Controlling the Behavior of Swarm Systems

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

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

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

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’

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

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]

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

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)

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

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

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

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.

13. Questions?