slides

Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Optimization of simulated biological multi-agent
systems by means of evolutionary processes
Alexander Hörnlein
Christoph Oechslein
Frank Puppe
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
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 behavior
e.g. “the agents should act exactly as in real life”
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Evolution as optimization
• Population of potential solutions
• Evaluation by means of “natural selection”
• Iteration: Survivors (i.e. highly fit individuals)
reproduce
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Reproduction
• Mutation
– Offspring differs slightly - possibly advantageous
– local search
• Recombination
– Child possibly unites the advantages of both parents
– global search
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Behavior in SeSAm
Activity1
Agent
• Rules
• Activities
IF (in activity1) AND
Condition THEN activity3
Activity2
Activity3
Action1
Action2
...
• Parameters
• Memory
• Perception
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
GP approach: Mutation operators
activity
• Change
Add newnumeric
activity terminals
• Add
Change
newsymbolic
rule
terminals
Parameter ab += 10
25
Flee from agent x
Approach
Increase speed
Focus on earth
• Change rule
non-terminals
• Delete activity
action
• Delete
Add action
rule
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Advantage
• Extremely powerful
• Little constraint by initial
structure of behavior
Disadvantages
• Development of
unnecessary or unwanted
complexity
• Restrictions are difficult
to define/set
• Slow
• Hard to implement within
SeSAm
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
GA/ES approach: Mutation operators
activity
Parameter a += 10
25
Approach agent x
Increase speed
• Change numeric terminals
that’s it in principle.
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Applicability of GA/ES approach
within SeSAm
• Actions
– Use numerical terminals
– Can be controlled by probabilities
• Rules
– Condition-parts use numerical terminals
– Action-parts can be controlled by probabilities
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Model modification
• Define rules for any
reasonable transient
• Let evolution weight
them
• Treat actions
accordingly
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Disadvantage
Advantages
• Sufficient powerful
• Not extremely powerful
• Easy to restrict:
Evolution can’t break
boundaries of predefined
behavior
• Fast
• Implementation within
SeSAm is ‘straight-forward’
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
SeSAm genes
RULE: IF ENERGY > gene0 THEN MOVE
gene0:
lower boundary
upper boundary
(initial) value
(initial) standard deviation
distribution
dominance
[
lower
boundary
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
(initial)
standard
deviation
(initial)
value
]
upper
boundary
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
SeSAm genomes
agent
role
behavior
genome
declaration
genome
egg storage
gene0 declaration
gene1 declaration
...
allele1-0
allele1-1
...
...
family attribute
gene0
allele0-0
allele0-1
...
gene1
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Polyploid genome
• Treated threadwise
• Treated genewise
dominance
mutation
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
dominance
mutation
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Possibilities for the gene-expression
• dominant/recessive
• ‘intermediary’
value0
value1
• weighted
meta gene
i
ω (dominancei ) ⋅ valuei
1
⋅ valuei
expression
value0
# alleles
ω (dominance
i
i)
i
value2
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Application
from individuals to colonies
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Insects’ behavior
hunt
set
marker
seek
marker
prey
insects
from own
reservoir
fight
transport
to nest
from nest
reservoir
idle
mate
lay egg
feed
on brood
seek
new nest
brood care
grow
feed on nest
reservoir
feed
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Insects’ genes
hunt-factor
hunt
brood care-factor
prey
transport
to nest
egg
level
genes
from own
reservoir
fight
from nest
reservoir
idle
queen-factor
mate
feed on brood
lay egg
seek new nest
brood care
feed on nest
reservoir
grow
feed
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
energy
level
genes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Initial insects’ world
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Insects’ world after 150,000 ticks
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Changes of gene-pool
queen-factor
hunt-factor
brood care-factor
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
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More changes of gene-pool
initial egg energy
energy portion ant
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
energy portion brood
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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
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)
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by means of evolutionary processes
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