Emergence in Artificial Societies

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Emergence in Artificial Societies

• Evolving Communication and Cooperation in a
  Sugarscape world
• by Pieter Buzing

2
Plan

• What are artificial societies?
• Sugarscape
• Our goal communication and cooperation
• Our model VUScape
• Setup
• Results
• Conclusions

3
Artificial Society?

• Multi agent system
• 2 levels autonomous parts, behaviour of whole
• AS more control over agents and world
• Artificial life
• emergent behaviour
• AS important role for individual
• Agent based simulation
• AS no problem to solve, like optimization
• Social modeling
• interactions of agents effects individual goals
  on population

4
Sugarscape

• Epstein Axtell, 1996
• Torus shaped world 50 x 50 cells
• Sugar resources 0 - 4
• Agents looking for food
• Evolution

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Sugarscape

• Agent Internals
• Age 0 - 100
• Vision 1 - 6
• Sugar Amount 0 - inf
• Metabolism 1 - 4
• Gender m/f

• Agent Actions
• Die
• Move
• Harvest
• Metabolise
• Reproduce

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Sugarscape

• Reproduction rule
• Agent has gender male or female
• Metabolism and vision are genetic!
• Parents ltm, vgt X ltM, Vgt
• Child ltm,Vgt, ltM,vgt, ltm,vgt or ltM,Vgt
• Child inherits half of parents sugar

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Sugarscape

• Agents will tend to move towards sugarhills
• Agents with high vision are better off
• Agents with low metabolism are better off

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Our Goal

• Individual
• limited harvesting capabilities (maxSugarHarvest)
• ability to talk and listen
• Emergent behaviour
• cooperation
• communication
• If cooperation is needed then talking is
  beneficial and communication will emerge

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VUScape

• Had to implement own testbed VUScape
• Model is highly based on SugarScape
• The major changes
• Sugar randomly distributed multi-agent cells
• talkPref 0 - 1 and listenPref 0 - 1 genes
• Talk actions and Listen actions
• MaxSugarHarvest value cooperation threshold

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VUScape random landscape

• Instead of 2 sugar hills a random distribution
• 2,500 sugar units are spread across 2,500 cells
• 30 population drop but still viable world
• (because it is harder to find food?)

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VUScape limited vision range

• Vision range set to 1 instead of gene range 1-6
• Evolution of vision is not the aim of our project
• local info from vision, global from communication
• Short-sighted agents face a tough environment

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VUScape multiple agents

• Cooperation scheme requires multi-agent cells
• Higher population size is now possible

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VUScape re-seed sugar

• Agents find food, wait there until it regenerates
• We need agents that are constantly searching
• Explorativeness is increased by reseeding sugar
  after consumption

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VUScape sex recovery period

• To avoid possible birth explosions we implement a
  sex recovery period
• Recovery period of 5 yields pop decrease of 11
• Flattens the age distribution

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Step 1 in need of help

• IF localAmount gt maxSugarHarvest
• THEN inNeedOfHelp

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Step 2 talking

• IF inNeedOfHelp AND rand lt talkPref
• THEN communicate to others on x and y axis
• cell coordinates and sugar value

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Step 3 listening

• IF rand lt listenPref
• THEN listen to others on x and y axis

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Step 4 cooperating

• Use obtained information in movement decision.
• Two agents can conquer any pile!
• Cooperation is beneficial for both parties.
• Communicative agents have an advantage?

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Setup

• Stepwise increase cooperative pressure and
  monitor the communicative gene values.
• Experiment A no communication
• Talk and listen genes disabled
• Experiment B with communication
• Talk and listen genes initiated between 0 and 1
• If communication is beneficial then an increase
  of talk and listen values is expected.

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Results no communication
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Results with communication
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Results listenPref
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Results talkPref
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Conclusion

• Communication makes society more viable
• High talking and listening preferences give
  agents a selective advantage
• Higher cooperative pressure induces communication
• Future work other topologies, communication
  protocols