The growth of the npc

1. NPC Adaptation in Interactive Fiction The growth of the npc By: Ryen WilkinsAdviser: Dr. C. David Shaffer

2. Introduction • Description:- Can Interactive Fiction (IF) take advantage of machine learning to build a more compelling story? • Implementation:- Focus on Genetic Algorithm- Limited story with learning character(s)- TADS 3 Interactive Fiction authoring system- Rock, Paper, Scissors

3. Genetic Algorithm (GA) a) Create random population (set of chromosomes)b) Evaluate each chromosome using fitness functionc) Select best n to continue into next generationd) Perform genetic operations - Mutation - Crossovere) Repeat from b until desired fitness is achieved

4. Interactive Fiction • Story in which the reader interacts with characters or items, typically impacting the story line. • Several genres exist • There are several IF authoring systems: Inform, TADS, Adrift, Quest… • None include support for machine learning

5. TADS 3 Dialogue Example

6. TADS 3 • Similar in appearance to C++, Java and Javascript. • Prototype-based language. • Standard object library (Adv3 library). • Built in random generator. • Debugging tool. • Replay function. • Vectors.

7. Incorporating GA in TADS 3 • Used Object-Oriented Implementation of Numerical Methods by Didier Besset • Convert from class-based OOL to prototype-based OOL. • Deal with differences in library, random generator, collections, iteration, etc.

8. Incorporating GA in TADS 3 • Pre-test: Partition Problem • Vector of weights. Each must be assigned to a left or right scale. • Goal is to get as close to balanced as possible. • Chromosome: Vector of 1s (left) and 0s (right) • Results: Simple test shows genetic algorithm is capable of being implemented in TADS 3

9. Next Step • Can I get a character in the story to react to the reader’s decisions? • Need a setting.-Reader is a wizard trapped in a dungeon.- Discovers that he will be released from the dungeon if he can defeat a guard in a game of rock, cloth, knife.

10. Rock, Cloth, Knife • Chromosome: Probabilities that this guard will select rock, cloth or knife. • Initial population size: 100 • From reader: 5 sets of 5 rounds each • Fitness: Number of sets in which the chromosome defeats the player. (50 iterations per set.) • Compared with system where rock, cloth and knife have equal probability (uniform).

11. Mutation and Crossover • Mutation: - New random value.- Mutate two at a time. • Crossover: - Cross and switch multiple values across two members.- Add both.

12. Initial Results • “Good” members were lost in mutation. • Adding both crossover members could trap algorithm when too many poor members were made because of it.

13. Mutation and Crossover revisited • Creep mutation – Small amount added or subtracted to a value. More even results and don’t lose “good” members. • Crossover over one value and only add a single new member.

14. Uniform PlayerRCK Results (50 iterations/set – 5 generations) Total Wins Trial #

15. Rock focused player (75%)50 iterations

16. Cloth focused player (75%)50 iterations

17. Knife focused player (75%)50 iterations

18. 25-50-25 – 50 iterations

19. Me (R: 11 C: 8 K: 6) 50 iterations

20. Conclusions • Genetic algorithm creates better opponents for specialized strategies than a uniform strategy, the only option built in with TADS 3. • Results can be used to create a character(s) that will seem to react to how the reader plays the game.

21. Second level (uniform)

22. Uniform – 50 generations

23. Uniform – 100 generations

24. Second level (25-50-25)

25. Second level (25-50-25 50 generations)

26. Second Level (me)

27. Second level (me – 50 generations)

28. Future Work • Expand to a more difficult game:- Waving Hands: http://www.gamecabinet.com/rules/WavingHands.html