1. CATEGORIZING COREWAR WARRIORS Nenad Tomašev, Doni Pracner

2. Data mining A process of extracting non-trivial, previously unknown and potentially useful pieces of information from a set of data Main types: 1)Classification 2)Association 3)Clustering 4)Numeric prediction

3. COREWAR Assembly language: redcode Programs compete for resources Virtual world where the battle takes place: Core MARS: Memory Array Redcode Simulator

5. COREWAR Great variety of strategies: –Replicators –Scanners –Coreclears –Stones –Vampires –Hybrid strategies

6. EVOLUTION IN COREWAR Warriors generated via genetic algorithms Fitness: a) benchmark score Fitness: b) round robin tournament score Coevolution, Island model

7. PROJECT GOAL Examining diversity among the warrior set generated by CCAI evolver via the use of clustering algorithms

8. CCAI EVOLVER (Barkley Vowk, Canada) Island model 20 random species Pool for the next generation is the previous generation

9. THE DATASET 26795 warrior files Chronoligically divided into 4 groups: –10554 –6889 –4973 –4689

10. DATA REPRESENTATION (1) Only command names counted Modified bag of instructions: –ADD and SUB joined –Presence or absence of imp components –Characteristic consecutive instruction pairs

11. The Application Created C:\data mine\>java Worker CoreWar analyzer Usage: Worker wu indir outdir [outfile]- complete process Worker w indir outdir - just analyze and create the files Worker u indir [outfile] - unite the files in the dir into a csv Worker d indir outdir adir - check for duplicates in indir. adir should be made by w Worker s indir outdir adir - (very) speeded up version of d C:\data mine\>

12. DATA REPRESENTATION (2) 30 warriors benchmark selection Score table for each set was created

13. FILTERING DATA Removing duplicates in the representation space Motivation: –Speeding up the process –Ensuring better clustering algorithm performance Duplicate: any two warriors with identical instructions and modifiers, ignoring address fields

14. FILTERING DATA (2) Dataset4 :4389 warriors 4389*4388/2 = 9.629.466 about 100 lines, each 4 parts to compare about 3.851.786.400 logical comparisons. Method duplicates – only 25.309 file comparisons – 188 minutes Method duplicatesSeparation – dataset 3: 4973 warriors – 29 minutes

15. FILTERING DATA (3) results 2150 instances were removed (8%) or per part: –12% - 1345 –8% - 559 –1% - 56 –4% - 193

16. Clustering EM clustering method was used (expectation maximization algorithm) Weka (Waikato Environment for Knowledge Analysis)

17. RESULTS groupnum. of clusters group 12 group 24 group 312 group 45

18. RESULTS Attribute Evaluation

23. GROUP 1 (JMN)

24. GROUP 1 (SPL/MOV)

25. GROUP 2 (DAT)

26. GROUP 3 (SPL)

27. GROUP 4 (DJN)

28. SOME CONCLUSIONS Over time, the apparent loss of diversity was recorded, due to the survival of the most efficient mutation-resistent forms Replicators and coreclears dominated the population Presence of imps was noted in a large part of the population

29. CONCLUSIONS Attribute evaluation Attribute evaluation did notice big differences between the original pool (set 1) and the first generation (dataset 2) ‘DJN’ and ‘MOVDJN’ were the most significant attributes in the clustering of the whole data set. ‘SPLMOV’ and ‘MOVJMP’ were also important in clustering of some of the subgroups.

30. FUTURE RESEARCH IDEAS Use of different clustering methods, results comparison Training the classifiers for identification of human-coded warriors –Based on syntax analysis –Based on benchmark score

31. THE END Thank You for Your attention