slides - Lehrstuhl für Informatik VI, Uni Würzburg

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
2 / 23
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
3 / 23
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
4 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Behavior in SeSAm
Agent
• Rules
• Activities
Activity1
IF (in activity1) AND
Condition THEN activity3
• Parameters
• Memory
• Perception
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
Activity2
Activity3
Action1
Action2
...
5 / 23
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
6 / 23
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
7 / 23
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
8 / 23
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
9 / 23
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
10 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Advantages
Disadvantage
• 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
11 / 23
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
12 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
SeSAm genomes
agent
role
behavior
genome
declaration
genome
egg storage
gene0 declaration
gene1 declaration
...
allele0-0
allele0-1
...
allele1-0
allele1-1
...
...
family attribute
gene0
gene1
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
13 / 23
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
14 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Possibilities for the gene-expression
• dominant/recessive
• ‘intermediary’
value0
• weighted
)  value
  (dominance
1
  value
expression
value0
# alleles
 (dominance )

i
value1
meta gene
i
i
i
i
i
i
value2
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
15 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Application
from individuals to colonies
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Insects’ behavior
hunt
set
marker
seek
marker
transport
to nest
prey
from own
reservoir
fight
seek
new nest
brood care
from nest
reservoir
idle
mate
lay egg
insects
feed
on brood
grow
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
feed on nest
reservoir
feed
17 / 23
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
18 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Initial insects’ world
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
19 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Insects’ world after 150,000 ticks
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
20 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
Changes of gene-pool
queen-factor
hunt-factor
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
brood care-factor
21 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg
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
22 / 23
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)
Optimization of simulated biological multi-agent systems
by means of evolutionary processes
23 / 23
Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg