Computational Creativity Group Computational Creativity and Game Design. Overview. 1. Computational Creativity Overview

Transcription

1 Creativity and Game Design Simon Colton Creativity Group Department of Computing Imperial College, London Creativity Group Six PhD students, four RAs, and me Supplemented by lots of UG/MSc students Artificial Intelligence research group Specifically interested in getting software to act in autonomously creative ways Address philosophical issues related to creativity video game design, graphic design, pure mathematics, and the visual arts Overview Brief introduction to computational creativity and our contributions Games projects related to the automatic design of board games, arcade games, social games, video games Return to computational creativity and a look at future directions 1. Creativity Overview What on Earth is... Creativity? What on Earth is... Creativity? The study of building software which can take on some of the creative responsibility in arts and science projects The application of AI techniques to artistic production or scientific discovery projects, etc. Pushing the boundaries of AI research methods We ve contributed to: ML, CSP, ATP, Evo, MAS,... Tools to aid/inspire Human Creativity Autonomously Creative Systems But also contributing to philosophical/psychological discussions about the nature of creativity

2 What on Earth is... Creativity? Major technical questions: Which AI techniques are best suited to projects where software has to act creatively? How can AI techniques be improved? How can we compare and contrast software in terms of its creativity (or lack thereof)? Using the artefacts produced Using information about the processes involved * Theory and implementation of automated theory formation in pure mathematics * Mathematical discovery tasks in number theory/algebras, leading to published results * Topics in the combination of automated reasoning systems * New techniques in machine learning, constraint solving and theorem proving * The introduction of Objet Trouvé browsing for design tasks * User studies comparing database versus evolutionary-based browsing for design * design tasks involving image filtering and abstract art generation * Future: machine learning of individual aesthetic preferences * Introduction of an automated painter via graphics and AI techniques * Using emotion detection to drive the painting process in automated portraiture * Employing AI techniques for automated scene construction and collage generation (in particular: you name it, we ve evolved it) * Challenging notions of creativity in software, e.g., intention, imagination, aesthetics * Working with video game companies to build next generation games * Building models of user enjoyment / immersion when playing games * Implementing games which dynamically adapt to individual players * Future: studies of the value of MCTS methods, especially w.r.t. auto-invention of games

3 Killer App Game design is a killer app for computational 2. Game Design Projects creativity research Involves the generation of many types of artefacts, including: visual art; music; audio; and text (plot lines, dialogue, commentary) There is a range of collaborative options from full board game design to in-game direction, to computer-aided game design On the Shoulders of Giants... Game Design Projects I m representing the work (and future work!) of members of our research group and our industrial partners Please forgive my occasional lack of knowledge! Adaptive video games Social network analysis around games Automatic invention of board games Evolutionary projects in game design Adaptive Video Games Adaptive Video Games Overall Idea In offline experiments, we work out how best to differentiate and profile players based on their actions and preferences (in game, in social network, etc.) In more offline experiments, we also work out how best to measure player s experiences (good, bad and indifferent), while playing games In even more offline experiments, we ask people to play different versions of games, with carefully controlled changes, and we use the methods above to measure their experiences

4 Adaptive Video Games Overall Idea We test various machine learning techniques to see which one(s) perform best at producing classifiers able to predict the change in experience for particular profiles of people under particular changes in the game We build new games able to (i) log user game-play (and socialising) data (ii) alter in various ways at run-time Building done for two projects by our games company partners We embed the profiling and prediction methods we have learned/generated offline So, the game can determine what type of player someone is (right now) and alter itself, hopefully to improve matters Adaptive Arcade Games!"#$%&"' ()#$$*+,' -&".*/0'!"#$%&' ("$)*' (+,--&' Work of Robin Baumgarten Research question: can we build arcade games which adapt to players in such a way to enhance their enjoyment? Three studies: (i) PacMan player classification (ii) Adaptive level generation in Super Mario (iii) Adaptive version of Tron/Snake clones PacMan Study PacMan Study 245 players played 5 times via Facebook Recorded pretty much everything for each player (enough data to entirely replay the game) 17 summary attributes (e.g., ghosts eaten per minute) Applied multi-class LDA to summary attributes Performed dimensionality reduction So, the first 3 or 4 dimensions become the only important ones Plotted each person five times with respect to the first two dimensions PacMan Study Results 1 Vector ghostseatenper totaltimeplayed totalkeystrokes 1 totallevelscomp Minute leted pillsperlife totalpacturns 0 powerpillspermi totalghostseate totalfruitseaten keystrokesperpi pacturnsperpill nute eatenbyghosts n pillsperminute ll score -1 ghostseatenper PowerPill -2 totalpowerpills Vector PacMan Study Conclusions People can be meaningfully classified according to their game playing actions Resulting space highlights significant features: Physical interaction with the game of game rules Risk averseness But... PacMan may not be the best game to build an adaptive version of...

5 Super Mario Level Generation Robin wrote an A* bot to win Mario competition, and is also interested in the level generation competition Automatically generate levels based on player behaviour, as extracted using the PacMan LDA analysis techniques Pilot study (10 people) LDA dimension 1: separates players who need much time to complete a level, but do not jump or run a lot from players who do the opposite (skill) LDA Dimension 2: separates players that show a differing level of interaction with game objects such as blocks and enemies (exploration) Level Generator #1 Created a simple level generator with around 20 level blocks with hand-annotated difficulty, e.g.,: Analyse players along LDA dimension 1 (ability) and generate a level by randomly appending level blocks, where the probability of choosing a block with certain difficulty depends on LDA metric Level Generator #2 Future Work Use evolutionary approaches to generate the levels Fitness function will be based on perceived level of ability in the player, and the perceived level of difficulty of the level, as estimated by the AI-bot playing it through Aim to match difficulty with ability Adaptive Game Design Snake/Tron adaptive clone (1 player with AI-bot opponents), put on FaceBook (processing) Adaptation possibilities include Level structure: moving stacles, bonus items; Tail: retracting, persistent, with gaps; Movement: 90 degree turns, gradually, speed adjustments; Opponents Planned sequence of surveys: basic, explorative, predictive CAD-Game Project Work of Jeremy Gow with Paul Cairns (York) and Paul Miller (Rebellion) Overall aim is to enable Rebellion to build game design tools which facilitate the building of dynamically altering games Our part is in trying to predict in-game experience from game actions Study of Player Commentaries We need to define the machine learning problem of predicting how player experience will differ when changes in games are carried out We don t know yet how best to determine player experience We come at this from a HCI perspective Asking players to play commercial video games and recording their experiences Working out ways to determine immersion, enjoyment, affect, etc. Pioneering player commentaries methods

6 Study of Player Commentaries Study of Player Commentaries Early results from four people playing Rogue Trooper Video the experience, ask them to talk over the video replay Pilot of an ongoing larger study (which will be 20 people) Qualitative study, primarily for hypothesis formation Interesting early results Social Agents Project Study of Player Commentaries J. Gow, P. Cairns, S. Colton, P. Miller and R. Baumgarten "Capturing Player Experience with Post-Game Commentaries" In Proceedings of the International Conference on Computer Games, Multimedia and Allied Technology, 2010 Work of Daniel Ramirez, John Charnley Social gaming application Three clustering problems Emote Games: the Hunter Game Better automated agents for tasks such as tutorials, competitions, etc. Player classification Better matchmaking Better determination of clusters Better determination of key players Some Results Meta-Clustering Approach E.g., Frustration and difficulty can be a good thing Sample of 25 people: clusters and interpretations of the clusters Skills (game), Preferences (game), Social (network) Clustering methods: For skills: dimensionality reduction, e.g., using PCA, and partitional clustering, e.g., using K-means For preferences: combination of earth mover s distance and multi-dimensional scaling EMD-MDS (projection of clusters into a new metric space) For social: geodesic distance to weight multi-dimensional scale Output is a cluster graph which can be queried along skill, preference and social axes (tested on a network of 50,000) D. Ramirez-Cano, S. Colton and R. Baumgarten "Player classification using a meta-clustering approach" In Proceedings of the International Conference on Computer Games, Multimedia and Allied Technology, 2010.

7 Identifying Social Communities Can automated techniques reproduce naturally formed social components? Motivating social interaction of new players What types of players talk to each other, and how? Compare clusters - real (social) v artificial (game) Real social clusters Connections: accepted friend requests (only) Clusters = connected components Artificial game clusters using the previous meta-clustering approaches Any correlation? Methods available: Rand Index, Jaccard Coefficient, Fowlkes and Mallows Index, Hubert's Gamma Statistics Results Social versus Game Clusters Game/social clusters show only a weak relationship Only real correlation if we have large clusters (lots of overlap) Tails off as cluster size drops (because clusters don t match) Tentative conclusions: validation of ideas proposed by people such as (Sande, 2010): social network formed mainly by real-life friends; people don t tend to develop friendships while engaged in casual gaming Automated Board Game Generation PhD work of Cameron Browne Ludi System: General game system for abstract combinatorial board games Game description language General game player with strategy module Automated game measurement Automatic generation of games Evolution of rule-sets Automated Board Game Generation Results 79 predefined games Tens of thousands of games generated, 1,389 survived evolutionary process (valid, playable) and 19 were deemed worthy of further investigation Played by people on and Yavalath was published by Nestor Games Ltd. and has proved popular MCTS Monte-Carlo Tree Search: having a big impact on Go players and general game playing Big Question: Can MCTS help in automated board game design Game Quality Prediction MSc. work of Gareth Williams Can we estimate game quality by an analysis of the MCTS trees produce by playing the game? Methodology: Correlated 12 games with BoardGameGeek scores Played out hundreds of games, producing thousands of trees, and produced statistical information about the trees Passed the numbers through many machine learning setups (ANN, SVM, DT) to learn a way of predicting game quality from MCTS data Results: we didn t manage to get anything like a good classifer However...

8 Game Quality Prediction Evolving Stuff Addressed instead the question of identifying broken games Took 12 games and 12 mutilated variants of them, achieved 86% accuracy using a decision tree method Particularly good for precision and recall: useful for filtering out bad games when automatically generated Showed statistical significance of the approach Broken Othello We ve evolved: Simple art-based games (spirographs) Buildings for Subversion Pixel shaders for Subversion An AI-bot for DEFCON based on behaviour trees Normal Othello Future Work Continuing to build models of user experience and immersion in games Lots of player studies, better logging, machine learned predictors Contributing to adaptive game design Applying social network analysis techniques to new data Universal Music Group - to detect the next big thing Applying MCTS techniques to absolutely everything! Word generation; generative art; video game bots; music generation Evolving entire arcade games - in seperate blocks PhD project of Michael Cook Automated design of more enjoyable board games (playing badly well) PhD project of Stephen Tavener Summary Game design is a killer app for computational creativity research We are looking at this area from CAD-game to full automatic design of games, including mobile, casual, social, arcade and video game design Hopefully contributing in the long term to the development of next generation games Has helped us crystallise some of our thoughts with respect to computational creativity in general Creativity Guiding Principles Ever decreasing circles Paradigms lost The whole is more than a sum of the parts Beauty is in the mind of the beholder The creativity tripod Climbing the meta-mountain Good art makes you think Thanks for Listening! Any questions?