1 / 20

From swarming to collaborative filtering.

Explore the study of complex systems in nature and learn to model behaviors such as branching in plants, Fibonacci numbers, flocking behavior, and ant trails. Discover the principles behind emergent behaviors and the bottom-up methodology of analyzing simple rules that produce complex behavior.

roney
Download Presentation

From swarming to collaborative filtering.

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. From swarming to collaborative filtering. http://www.csml.ucl.ac.uk/images/Netflix_Prize.jpg

  2. Informatics: a possible parsing Health- HCID Security Geo- Data Mining Bio- Data & Search Social Informatics Complex Systems • towards problem solving • beyond computing • into the natural and social • synthesis of information technology Music- Chem-

  3. b b b a a a b a b b a a b a b Psilophyta/Psilotum Let’s Observe Nature! What do you see? • Plants typically branch out • How can we model that? • Observe the distinct parts • Color them • Assign symbols • Build Model • Initial State: b • b -> a • a -> ab • Doesn’t quite Work! a b

  4. Complex systems approach: looking at nature • A complex system is any system featuring a large number of interacting components (agents, processes, etc.) whose aggregate activity is nonlinear • not derivable from the summations of the activity of individual components • Network identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the components • Genetic networks, Immune networks, Neural networks, Social insect colonies, Social networks, Distributed Knowledge Systems, Ecological networks • Bottom-up Methodology • Collections of simple units interacting to form a more complex hole • Study of Simple Rules that Produce Complex Behavior • Discovery of Global Patterns of behavior

  5. b b b a a a b a b b a a b Psilophyta/Psilotum a b What about our plant? • An Accuratemodel requires • Varying angles • Varying stem lengths • Randomness • The Fibonacci Model is similar • Sneezewort: a b

  6. Fibonacci Numbers! • Rewritingproduction rules • Initial State: A • A -> B • B -> AB • n=0 : A • n=1 : B • n=2 : AB • n=3 : BAB • n=4 : ABBAB • n=5 : BABABBAB • n=6 : ABBABBABABBAB • n=7 : BABABBABABBABBABABBAB • The length of the string is the Fibonacci Sequence • 1 1 2 3 5 8 13 21 34 55 89 ... • Fibonacci numbers in Nature • Livio (2003) The Golden Ratio: The Story of PHI, the World's Most Astonishing Number

  7. Another example: flocking in nature • Flocking occurs when large groups of animals of the same species form aggregates that behave like a coherent, single entity • Herds, flocks, schools, swarms, humans • Properties: • Collectiveflight, migration, foraging, “drafting” • Coherence: aggregate has its own distinguishable system behavior and form • Adaptive: behavior of aggregate responds and adapts to external events (predators) • Coordination: behavior of individuals seems to be indicative of central controlor symbolic/long-range communication, but isn’t

  8. How to model flocking behavior? • Describing properties of aggregate behavior will only go so far: • Study shapes of aggregate • Situations in which it occurs • Dynamics, features of behavior • Biologists fixing radios? • Lessons from complex systems: • Complex systems behavior: not derivable from the summations of the activity of individual components • Network identity: Components form aggregate structures or functions that requires more explanatory devices than those used to explain the components ~ emergence • Bottom-up Methodology: • Collections of simple units interacting to form a more complex hole • Study of Simple Rules that Produce Complex Behavior Parrish(2002) – Self-organized fish schools

  9. Models of flocking behavior • Boids: Craig Reynolds “Flocks, Herds and schools”, SIGGRAPH 21(4),1987 • Visual model of bird flocks • Lack of centralized control • Lack of symbolic communication • General approach: Local computation, i.e. each individual maximizes: • Collision avoidance: steer away from impact • Velocity matching: match speed of neighboring birds • Flock centering: steer towards perceived flock center • Flock behavior = emerges from interactions of large groups of such construed individuals

  10. Ant trails: emergent organizaton driven by communication • Problem: optimize location and extraction of food source • Lack of centralized control • Lack of symbolic communication • General modeling approach: • Local computation leads to higher order emergent computation • Walk algorithm probabilistic, but biased by pheromone concentraion • Ants leave pheromone trail when food is found • Pheromone evaporates with time • Find shortest path • Note: • ~ greedy algorithm: hill-climbing on trail strength leads to adaptive, collective behavior • Approaches to address traveling salesman problem: BIOS group: S. Kaufmann (Santa Fe), see also M. Dorigo(2006) Ant Colony Optimization-IEEE Computational Intelligence Magazine for overview

  11. Probabilistic cleaning: ants • Very simple rules for colony clean up • Pick dead ant. if a dead ant is found pick it up (with probability inversely proportional to the quantity of dead ants in vicinity) and wander. • Drop dead ant. If dead ants are found, drop ant (with probability proportional to the quantity of dead ants in vicinity) and wander. See Also: J. L. Deneubourg, S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chretien. “The Dynamics of Collective Sorting Robot-Like Ants and Ant-Like Robots”. From Animals to Animats: Proc. of the 1st Int. Conf. on Simulation of Adaptive Behaviour. 356-363 (1990). Figure by Marco Dorigo in Real ants inspire ant algorithms

  12. Ant-inspired robots • Rules (Becker et al, 1994) • Move: with no sensor activated move in straight line • Obstacle avoidance: if obstacle is found, turn with a random angle to avoid it and move. • Pick up and drop: Robots can pick up a number of objects (up to 3) • If shovel contains 3 or more objects, sensor is activated and objects are dropped. Robot backs up, chooses new angle and moves. • Results in clustering • Theprobabilityofdroppingitemsincreaseswithquantityofitems in vicinity Figure from R Beckers, OE Holland, and JL Deneubourg [1994]. “From local actions to global tasks: Stigmergy and collective robotics”. In Artificial Life IV.

  13. becker et al experiments

  14. Luc Steels et al: ant algorithms http://www.youtube.com/watch?v=93LwvuxDbfU

  15. Adaptive information systems Swarm Smarts. 78. Scientific American March 2000. ERIC BONABEAU Johan Bollen (1994): adaptive hypertext systems

  16. Shameboy Plastic Operator [Shameboy, Plastic Operator, Figurine,…] Buyer 1 [1, 1, 0, 0, 0,…] Buyer 2 [1, 0, 0, 0, 0,…] Recommender systems: general principles • People ~ n-dimensional vectors • Person = { CD/book purchases, DVDs rented, …} • Vector is a representation of consumer. Entries can be weighted (TFIDF etc) • “Vector Space Model” • Calculate similarity of users: • Correlation of user vectors • Cosine similarity • Group consumers according to similarity: clustering • Similar users: discrepancies in vectors are recommendations • Used for all sorts of applications • Similar problem to “bad of words” • Multiple user personalities? • Orthogonality? • Same = better?? Angle: Consumer Similarity

  17. Tracking scientists (they are people too!) http://informatics.indiana.edu/jbollen/PLosONEmap André Skupin Borner/Ketan (2004) PNAS 101(1) Highly recommended: http://www.scimaps.org/

  18. We’re all ants now? • User vectors: • Represent individual trail/exploration in n-dimension information space • Recommender systems: • bias probabilistic exploration paths of users based on others’ actions • Higher probability of following existing trails • Analogy: • Set of user vectors + recommender system ~ ant trails • Solving traveling salesman in n dimensions? ;-) • Modeling fads, hypes, flashcrowds in cyberspace, self-fulfilling prophecies, but also long tail effects, more optimized exploration of information space? • Which features of recommender systems promote either of the above? • Cf. youtube.com: “other users are watching” vs. batch-processed recommendations documents recommender interface

  19. Readings: Questions: - Atlantic (2009) “Is google making us stupid”: As a scientist how would you falsify Carr’s theory that “google is changing the way we think”? Has google changed the way you think? (notions of sampling, plagiarism, etc) - Bettencourt (2008), PNAS: The proposed model results in a scenario in which cities undergo cycles of expansion followed by crisis as a result of the exhaustion of resources. Cycle length shortening with each generation. Speculate: where does this process “break”? What’s a way out?

  20. Next week readings Gouth (2009) Training for Peer Review. Science Signaling 2 (85), tr2. [DOI: 10.1126/scisignal.285tr2] MONASTERSKY (2005) The number that is devouring science. Chronicle of higher education, Section: Research & Publishing Volume 52, Issue 8, Page A12 Eysenbach G, 2006 Citation Advantage of Open Access Articles. PLoSBiol 4(5): e157. doi:10.1371/journal.pbio.0040157 Lance Fortnow (2009) Time for Computer Science to Grow Up. Communications of the ACM, august, 52(8)doi:10.1145/1536616.1536631

More Related