Swarm Swarm Intelligence Intelligence Systems Systems - PowerPoint PPT Presentation

Swarm Swarm Intelligence Intelligence Systems Systems —————————————— —————————————— Christian Jacob Christian Jacob jacob@cpsc.ucalgary.ca Department of Computer Science University of Calgary

Cellular Cellular Automata Automata Global Effects from Local Effects from Local Global Rules Rules

Cellular Automata Cellular Automata ✦ The CA space is a lattice of cells with a The CA space is a lattice of cells with a ✦ particular geometry. particular geometry. ✦ Each cell contains a variable from a Each cell contains a variable from a ✦ limited range (e.g., 0 and 1). limited range (e.g., 0 and 1). ✦ All cells update synchronously. All cells update synchronously. ✦ ✦ All cells use the same updating rule, All cells use the same updating rule, ✦ depending only on local relations. depending only on local relations. ✦ Time advances in discrete steps. Time advances in discrete steps. ✦ 3

One-dimensional finite CA architecture One-dimensional finite CA architecture ✦ K = 5 local K = 5 local ✦ connections connections per cell per cell time ✦ Synchronous Synchronous ✦ update in discrete update in discrete time steps time steps A. Wuensche: The Ghost in the Machine, Artificial Life III, 1994. 4

Cellular Automata: Cellular Automata: Local Rules — Global Effects Local Rules — Global Effects 5

2-D CA: 2-D CA: Emergent Pattern Formation Emergent Pattern Formation in Excitable Media in Excitable Media Neuron excitation Hodgepodge Neuron excitation Hodgepodge Neuron excitation (relaxed) Neuron excitation (relaxed) 9

Cellular Automata Swarm Systems Random Boolean Networks Classifier Systems 10

Ants Ants Hölldobler & Wilson, 1990

Self-organization Team work Competition ... and Heavy Loads Hölldobler & Wilson, 1990

Ant Foraging Ant Foraging Behaviour Behaviour Learning about Emergent Learning about Emergent System Behaviours Behaviours System

Ant Foreaging Foreaging and Shortest Paths and Shortest Paths Ant Experimental setup for studying ant foreaging behaviour Bonabeau et al., 1999

Shortest Path Discovery Shortest Path Discovery (a) Ants walking between nest (b) An obstacle is placed in the (a) Ants walking between nest (b) An obstacle is placed in the and food sites and food sites middle. middle. (c) Ants turn left or right, while (c) Ants turn left or right, while (d) … and finally the shortest (d) … and finally the shortest droping pheromone ... droping pheromone ... path emerges. path emerges.

Adaptation to Environmental Adaptation to Environmental Changes Changes (a) The newly found shortest path (a) The newly found shortest path (b) Moving the obstacle (c) Discovery of new shortest path (b) Moving the obstacle (c) Discovery of new shortest path

Massively Parallel Parallel Massively Micro Worlds Micro Worlds StarLogo StarLogo Mitchel Resnick (MIT, 1997) (MIT, 1997) Mitchel Resnick

Agent-Based Evolution Agent-Based Evolution ✦ Massive Parallelism Massive Parallelism ✦ ✦ Interacting Agents Interacting Agents ✦ ✦ Cooperation Cooperation ✦ ✦ Competition Competition ✦ ✦ Emergent System Emergent System Behaviour Behaviour ✦

Simulated Simulated Emergent System Behaviour Behaviour Emergent System Ant Foraging Ant Foraging Collective Collective Equidistant Equidistant Randomly Distributed Randomly Distributed Foraging Foraging Food Sites Food Sites Food Sites Food Sites

Simulated Simulated Emergent System Behaviour Behaviour Emergent System Ant Foraging Ant Foraging to look-for-food to find-food if not carrying-food? if (not carrying-food?) [ifelse (ask patch-here [pheromone]) < 0.2 and ask patch-here [food > 0] [right random 40 left random 40] [set-carrying-food? True [set-heading uphill pheromone] ask patch-here [set-food food - 1] forward 1] set-drop-size 35 end right 180 forward 1] end to return-to-nest to find-nest if carrying-food? if carrying-food? and ask patch-here [nest?] [ask patch-here [add-pheromone-drop] [set-carrying-food? False set-drop-size drop-size - 0.6 right 180 forward 1] set-heading uphill nest-scent end forward 1] end

Demo Demo Following Following Behaviour Behaviour

Interactions Interactions among among Social Insects Social Insects

Interactions among Social Insects Interactions among Social Insects ✦ Direct Interactions Direct Interactions ✦ – Food or liquid exchange Food or liquid exchange – – Visual or tactile, or Visual or tactile, or scentuous scentuous contact contact – – Pheromones Pheromones – ✦ Indirect Interactions: Indirect Interactions: Stigmergy Stigmergy ✦ – Individual Individual behaviour behaviour modifies the modifies the – environment (e.g., by putting up signs = signs = environment (e.g., by putting up stigma ), ), which in turn modifies the which in turn modifies the stigma behaviour of other individuals. behaviour of other individuals.

Demo Demo Stigmergy Stigmergy in in Action Action Bonabeau et al., 1999

What to Learn from Ant What to Learn from Ant Colonies as Complex Systems Colonies as Complex Systems ✦ Fairly simple units generate Fairly simple units generate ✦ complicated global behaviour behaviour. . complicated global ✦ “If we knew how an ant colony works, “If we knew how an ant colony works, ✦ we might understand more about how we might understand more about how all such systems work, from brains to all such systems work, from brains to ecosystems.” ecosystems.” (Gordon, 1999) (Gordon, 1999)

Emergence in Complex Systems Emergence in Complex Systems ✦ How do How do neurons neurons respond to each other respond to each other ✦ in a way that produces thoughts? in a way that produces thoughts? ✦ How do How do cells cells respond to each other in a respond to each other in a ✦ way that produces the distinct tissues of way that produces the distinct tissues of a growing embryo? a growing embryo? ✦ How do How do species species interact to produce interact to produce ✦ predictable changes changes, over time, in , over time, in predictable ecological communities? ecological communities? ✦ ... ... ✦

Swarm Systems Swarm Systems Providing New New Insights ... Insights ... Providing

References References Bonabeau, E., Dorigo, M., and Theraulaz, G. (1999). Swarm ✦ Intelligence: From Natural to Artificial Systems . New York, Oxford University Press. Ernst, A. M., ed. (1998). Digest: Kooperation und Konkurrenz, ✦ Heidelberg, Spektrum Akademischer Verlag. Gordon, D. (1999). Ants at Work . New York, The Free Press. ✦ Hölldobler, B., and Wilson, E. O. (1990). The Ants . Cambridge, ✦ MA, Harvard University Press. Nuridsany, C., and Pérennou, M. (1996). Microcosmos: The ✦ Invisible World of Insects . New York, Stewart, Tabori & Chang. Resnik, M. (1997). Turtles, Termites, and Traffic Jams . ✦ Cambridge, MA, MIT Press. Stevens, C. F., et al. (1988). Gehirn und Nervensystem . ✦ Heidelberg, Spektrum Akademischer Verlag.