Transcript Zachary Kurtz

Controlling the Behavior
of Swarm Systems
Zachary Kurtz
CMSC 601, 5/4/2011
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Background
 Swarm systems are composed of many simple agents,
each following a set of distributed rules or behaviors
 Swarm systems have a number of applications
 Swarm Robotics
 Particle Swarm Optimization
 The are several standard rule sets used in swarm systems
 Boid Model developed by Reynolds [1]
 Physics based models
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Background: Example
 On the left is an example of a
Boid swarm
 There are three rules
controlling the swarm
 Cohesion pulls the members
together
 Separation keeps the members
from colliding
 Alignment keeps the velocities
of the members similar
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Background (Cont.)
 Creating more complex behaviors often requires custom
rules
 For example creating a circular formation with a swarm
requires specially designed rules:
 For each Agent A, select the farthest agent A’
 If the distance(A, A’) > R, A moves toward A’
 If the distance(A, A’) < R, A moves away from A’
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Problem
 Creating a desired swarm behavior requires handcrafted rules
 It is often easier to evaluate how well a swarm is
matching a behavior
 Solution: Develop an automated system to select a rule
set, given an evaluation function
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Related Work
 Finding optimal parameters for a rule set has be
previously explored by Miner [2]
 Many groups have explored methods for creating various
formations:
 Sugihara explored methods for forming circles, lines, and
polygons with distributed rules [3]
 Spears and Spears created hexagonal and square lattices
using distributed physics based rules [4]
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Approach
 Have as an input, an evaluation function that determines
how well the swarm is matching the desired behavior
 Start with a large set of basis rules
 A rule set can be created by assigning a weight to each
basis rule
 If a large, represented set of basis rules is used, the
optimal rule set should a subset of the basis rules
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Approach (Cont.)
 A genetic algorithm can be applied to find the best
subset of rules
 Start with a population of random rule sets
 Evaluate the fitness of each rule set by creating a swarm,
and applying the given evaluation function
 Select members for the next generation from the old
population weighted by fitness
 Mutate and crossover
 Repeat until the fitness converges (or some time limit has
been reached)
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Challenges
 May be computationally expensive to find the optimal
set of rules
 The set of possible rule sets is limited by the basis rules
 General representation of more complex rules, such as
rules that assign different types to the members of the
swarm
 The evaluation function output shouldn’t need to be
“fine-tuned” to work with the genetic algorithm
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Evaluation
 Pick a set of basis rules from the literature
 Pick a set of behaviors with known rules sets from the
literature
 Create evaluation functions for each of these behaviors
 Create a swarm from that evaluation function using the
detailed approach
 Compare the performance of the created swarm to the
swarm from the literature
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Conclusion
 Introduced swarm systems
 Proposed a method for generating a set of rules to
create an emergent behavior
 Discuss the feasibility of the approach and potential
challenges
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References
 [1] - C.W. Reynolds. Flocks, herds and schools: A distributed
behavioral model. In Proceedings of the 14th annual conference
on Computer graphics and interactive techniques, pages 25–34.
ACM, 1987.
 [2] - Don Miner and Marie desJardins. Predicting and controlling
system-level parameters of multi- agent systems. In AAAI Fall
Symposium on Complex Adaptive Systems and the Threshold
Effect, 2009.
 [3] - K. Sugihara and I. Suzuki. Distributed motion coordination of
multiple mobile robots. In 5th IEEE International Symposium on
Intelligent Control, pages 138–143. IEEE, 1990.
 [4] - W. Spears and D. Spears. Distributed physics based control of
swarm vehicles. Autonomous Robots, 17(2):137–162, 2004.
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Questions?
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