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Swarm Intelligence: The Method Behind the Mobs PowerPoint Presentation
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Swarm Intelligence: The Method Behind the Mobs

Swarm Intelligence: The Method Behind the Mobs

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Swarm Intelligence: The Method Behind the Mobs

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  1. Bio-Engineering for the Exploration of Space BEES Swarm Intelligence: The Method Behind the Mobs Robert J. Marks II Distinguished Professor of Electrical & Computer Engineering, Baylor University NASA Office of Biological and Physical Research Program Review California Institute of Technology December 17-18, 2003

  2. Evolution & Development “Embryonic” Systems Complex Systems Eric Mjolsness “Morphogenesis & Statistical Inference” Jay Hove “Embryonic Heart” Michael Dickinson “Aerodynamics and Flight Behavior of Insects” Biosciences Flavio Noca “Nanofluidics” Chris Adami “EVO-DEVO” Robert J. Marks II “Swarm Intelligence and Collective Behavior” Bioengineering Payman Arabshahi “Distributed Communication, Control, and Navigation Systems” Role within BEES (highlight)

  3. What are the competing paradigms? CONJUNCTIVE Approach Do this1andthis2andthis3andthis4andthis5to getthat. Result: Highly complex and brittle design. Loose this4 and your system can fail. Conjunctive statement:

  4. What are the competing paradigms? DISJUNCTIVE Approach (Do this1to getthat ) or (Do this2to getthat ) or (Do this3to getthat ) or (Do this4to getthat ) Result: Highly robust and fault tolerant design. Loose this4 and you’re still in business. Disjunctive statement:

  5. What are the competing paradigms? Is… DISJUNCTIVE = CONJUNCTIVE? Is… (Do this1to getthat ) or (Do this2to getthat ) or (Do this3to getthat ) or (Do this4to getthat ) = (Do this1andthis2andthis3andthis4 ) to getthat. ??? In a Boolean sense,

  6. Disjunctive vs. Conjunctive • Disjunctive reasoning sometimes referred to as “The Combs Method”* • Examples of Complex Disjunctive Systems • Swarms: Insects & People • Your Body • Animal motor functions • Genomic symbiogenesis William E. Combs • J. J. Weinschenk, W. E. Combs, R. J. Marks II, “Avoidance of rule explosion by mapping fuzzy systems to a disjunctive rule configuration,” IEEE Int’l Conference on Fuzzy Systems, St. Louis, MO, 2003, pp 43-48. • J. J. Weinschenk, R. J. Marks II, W. E. Combs, “Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks,” Proc. IEEE Intl’ Joint Conf. on Neural Networks, Portland, OR, 2003, pp. 2995-3000. • J. J. Weinschenk, W. E. Combs, R. J. Marks II, “On the avoidance of rule explosion in fuzzy inference engines,” Submitted to IEEE Trans. Fuzzy Systems, November 12, 2003. * Earl Cox, The Fuzzy Systems Handbook, Academic Press/ Morgan Kaufman.

  7. DR vs. CR Scorecard

  8. Applied Symbiogenesis: A Disjunctive Process Disjunctively Addend NewFeature System Heterogeneous Disjunctive Design: Genomic Programming Forced Symbiotic Adaptation Evolved System Acquiring Genomes: A Theory of the Origins of Speciesby Lynn Margulis and Dorion Sagan

  9. Designing a Running Man If… The ball pressure is high Then… Rotate joint CW If… The heal pressure is high Then… Rotate joint CCW OR joint Ball Pressure Heal Pressure Impose: Forced symbiotic adaptation

  10. Disjunctive Symbiogenetic Design  design of Sagittal balance. Disjunctively Addend New Feature System Forced Symbiotic Adaptation Evolved System

  11. Disjunctive Symbiogenetic Design design of FRONTAL balance. Disjunctively Addend  New Feature System Forced Symbiotic Adaptation Evolved System

  12. Disjunctive Symbiogenetic Design design of BALANCED PERSON 

  13. Disjunctive Symbiogenetic Design • CONTRAST CONJUNCTIVE BIPEDS: • ONE FOOT ALWAYS ON THE GROUND. • They’ll never run BALANCED PERSON WALKING: Human walking is controlled falling

  14. Five Year Plan • Formalize disjunctive paradigm as applied to symbiogenic processing. • Emulate symbiogenic development of the “walking man”. • Generate a cute name for walking man, like “Symbio Sam” or “Disjunctive Dick”. • Download emulation into biped robot and force physical symbiotic adaptation. (Baylor Time Scale Robotics Lab - www.TimeScales.org ) • Work with JPL for NASA missions applications.

  15. Homogeneous Disjunctive Systems: Swarm Intelligence

  16. Applications: Warfare & Game Theory Aviation Weekly , Sept 29, 2003

  17. Applications: Business “Swarm Intelligence: A Whole New Way to Think About Business” Harvard Business Review, May 2002 Using swarm intelligence optimization, Southwest Airlines slashed freight transfer rates by as much as 80%. “Similar research into the behavior of social insects has helped … Unilever, McGraw Hill, and Capital One, to develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people , and even plot strategy.”

  18. Applications: Telecommunications Scientific American, March 2000 “Several companies are [using swarm intelligence] for handling the traffic on their networks. France Télécom and British Telecommunications have taken an early lead in applying antbased routing methods to their systems… The ultimate application, though, may be on the Internet, where traffic is particularly unpredictable.”

  19. Plants and Distributed Computing • Leaves have openings called stomata that open wide to let CO2 in, but close up to prevent precious water vapor from escaping. Plants attempt to regulate their stomata to take in as much CO2 as possible while losing the least amount of water. • “[The] results are consistent with the proposition that a plant solves its optimal gas exchange problem through an emergent, distributed computation performed by its leaves.” • Patches of open or closed stomata sometimes move around a leaf at constant speed • “Under some conditions, stomatal apertures become synchronized into patches that exhibit richly complicated dynamics, similar to behaviors found in cellular automata that perform computational tasks.” “Our values are statistically indistinguishable from those of the same correlations found in the dynamics of automata that compute.” • Peak, D. A., West, J. D., Messinger, S. M & Mott, K. A. Evidence for complex, collective dynamics and emergent, distributed computation in plants. Proceedings of the National Academy of Sciences USA, 101, 918 - 922, (2004). cactus leaf cocklebur

  20. Applications: Optimization Particle Swarm: An (enormously effective!) multi- agent optimization algorithm based on the biomimetics of bird flight.

  21. Application: Fiction

  22. What is Swarm Intelligence?Simple Rules for Multiple Agents. Indy 500’s Rules • Drive Fast • Turn Left

  23. Another rule… • Drive Fast • Turn Left • Don’t hit stuff • Emergent Behavior • Competition- Winning!

  24. The Dumb Termite Clearing Wood RULES • Run around randomly until you bump into a piece of wood. • Pick it up. • Run around randomly until you bump into a piece of wood. • Put it down. • Repeat forever. Q: What does this do?

  25. Looking for Your Lost Pet Turtle Under a Lamppost Multi-Agent searching in the presence of sensor range inhomogeneity. • Agent Rule: • Diminishing Radius Momentum – if the visible radius decreases, the momentum is increased. • Don’t tred on me. • Emergent Behavior: A parameter to tune between the optimization criteria. Tradeoffs: • Easier to look under lamppost • Want to look uniformly in around the area. Pareto Optimization (Efficient Frontier)

  26. A Simple Disjunctive ExtensionMulti-Agent Criteria: Uncover important search area in the presence of sensor range inhomogeneity • Consequents: • Velocity Components • In direction of new discovery • In direction of unexplored area • Away from nearby agents • In direction of diminished radius • Constraints: • Information is local, or, • Information obtained from stygmergy. Antecedents: Important Parameters: • Distance from Unexplored Area • Location of Newly Discovered area • Distance of Nearest Agent • Radius Diminishment

  27. Five Year Plan • Formalize disjunctive swarm paradigm. • Applications • NASA • Communications • Space Robotics • Air Military: Swarming Drones • Navy: Search Patterns • Work with JPL for other NASA missions applications.