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Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Optimization of simulated biological multi-agent systems by means of evolutionary processes Alexander.

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Präsentation zum Thema: "Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Optimization of simulated biological multi-agent systems by means of evolutionary processes Alexander."—  Präsentation transkript:

1 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Optimization of simulated biological multi-agent systems by means of evolutionary processes Alexander Hörnlein Christoph Oechslein Frank Puppe

2 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 2 / 23Optimization 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 behavior e.g. the agents should act exactly as in real life

3 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 3 / 23Optimization 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 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 4 / 23Optimization 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 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 5 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Behavior in SeSAm Agent Rules Activities Parameters Memory Perception IF (in activity1) AND Condition THEN activity3 Activity1 Activity2 Activity3 Action1 Action2...

6 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 6 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes GP approach: Mutation operators activity Parameter a += 10 Approach agent x Increase speed Parameter a += 25Parameter b += 25 Flee 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 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 7 / 23Optimization 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 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 8 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes GA/ES approach: Mutation operators activity Parameter a += 10 Approach agent x Increase speed Change numeric terminals Parameter a += 25 thats it in principle.

9 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 9 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes 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

10 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 10 / 23Optimization 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 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 11 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Advantages Sufficient powerful Easy to restrict: Evolution cant break boundaries of predefined behavior Fast Implementation within SeSAm is straight-forward Not extremely powerful Disadvantage

12 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 12 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes SeSAm genes RULE: IF ENERGY > gene0 THEN MOVE gene0: (initial) value (initial) standard deviation ] upper boundary [ lower boundary (initial) standard deviation dominance distribution (initial) value lower boundary upper boundary

13 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 13 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes SeSAm genomes agent role behavior family attribute egg storage genome declaration gene0 declarationgene1 declaration... genome gene0gene1... allele0-0allele1-0 gene0allele0-1gene1allele

14 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 14 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Polyploid genome Treated threadwiseTreated genewise dominance mutation dominance mutation

15 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 15 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes value0 value1 value2 meta gene Possibilities for the gene-expression weighted value0 dominant/recessive intermediary expression

16 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg Application from individuals to colonies

17 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 17 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Insects behavior from own reservoir brood care from nest reservoir idle grow feed feed on nest reservoir feed on brood lay egg mate seek new nest seek marker set marker insects prey hunt fight transport to nest

18 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 18 / 23Optimization 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 fight transport to nest hunt-factor from own reservoir brood care from nest reservoir brood care-factor energy level genes feed feed on nest reservoir feed on brood egg level genes

19 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 19 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Initial insects world

20 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 20 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Insects world after 150,000 ticks

21 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 21 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes Changes of gene-pool queen-factor brood care - factor hunt-factor

22 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 22 / 23Optimization of simulated biological multi-agent systems by means of evolutionary processes More changes of gene-pool initial egg energyenergy portion ant energy portion brood

23 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg 23 / 23Optimization 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)

24 Lehrstuhl für Künstliche Intelligenz - Univ. Würzburg


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