Motivation / Problem

Transkript

1 Optimization of simulated biological multi-agent systems Alexander Hörnlein Christoph Oechslein Frank Puppe 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 2 / 23 1

2 Evolution as optimization Population of potential solutions Evaluation by means of natural selection Iteration: Survivors (i.e. highly fit individuals) reproduce 3 / 23 Reproduction Mutation Offspring differs slightly - possibly advantageous local search Recombination Child possibly unites the advantages of both parents global search 4 / 23 2

3 Behavior in SeSAm Agent Rules Activities Parameters Memory Perception Activity1 IF (in activity1) AND Condition THEN activity3 Activity2 Activity3 Action1 Action / 23 GP approach: Mutation operators activity Parameter b a += Approach Flee from agent x Increase speed Focus on earth Change Add new numeric activity terminals Add Change new symbolic rule terminals Change rule non-terminals Delete activity action Delete Add action rule 6 / 23 3

4 Advantage Disadvantages 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 7 / 23 GA/ES approach: Mutation operators activity Change numeric terminals Parameter a += Approach agent x Increase speed that s it in principle. 8 / 23 4

5 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 9 / 23 Model modification Define rules for any reasonable transient Let evolution weight them Treat actions accordingly 10 / 23 5

6 Advantages Disadvantage Sufficient powerful Easy to restrict: Evolution can t break boundaries of predefined behavior Fast Implementation within SeSAm is straight-forward Not extremely powerful 11 / 23 SeSAm genes RULE: IF ENERGY > gene0 THEN MOVE gene0: lower boundary upper boundary (initial) value (initial) standard deviation distribution dominance [ lower boundary (initial) value (initial) standard deviation ] upper boundary 12 / 23 6

7 SeSAm genomes agent behavior genome egg storage family attribute gene0 declaration gene1 declaration... allele0-0 allele1-0 gene0 allele0-1 gene1 allele role genome declaration 13 / 23 Polyploid genome Treated threadwise Treated genewise dominance mutation dominance mutation 14 / 23 7

8 Possibilities for the gene-expression dominant/recessive intermediary value0 weighted value1 meta gene i ω( dominancei ) valuei 1 expression value0 value i # alleles ω( dominance i ) i i value2 15 / 23 Application from individuals to colonies 8

9 Insects behavior hunt set marker seek marker prey insects transport to nest fight from own reservoir brood care mate idle from nest reservoir lay egg seek new nest grow feed on brood feed on nest reservoir feed 17 / 23 Insects genes hunt-factor hunt prey brood care-factor from own transport fight reservoir brood care to nest egg level genes mate lay egg queen-factor seek new nest grow idle feed on brood from nest reservoir feed on nest reservoir feed energy level genes 18 / 23 9

10 Initial insects world 19 / 23 Insects world after 150,000 ticks 20 / 23 10

11 Changes of gene-pool queen-factor hunt-factor brood care-factor 21 / 23 More changes of gene-pool initial egg energy energy portion ant energy portion brood 22 / 23 11

12 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) 23 / 23 12