1. Controlling the Behavior of Swarm Systems Zachary Kurtz CMSC 601, 5/4/2011 1

2. 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 2

3. 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 3

4. 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’ 4

5. Problem  Creating a desired swarm behavior requires hand- crafted 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 5

6. 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] 6

7. 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 7

8. 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) 8

9. 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 9

10. 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 10

11. 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 11

12. 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. 12

13. Questions? 13