Putting Simple Hierarchy into Ant Foraging: Cluster-based Soft-bots

1. Putting Simple Hierarchy into Ant Foraging: Cluster-based Soft-bots Wei Peng, Qingmai Wang, Bin Wang, and Xinghuo Yu School of Electrical and Computer Engineering RMIT University, Melbourne

2. Summary • Introduction • Traditional Ant Foraging algorithm • Cluster-based Soft-bots algorithm • Simulation and Result • Discussion and Future direction

3. Introduction • Swarm Intelligence (SI) • SI is the property of a system whereby the collective behaviors of agents interacting locally with their environment cause coherent functional global patterns to emerge • Simple behaviors of individual agents + Communication locally with environment = Complex behavior of the group • SI-based approaches fit well into application domains whereas centralized control architecture is either unavailable or too expensive to implement.

4. Introduction

5. Introduction • Key concepts of SI • Stigmergy: • Stigmergy refers to a class of mechanisms that mediate animal-animal interactions. • Indirect interaction: Two individuals interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time. • Self-organization • No need for any planning and central control • No global pattern or external management

6. Introduction • Not many resource constraints have been considered when constructing SI-based algorithms, which result in high accumulated costs • There is a need to introduce simple hierarchy to enhance the overall performance of traditional SI approach

7. Introduction • A cluster-based Softbots algorithm is developed to improve the overall performance of a traditional Ant Foraging algorithm in a search-and-rescue scenario • A Layer of hierarchy is introduced to allow for cohort-like regulated behaviors so as to minimize the randomized behaviors presented in traditional SI agents.

8. Ant Foraging Algorithm • Two phases: • Leaving the nest to search for food • Returning to the nest with food • Pheromone: • Ants deposit pheromone on the paths that they cover to mark the trail. • Evaporation and diffusion

9. Ant Foraging Algorithm • Two different pheromones were used • One pheromone is deposited by ants to mark trails to the food sources. • The other pheromone is released by the nest, which diffuses in the environment and creates gradient that the ants can follow to locate the nest.

10. Ant Foraging Algorithm • Ant Foraging Procedure ask each ant [ if not carrying food [look-for-food] else [return-to-nest] ] diffuse ask environment [evaporate]

11. Ant Foraging Algorithm • return-to-nest if nest? [ drop food and head out again] else [ drop some chemical and head toward the greatest value of nest-scent] • look-for-food if food > 0 [ pick up food and reduce the food source and turn around] if sense chemical [go in the direction where the chemical smell is strongest]

12. Ant Foraging Algorithm

13. Cluster-based Soft-bots Algorithm • The assumption for this approach is that simple layer of hierarchy will reduce randomized costs associated with autonomous search in Ant foraging Algorithm • Soft-bots are autonomous agents that are organized in a unit called “cluster”. • Each cluster consists of a header agent and several followers.

14. Cluster-based Soft-bots Algorithm • A header can recruit several followers to search for the target. They form a cluster and each follower only communicates with its header. • Once a member of a cluster identifies a target group, all members of the cluster will share the information and carry targets back to the home base.

15. Cluster-based Soft-bots Algorithm • A header has the ability to lay emitters, which can release signals and broadcast about the location of the identified target. • Once in the signal range of an emitter, a header will lead its cluster to move towards the emitter along the gradient of the signal. • If all targets within the range of a signal emitter have been moved away, the individual who reaches the emitter and no longer finds the target will decommission the emitter

16. Cluster-based Soft-bots Algorithm • Cluster-based Softbots Procedure ask each header [if find target [Drop a emitter, carry the target and return to base] else [construct cluster if detect the signal [move to emitter] else [move randomly and look-for-target] ] ]

17. Cluster-based Soft-bots Algorithm ask each follower [if find target [carry and return to base] else [if see the emitter [move to emitter] else [look for target if follows a header [move with header] else [move randomly] ] ] ]

18. Cluster-based Soft-bots Algorithm

19. Simulation and Result • The task for both algorithms is identical, which is to search for food (or target groups) and bring them to the nest (or home base). • The experiment compares time and efforts consumed in the designed task in each algorithm for various number of ants and Soft-bots. • Each algorithm uses a different strategies: ants release chemicals but Soft-bots emit signal emitters to inform other members the location of the target

20. Simulation and Result

21. Simulation and Result

22. Simulation and Result

23. Simulation and Result

24. Discussion and Future works • ACluster-based Soft-bots algorithm has been proposed to compensate for large portions of efforts consumed in randomized search in traditional SI-based algorithm. • The Soft-bots algorithm has been demonstrated significant comparative advantages over Ant Foraging algorithm in the presented simulation experiments.

25. Discussion and Future works • There is a need to validate and generalize this result under all potential operational configurations and more complicate scenario for the two algorithms. • There is also space for further improvement for the performance of the Soft-bots via introducing other regulating rules • Balance the functionality of agents and the cost of implementation

26. Thank you