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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary
processes
Alexander HörnleinChristoph Oechslein
Frank Puppe
2 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
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”
3 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Evolution as optimization
• Population of potential solutions• Evaluation by means of “natural selection”• Iteration: Survivors (i.e. highly fit
individuals) reproduce
4 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Reproduction
• Mutation– Offspring differs slightly - possibly advantageous– local search
• Recombination– Child possibly unites the advantages of both
parents– global search
5 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Behavior in SeSAm
Agent
• Rules• Activities
• Parameters• Memory• Perception
IF (in activity1) ANDCondition THEN activity3
Activity1 Activity2
Activity3
Action1Action2
...
6 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
7 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
8 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
GA/ES approach: Mutation operators
activity
Parameter a += 10Approach agent xIncrease speed
• Change numeric terminals
Parameter a += 25 that’s it in principle.
9 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
10 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Model modification
•Define rules for any reasonable transient
•Let evolution weight them
•Treat actions accordingly
11 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
12 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
13 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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 ...... ...
14 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Polyploid genome
• Treated threadwise • Treated genewise
dominancemutation
dominancemutation
15 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
value0
value1
value2
meta gene
Possibilities for the gene-expression
•weighted
ii
iii
dominance
valuedominance
)(
)(
value0
•dominant/recessive
i
ivaluealleles#
1
•‘intermediary’
expression
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Application
from individuals to colonies
17 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Insects’ behavior
from ownreservoir
brood care
from nestreservoiridle
growfeed
feed on nestreservoir
feedon broodlay egg
mate
seeknew nest
seekmarker
setmarker insectsprey
hunt
fighttransportto nest
18 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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
19 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Initial insects’ world
20 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Insects’ world after 150,000 ticks
21 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
Changes of gene-pool
queen-factor
brood care-factor
hunt-factor
22 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
More changes of gene-pool
initial egg energy
energy portion ant
energy portion brood
23 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent systems by means of evolutionary processes
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