Swarm Intelligence The Method Behind the Mobs Robert

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

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

What are the competing paradigms? CONJUNCTIVE Approach Do this 1 and this 2 and this 3 and this 4 and this 5 to get that. Result: Highly complex and brittle design. Loose this 4 and your system can fail. Conjunctive statement: 3

What are the competing paradigms? DISJUNCTIVE Approach (Do this 1 to get that ) or (Do this 2 to get that ) or (Do this 3 to get that ) or (Do this 4 to get that ) Result: Highly robust and fault tolerant design. Loose this 4 and you’re still in business. Disjunctive statement: 4

What are the competing paradigms? Is… DISJUNCTIVE = CONJUNCTIVE? Is… (Do this 1 to get that ) or (Do this 2 to get that ) or (Do this 3 to get that ) or (Do this 4 to get that ) = (Do this 1 and this 2 and this 3 and this 4 ) to get that. ? ? ? In a Boolean sense, 5

Disjunctive vs. Conjunctive Disjunctive reasoning sometimes referred to as “The Combs Method”* Examples of Complex Disjunctive Systems 1. Swarms: Insects & People 2. Your Body 3. Animal motor functions 4. 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. 6

DR vs. CR Scorecard Property Conjunctive Reasoning (CR) Disjunctive Reasoning (DR) Scalability Exponential Linear Plasticity Rigid Plastic Coupling High Low High For low order systems, CR most closely parallels human cognitive inference. . For complex systems, DR most closely parallels human cognitive inference. 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. Robustness Fault Tolerance Cognitive Parallel & Distributed Processing Ability 7

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

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

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

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

Disjunctive Symbiogenetic Design design of BALANCED PERSON 12

Disjunctive Symbiogenetic Design BALANCED PERSON WALKING: Human walking is controlled falling CONTRAST CONJUNCTIVE BIPEDS: 1. ONE FOOT ALWAYS ON THE GROUND. 2. They’ll never run 13

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. Time. Scales. org ) • Work with JPL for NASA missions applications. 14

Homogeneous Disjunctive Systems: Swarm Intelligence 15

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

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, Mc. Graw Hill, and Capital One, to develop more efficient ways to schedule factory equipment, divide tasks among workers, organize people , and even plot strategy. ” 17

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. ” 18

Plants and Distributed Computing cactus leaf cocklebur • • Leaves have openings called stomata that open wide to let CO 2 in, but close up to prevent precious water vapor from escaping. Plants attempt to regulate their stomata to take in as much CO 2 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). 19

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

Application: Fiction 21

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

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

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? 24

Looking for Your Lost Pet Turtle Under a Lamppost Multi-Agent searching in the presence of sensor range inhomogeneity. Tradeoffs: • Easier to look under lamppost • Want to look uniformly in around the area. Pareto Optimization (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. 25

A Simple Disjunctive Extension Multi-Agent Criteria: Uncover important search area in the presence of sensor range inhomogeneity Antecedents: Important Parameters: 1. Distance from Unexplored Area 2. Location of Newly Discovered area 3. Distance of Nearest Agent 4. Radius Diminishment Consequents: • Constraints: Velocity Components 1. In direction of new discovery 2. In direction of unexplored area 1. Information is local, or, 2. Information obtained from stygmergy. 3. Away from nearby agents 4. In direction of diminished radius 26

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. 27