[PDF] - 1 Self-Organization Self-organization is a process in which PDF Document

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Umeå University Department of Computing Science

Emergent systems

Spring-12 Self-organization, autonomous agents and ant algorithms

http://www.cs.umu.se/kurser/5DV017

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Previous lecture

❒ Nonlinear dynamic systems

❍ The Logistic map

❒ Strange attractors

❍ The Hénon attractor ❍ The Lorenz attractor

❒ Producer-consumer dynamics

❍ Equation-based modeling ❍ Individual-based modeling

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Outline for this lecture

❒ Self-Organization ❒ Autonomous Agents ❒ Real Ants ❒ Virtual Ants ❒ Ant Algorithms

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization

❒ ”Self-organization is a process in which

pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the

system. Moreover, the rules specifying

interactions among the system’s components are executed using only local information, without reference to the global pattern.” – Camazine et al, p. 8

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization

❒ Pattern

❍ A particular, organized arrangement of objects

in space or time ❒ Interactions

❍ Based on local information only - no global

information

❍ Physical laws ❍ Genetically controlled properties of the

components

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization - Ingredients

❒ Positive feedback

❍ Activity amplification

❒ Negative feedback

❍ Activity balancing

❒ Amplification of random fluctuations ❒ Multiple interactions

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization - Information

❒ Signals

❍ Stimuli shaped by natural selection specifically

to convey information ❒ Cues

❍ Stimuli that convey information only

incidentally ❒ Gathered from one’s neighbors

❍ Stimuli-response, simple behavioral rules of

thumb ❒ Gathered from work in progress

❍ Stigmergy ❍ Random fluctuation and chance heterogeneities

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization - Characteristics

❒ Dynamic systems ❒ Exhibit emergent properties

❍ Attractors ❍ Multistability ❍ Bifurcations ❍ Parameter tuning ❍ Environmental factors

❒ Adaptive systems ❒ Different patterns may result from the

same mechanism

❒ Simple rules, complex patterns

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Self-Organization – Alternatives

❒ Central leader

❍ Need effective communication and cognitive

abilities ❒ Blueprints

❍ Must be stored

❒ Recipes

❍ Hinders flexibility

❒ Templates

❍ Must be available

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Stigmergy

❒ A recursive control system ❒ Effective for coordination in space and

time

❒ A sequence of qualitatively different

stimulus-response behaviors

❒ Two types:

❍ Qualitative stigmergy ❍ Quantitative stigmergy

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Stigmergy - Advantages

❒ Permits simpler agents ❒ Decrease direct communication between

agents

❒ Incremental improvement ❒ Flexible, since when environment changes,

agents respond appropriately

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Autonomous Agent

❒ ”a unit that interacts with its environment

(which probably consists of other agents)

❒ but acts independently from all other

agents in that it does not take commands from some seen or unseen leader,

❒ nor does an agent have some idea of a

global plan that it should be following.”

Flake, p. 261

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Real Ants

❒ Imagine if artificial systems could do the

things ants can do?

❒ Why ants?

❍ Amazonas: 30% of biomass is ants/termites ❍ Amazonas: dry weight of social insects is four

times that of other land animals

❍ Earth: ~10% of total biomass (like humans)

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Army Ants

❒ 100 000s in colony ❒ Create temporary

”bivouacs”

❒ Act like unified entity

(Pictures from AntColony.org) Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Fungus-Growing Ants

❒ "A Leaf Cutter Colony

can strip the tallest

f trees in a single
day. Equivalent

consumption of a full grown cow in the same time!"

❒ ”Cultivate” fungi

underground

❒ Fertilize with

compost from chewed leaves

(Pictures from AntColony.org)

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Fungus Cultivator Nest

(Picture from AntColony.org) Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Langton’s Virtual Ants

❒ Grid with white or black squares ❒ Virtual ants can face N, S, E, W ❒ Behavioral rule:

❍ Take a step forward ❍ if on a white square then paint it black and turn

90º right

❍ if on a black square then paint it white and turn

90º left

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Virtual Ants - Example

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Virtual Ants – Time Reversibility

❒ Virtual ants are time-reversible ❒ But, time-reversibility does not imply

global simplicity

❒ Even a single virtual ant interacts with its

wn prior history

❒ Demonstration

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Virtual Ants - Conclusion

❒ Even simple, reversible local behavior can

lead to complex global behavior

❒ Such complex behavior may create

structures as well as apparently random behavior

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Harvester Ants

❒ Find shortest path to

food

❒ Prioritize food sources

based on distance and ease of access

(Picture from The Texas A&M University System)

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Emergent Systems, Jonny Pettersson, UmU

Adaptive Path Optimization

❒ How do they do it?

❍ Deposit pheromone

Can be several different
Can detect gradients and

frequency of contact

❍ Does not follow trails

perfectly

Exploration

❍ Feedback reinforces

”good” trails

28/1 - 13 Emergent Systems, Jonny Pettersson, UmU

Adaptive Path Optimization

❒ Adaptive significance

❍ Chooses the “best” food source ❍ Chooses the shortest trail ❍ Adapt grade of exploration to the quality of the

food source

❍ Collective decision making

❒ Observations at trail formation

❍ If equal length, one is chosen randomly ❍ Sometimes a longer/worse is selected ❍ Pros

Easier to follow
Easier to protect
Safer

28/1 - 13 Emergent Systems, Jonny Pettersson, UmU

Formation of trails

❒ Find trail

❍ ”Forager” deposit pheromone ❍ How and when pheromone is deposited varies ❍ Other follows trail ❍ Pheromone also act as orientation aid

❒ Follow trail

❍ PL = ((CL + k)h) /((CL + k)h + (CR + k)h) ❍ CL, CR : concentration of pheromone ❍ k, h: to fit the model to experimental data

❒ Pheromone evaporation

❍ Trails can last several hours to several months ❍ The lifetime of pheromone

average 30-60 min, but can be detected much longer

28/1 - 13

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Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Ant Algorithms

❒ Basic ingredients for all ant based

algorithms

❍ Positive feedback

Reinforce good solutions
Reinforce good parts of solutions
Through pheromone accumulation

❍ Negative feedback

Avoid too early convergence
Through pheromone evaporation

❍ Cooperation

Parallel search
Through more ants and through pheromone trails

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Ant Algorithms

❒ Ant colony optimization (ACO) ❒ Developed in 1991 by Dorigo (PhD

dissertation) in collaboration with Colorni and Maniezzo

Emergent Systems, Jonny Pettersson, UmU 28/1 - 13

Summary

❒ Self-Organization ❒ Autonomous Agents ❒ Real Ants ❒ Virtual Ants ❒ Ant Algorithms