NPC Adaptation in Interactive Fiction By: Ryen Wilkins Adviser: Dr. C. David Shaffer

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

Genetic Algorithm (GA) a) Create random population (set of chromosomes) b) Evaluate each chromosome using fitness function c) Select best n to continue into next generation d) Perform genetic operations - Mutation - Crossover e) Repeat from b until desired fitness is achieved

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

TADS 3 Dialogue Example

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.

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.

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

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.

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).

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

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.

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.

Uniform Player RCK Results (50 iterations/set – 5 generations) Total Wins Trial #

Rock focused player (75%) 50 iterations

Cloth focused player (75%) 50 iterations

Knife focused player (75%) 50 iterations

– 50 iterations

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

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.

Second level (uniform)

Uniform – 50 generations

Uniform – 100 generations

Second level ( )

Second level ( generations)

Second Level (me)

Second level (me – 50 generations)

Future Work  Expand to a more difficult game: - Waving Hands: ngHands.html