Swarm jobs of today covers some basics of complex systems, agent based modelling and contemporary swarm applications.

1. Swarm Jobs of Today : Harnessing Swarm Intelligence
suresh.sood@uts.edu.au
@soody
http://www.slideshare.net/ssood/swarm-jobs

2. Housekeeping Complexity Stories 4– Building Complex Systems Models
This discussion and lecture includes a hands on with building or rather playing with complex system (agent
based ) models. The only background assumed is general use of your laptop computer. During this session
participants gain familiarity with NetLogo, a specialist agent-based modelling language. NetLogo is free, and is
available for Mac OSX, Windows and Linux. The latest software as of February 2017 is downloadable from:
https://ccl.northwestern.edu/netlogo/6.0/
You are encouraged to use your own laptop computer or at least have one shareable amongst a group.
Owing to limited time the expectation is you have already downloaded and installed the Netlogo software prior
to attending this session.
This in class session covers agent-based modelling theory and simulations of selected models. In
Netlogo a modeler gives instructions to hundreds or thousands of independent "agents” operating in parallel.
As a takeaway, you will be able to execute agent based models installed with Netlogo as well as modify an
existing model. Beyond the class, the desire based on your own interest is undertaking experimentation by
executing a variety of models and changing parameters to witness first hand impacts of the number of agents
and other variables. Furthermore, a “Modeling Commons” is available at
http://modelingcommons.org/account/login. This community of over 1,000 models is for sharing and
discussing agent-based models written in NetLogo by modelers from around the world allowing the exploration
of the connections existing between the micro-level behavior of individuals and the macro-level patterns that
emerge from the interaction of many individuals.
2Suresh Sood 2017

3. Swarm Intelligence (SI)
• Bio- Inspiration (biomimetic)
 Flocking of birds
 School of fish
 Ants foraging food
 Termite mound construction
• Physics Inspiration (physicomimetics; Spears and Spears 2011)
 Nature is lazy informs physical robot design
 Physics is most “predictive science” (ibid) with respect to behavior of systems and pre-setting parameters
 Overcomes challenges of environmental traces
• Swarm Algorithms
 Ant colony optimisation
 Particle swarm optimisation
• Emergent Behaviour
 Bottom up # top down
 Builds on local interactions of the swarm directly or via environment (stigmergy)
Suresh Sood 2017 3

4. SI Represents a Complex System
• Complex Adaptive Systems
• Whole is greater than the sum of the parts
• Many parts
• Relationships “the missing link”
• Emergence and self-organisation
• Non-linear and dynamic patterns
• Difficult to predict outcomes & manage - little things make big difference
Suresh Sood 2017 4

5. Fingerprints of Complexity (Casti 199x)
• Medium sized number of agents ( 3 to 10^23 but usually few hundred)
• Intelligent (rules following) and adaptive
• Local information (no global information, just your immediate neighbors)
• Emergent patterns from very simple local rules
• Complex systems include Stock markets, road traffic networks, evolutionary
ecosystems, supermarkets national economies, health care delivery systems,
communications networks, insurance industry.
Suresh Sood 2017 5

6. Why Swarms in 2017?
1. Amazon Package Shipments via Robots (December 2014)
2. Perdix UAV Swarm Demo (October 2016)
3. Intel 500 Guinness Record (November 2016)
4. Navy Autonomous Swarmboats (December 2016)
5. First Prime Air Delivery in UK (December 2016)
Suresh Sood 2017 6

7. Swarm Characteristics
(Adapted from Spear and Spear 2014)
• Fully distributed no central control (Emergent behaviour)
• Self organisation
• Robustness (A Predator UAV costs USD4.5m)
• Self-repair
• Scalability
• Noise tolerance
• Local communication Suresh Sood 2017 7

8. Complex Self Organising Systems- School of Fish
3 Rule Model of Self-Organization for a school of fish by Craig Reynolds (1986)
1.Separation
keep a minimum distance from your neighbors
2.Alignment
steer in the average direction of your neighbors
3.Cohesion
steer toward the average position of your neighbors
Reference : http://www.red3d.com/cwr/boids/Suresh Sood 2017 8

9. Drowsy Driver Detection
Anomalies?
Lateral
Drift? Micronods? Large
Motions?
Stillness?
Drowsiness
(Surprise)
Suresh Sood 2017 9
No single indicator is sufficiently indicative to trigger an alert. But together there is enough information.

10. Terrorism Detection
Contact with
other
suspects
Last-
minute
ticket
purchase
Financial
discrepancies
Passport
irregularities
Prior
Criminal
Record
Terrorism Event
(Surprise)
Suresh Sood 2017 10
A similar approach can identify the convergence of terrorism-related activities

11. Suresh Sood 2017 11
https://www.amazon.jobs/en/teams/prime-air?base_query=&loc_query=&job_count=10&result_limit =10&sort= relevant&team _category%5B%5D=prime-air&cache, last viewed 30 January 2017.

12. Swarm Jobs of Today & Tomorrow
• Swarm Captain
• Swarm Technician
• Swarm Manager
• Swarm Co-ordinator
• Swarm Artist
• Swarm Logistics Commander
• Swarm Modeler
• Swarm Community Manager of Swarm Tactics
Suresh Sood 2017 12