similar to Conway’s glider, in the case of boids or flocks, the possibility of a spatially coherent patterncannot be easily determined due to its vast number of non-linear interactions. The possibilities areendless, and we model based on what we perceive. It requires a combination of experiment, bird-watching in this case, and theory to discover and explain regularities. This implies that birds may have

evolved and ‘emerged’ over time, they may truly have developed an emergent behavior of higher order intelligence; but can they ‘emerge’ further to produce even higher orders of intelligence given the endless possibilities.



References

[1] Reynolds, C. W., “Flocks, Herds, and Schools: A Distributed Behavioral Model” atSIGGRAPH ‘87

[2] Kennedy, J. and Eberhart,R.C, “Swarm Intelligence”, Morgan Kaufman Publishers, 1994

boids: modeling and understanding emergent behavior - ee 594 final paper

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