swarm intelligence: the method behind the mobs
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B io- E ngineering for the E xploration of S pace. 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 - PowerPoint PPT PresentationTRANSCRIPT
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Swarm Intelligence: The Method Behind the Mobs
Robert J. Marks IIDistinguished Professor
of Electrical & Computer Engineering,Baylor UniversityBio-Engineering for
the Exploration of Space
NASA Office of Biological and Physical ResearchProgram Review
California Institute of TechnologyDecember 17-18, 2003
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Role within BEES
(highlight)
Evolution&
Development
ComplexSystems
“Embryonic”Systems
Biosciences
Bioengineering
Jay Hove“Embryonic Heart”
Flavio Noca“Nanofluidics”
Eric Mjolsness“Morphogenesis &
Statistical Inference”
Chris Adami“EVO-DEVO”
Michael Dickinson“Aerodynamics and Flight
Behavior of Insects”
Payman Arabshahi“Distributed Communication,
Control, and Navigation Systems”
Robert J. Marks II“Swarm Intelligence and
Collective Behavior”
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What are the competing paradigms?
CONJUNCTIVE Approach
Do this1 and this2 and this3 and this4 and this5 to get that.
Result: Highly complex and brittle design. Loose this4
and your system can fail.
Conjunctive statement:
CAj
j
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What are the competing paradigms?
DISJUNCTIVE Approach
(Do this1 to get that ) or (Do this2 to get that ) or (Do this3 to get that ) or (Do this4 to get that )
Result: Highly robust and fault tolerant
design. Loose this4 and you’re still in business.Disjunctive statement:
CAjj
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What are the competing paradigms?
Is… DISJUNCTIVE = CONJUNCTIVE?
Is…
(Do this1 to get that ) or (Do this2 to get that ) or (Do this3 to get that ) or (Do this4 to get that )
= (Do this1 and this2 and this3 and this4 ) to get that.
???
CAjj CA
jj
In a Boolean sense,
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Disjunctive vs. Conjunctive
Disjunctive reasoning sometimes referred to as
“The Combs Method”*
Examples of Complex Disjunctive SystemsExamples of Complex Disjunctive Systems
1.1. Swarms: Insects & PeopleSwarms: Insects & People
2.2. Your BodyYour Body
3.3. Animal motor functionsAnimal motor functions
4.4. Genomic symbiogenesisGenomic symbiogenesis William E. Combs
* Earl Cox, The Fuzzy Systems Handbook, Academic Press/ Morgan Kaufman.
• 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.
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DR vs. CR Scorecard
Property Conjunctive Reasoning (CR) Disjunctive Reasoning (DR)
Scalability Exponential Linear
Plasticity Rigid Plastic
Coupling High Low
Robustness Low High
Fault Tolerance
Low High
Cognitive Parallel
For low order systems, CR most closely parallels human cognitive inference..
For complex systems, DR most closely parallels human cognitive inference.
Parallel & Distributed Processing
Ability
Parallel and distributed processing increases the complexity of most properties.
DR is readily applied to distributed processing as each unit has a relationship with the consequent that is independent of the other units.
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Applied Symbiogenesis: A Disjunctive Process
System
ForcedSymbioticAdaptation
DisjunctivelyAddend
EvolvedSystem
New
Feature
Acquiring Genomes: A Theory of the Origins of Species by Lynn Margulis and Dorion Sagan
Heterogeneous Disjunctive Design:
Genomic Programming
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Impose:
Forced symbiotic adaptation
Designing a Running Man
Ball
Pressure
joint
If…
The ball pressure is
high
Then…
Rotate joint CW
OR
If…
The heal pressure is
high
Then…
Rotate joint CCW
Heal
Pressure
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design of Sagittal balance.
System
ForcedSymbioticAdaptation
DisjunctivelyAddend
EvolvedSystem
New Feature
Disjunctive Symbiogenetic Design
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design of FRONTAL balance.
System
ForcedSymbioticAdaptation
DisjunctivelyAddend
EvolvedSystem
New Feature
Disjunctive Symbiogenetic Design
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design of BALANCED PERSON
Disjunctive Symbiogenetic Design
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BALANCED PERSON WALKING:
Human walking is controlled falling
Disjunctive Symbiogenetic Design
CONTRAST CONJUNCTIVE BIPEDS:
1. ONE FOOT ALWAYS ON THE GROUND.
2. They’ll never run
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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.
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Homogeneous Disjunctive Systems: Swarm Intelligence
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Applications: Warfare & Game Theory
Aviation Weekly , Sept 29, 2003
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Applications: Business“Swarm Intelligence: A Whole New Way to Swarm Intelligence: A Whole New Way to
Think About Business”Think About Business”
Harvard Business Review, May 2002Harvard Business Review, May 2002
Using swarm intelligence optimization, Using swarm intelligence optimization, Southwest Airlines slashed freight transfer Southwest Airlines slashed freight transfer
rates by as much as 80%.rates by as much as 80%.
““Similar research into the behavior of Similar research into the behavior of social insects has helped … Unilever, social insects has helped … Unilever,
McGraw Hill, and Capital One, to develop McGraw Hill, and Capital One, to develop more efficient ways to schedule factory more efficient ways to schedule factory
equipment, divide tasks among workers, equipment, divide tasks among workers, organize people , and even plot strategy.”organize people , and even plot strategy.”
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Applications: Telecommunications
Scientific American, March 2000Scientific 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.”
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Plants and Distributed Computing
• 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).
• 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.”
cactus leaf cocklebur
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Applications: OptimizationApplications: Optimization
Particle Swarm: An Particle Swarm: An (enormously effective!) (enormously effective!)
multi- agent multi- agent optimization optimization
algorithm based on the algorithm based on the biomimetics of bird biomimetics of bird
flight.flight.
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Application: FictionApplication: Fiction
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What is Swarm Intelligence?What is Swarm Intelligence?Simple Rules for Multiple Agents.Simple Rules for Multiple Agents.
Indy 500’s RulesIndy 500’s Rules–Drive FastDrive Fast
–Turn LeftTurn Left
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Another rule…Another rule…
– Drive FastDrive Fast– Turn LeftTurn Left– Don’t hit stuffDon’t hit stuff
• Emergent BehaviorEmergent Behavior– Competition- Winning!Competition- Winning!
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The Dumb Termite Clearing Wood
RULESRULES• Run around randomly until you bump Run around randomly until you bump
into a piece of wood.into a piece of wood.• Pick it up.Pick it up.• Run around randomly until you bump Run around randomly until you bump
into a piece of wood.into a piece of wood.• Put it down.Put it down.• Repeat forever.Repeat forever.Q: What does this do?Q: What does this do?
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Looking for Your Lost Pet Turtle Under a Lamppost
Multi-Agent searching in the presence of sensor range inhomogeneity.
Tradeoffs:Tradeoffs:• Easier to look Easier to look
under lamppost under lamppost • Want to look Want to look
uniformly in uniformly in around the area.around the area.
Pareto Optimization Pareto Optimization (Efficient Frontier)(Efficient Frontier)
Agent Rule:
1. Diminishing Radius Momentum – if the visible
radius decreases, the momentum is increased.
2. Don’t tred on me.
Emergent Behavior: A parameter to tune between the optimization criteria.
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A Simple Disjunctive ExtensionA Simple Disjunctive Extension
Multi-Agent Criteria: Uncover important search area in the presence of sensor range inhomogeneity
Antecedents:Important Parameters:1. Distance from
Unexplored Area2. Location of Newly
Discovered area3. Distance of Nearest
Agent4. Radius
Diminishment
• Constraints:1. Information is
local, or,2. Information
obtained from stygmergy.
Consequents:
Velocity Components
1. In direction of new discovery
2. In direction of unexplored area
3. Away from nearby agents
4. In direction of diminished radius
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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.