Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Optimization of simulated biological...

Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg

Optimization of simulated biological multi-agent systems by means of evolutionary

processes

Alexander HörnleinChristoph Oechslein

Frank Puppe

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Optimization of simulated biological multi-agent systems by means of evolutionary processes

Motivation / Problem

• Optimization of behavior in respect of– explicit evaluation function– implicit evaluation function

e.g. “the agents have to survive a certain period”

• Calibration towards a predefined target behaviore.g. “the agents should act exactly as in real life”

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Evolution as optimization

• Population of potential solutions• Evaluation by means of “natural selection”• Iteration: Survivors (i.e. highly fit

individuals) reproduce

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Reproduction

• Mutation– Offspring differs slightly - possibly advantageous– local search

• Recombination– Child possibly unites the advantages of both

parents– global search

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Behavior in SeSAm

Agent

• Rules• Activities

• Parameters• Memory• Perception

IF (in activity1) ANDCondition THEN activity3

Activity1 Activity2

Activity3

Action1Action2

...

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GP approach: Mutation operators

activity

Parameter a += 10Approach agent xIncrease speed

Parameter a += 25Parameter b += 25Flee from agent x

Focus on earth

• Change numeric terminals• Change symbolic terminals

• Change non-terminals• Delete action• Add action

• Add new activity• Add new rule• Change rule• Delete activity• Delete rule

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Advantage

• Extremely powerful• Little constraint by

initial structure of behavior

• Development of unnecessary or unwanted complexity

• Restrictions are difficult to define/set

• Slow• Hard to implement

within SeSAm

Disadvantages

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GA/ES approach: Mutation operators

activity

Parameter a += 10Approach agent xIncrease speed

• Change numeric terminals

Parameter a += 25 that’s it in principle.

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Applicability of GA/ES approachwithin SeSAm

• Actions– Use numerical terminals– Can be controlled by probabilities

• Rules– Condition-parts use numerical terminals– Action-parts can be controlled by probabilities

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Model modification

•Define rules for any reasonable transient

•Let evolution weight them

•Treat actions accordingly

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Advantages

• Sufficient powerful• Easy to restrict:

Evolution can’t break boundaries of predefined behavior

• Fast• Implementation within

SeSAm is ‘straight-forward’

• Not extremely powerful

Disadvantage

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SeSAm genes

RULE: IF ENERGY > gene0 THEN MOVE

gene0:

(initial)value

(initial) standar

d deviatio

n

]upper

boundary

[lower

boundary

(initial) standard deviationdominancedistribution

(initial) value

lower boundaryupper boundary

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SeSAm genomes

agent role

behavior

family attribute

egg storage

genomedeclaration

gene0 declaration gene1 declaration ...

genome

gene0 gene1 ...allele0-0 allele1-0

gene0 allele0-1 gene1 allele1-1 ...... ...

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Polyploid genome

• Treated threadwise • Treated genewise

dominancemutation

dominancemutation

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value0

value1

value2

meta gene

Possibilities for the gene-expression

•weighted

ii

iii

dominance

valuedominance

)(

)(

value0

•dominant/recessive

i

ivaluealleles#

1

•‘intermediary’

expression


Application

from individuals to colonies

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Insects’ behavior

from ownreservoir

brood care

from nestreservoiridle

growfeed

feed on nestreservoir

feedon broodlay egg

mate

seeknew nest

seekmarker

setmarker insectsprey

hunt

fighttransportto nest

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Insects’ genes

idle

grow

lay egg

mate

seek new nest

queen-factor

prey

hunt

fighttransportto nest

hunt-factor

from ownreservoir

brood care

from nestreservoir

brood care-factor

energy levelgenes

feed

feed on nestreservoir

feed on brood

egglevelgenes

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Initial insects’ world

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Insects’ world after 150,000 ticks

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Changes of gene-pool

queen-factor

brood care-factor

hunt-factor

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More changes of gene-pool

initial egg energy

energy portion ant

energy portion brood

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Results & Discussion

• Successful evaluation in three scenarios• ES/GA approach powerful and easy to use

? Use of explicit evaluation function for greater applicability

? Accelerate optimization (through parallelism)

Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Optimization of simulated biological...

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