AI and Interactive Narrative
- 1. AI AND INTERACTIVE NARRATIVE
Mirjam Palosaari Eladhari, PhD
Otter Play Independent studio and Södertörn University, Media technology, Game Research Group
Interactive Narrative Design Think Tank, Nederlands Film Festival September 29, 2019
- 7. A WORLD, SO PERFECTLY DESIGNED, THAT
DRAMAS AND STORIES ARE MEANINGFUL
FOR ALL PLAYERS
A WORLD AFFORDING EMERGENCE OF
GREAT DRAMA.
- 8. OVERVIEW
➤ AI for Games/Interactive
Narrative
➤ Developments, past decade
➤ Tech at our finger tips:
➤ Procedural Content
Generation
➤ Machine learning
➤ Opportunities, Challenges
and wish lists
- 9. ABOUT ME
➤ Developed narrative games and applications
➤ Lead game programmer & story logics 1999-2001 (Liquid Media, Stockholm)
➤ MMORPG Systems and location based games- MindModule (five game prototypes
exploring character driven narrative)2002-2011 (Tech Lead at Zero Game Studio,
Interactive Institute, then PhD Student Gotland Uni, Tokyo Tech, Georgia Tech, USCS,
Teesside Uni)
➤ Co-creative systems, and story making games (suite of games for C2Learn project,
using computational creativity tech, Lead designer)
➤ Board game design (minimalist CI and story making games)
➤ Narrative generation (tarot based)
➤ Written papers on the above, and on AI Based Game Design
➤ Teaching Game Design, tech innovation (TOG approach), research method (lecturer
Gotland Uni, Malta Uni, now at Södertörn Uni)
➤ Organising workshops on Social Believable Agents, NPC Technologies and INT for past
decades, ambulating to different conferences (AIIDE, AAAI, FDG, DiGRA)
- 13. AI PERCEPTION VS REALITY
HUMAN-LIKE INTELLIGENCE VS. SMART SYSTEMS
NO ARTIFICIAL BRAIN, BUT WE HAVE SIRI, ALEXA AND OTHERS, EG. IN
CURRENT CARS
EXAMPLES:
- PATH FINDING - THE ENEMIES IN A VIDEO GAME FOLLOW YOU
- MAP GENERATION - MANY MOBILE GAMES CREATE NEW LEVELS ON
THE FLY
- NPC BEHAVIOR
- BRANCHING STORIES
- COMPUTERS PLAYING CHESS, GO, STARCRAFT
- 14. IN GAMES
SMART SYSTEMS, BIG WORLDS
Sandboxes,
Rulesets that enable play and emergence that doesn’t break the system.
SMOKE AND MIRRORS.
Simple AI looks smarter than it is when designed well
BELIEVABILITY VS REALISM
Expressive agents that are believable in *their* fictional worlds
- 15. BUT I’M NOT DOING GAMES,
FILM(VIDEO) IS DIFFERENT
YES, BUT THE LINES BETWEEN GAMES
AND FILM/VIDEO IS BLURRING (E.G.
GAME ENGINES RE USED FOR REAL-
TIME CGI WORKFLOWS)
- 16. MAIN TYPES OF AI FOR NARRATIVE
NARRATIVE GENERATION
From old approach of fully automated authoring process (BRUTUS, tale spin ) to assisted
authoring, co-creative systems.
CHARACTER AI
From state machines more sophisticated NPCs to synthetic humans IVA style. Also Chatbots,
twitter bots,
AUTHORING TOOLS
Inform7, Twine, and about 300 more
SYSTEM FRAMEWORKS FOR STORY TELLING
Story telling reasoning, story logics, ‘drama management’ AI game masters etc.
- 18. IN GAMES: IMPROVED, MORE SOPHISTICATED METHODS
➤ More sophisticated ways of solving the same problems.
Game AI is way past A* Pathfinding and state machines.
➤ But the technology has not changed much. Same
problems are tackled but with more sophistication:
➤ deeper searches with for example MTCSes (AI winning
against human GO player)
➤ behaviour trees and utility systems rather than state
machines.
➤ Planners -> reactive planners and forward chaining.
(eg, new plan for agent if situation changes)
- 19. NEW DIRECTIONS IN FIELD OF GAME AI
➤ General Game AI: defining a language for AI that can be used
across games. (still new, quite promising endeavour)
➤ AI Based games has become a topic of study and
experimentation.
- 21. PAST 5 - 10 YEARS: DEVELOPMENTS IN FIELD OF GAME AI (2)
➤ The holy grail of the open world system with character driven drama
has been modified
➤ Adding systems for drama management -> to give player reader
direction and “story beats” in a vast world with backstory that can
be explored.
➤ Great games/interactive narratives are built (many mentioned here
this evening) but their greatness is most often due of human artistry,
skill, sensitivity and hard work. (rather than any groundbreaking AI
causing it)
➤ The breakthrough moment for more ambitious interactive narrative
might be just around the corner, several projects existed and have
been stopped for business reasons (Versu, Story Bricks, Telltale titles
etc). The tech is ready.
- 23. GAME AI HAS NOT YET
UTILISED MUCH OF THE
OPPORTUNITIES GIVEN
BY MACHINE LEARNING.
- 24. THERE ARE REASONS!
(for not immediately jumping into using
data mining and Machine learning when making
games/interactive narratives)
- 27. GROUND BREAKING TECHNOLOGIES ALREADY AT OUR FINGERTIPS
➤ Procedural generation
PCG is a family of techniques,
algorithms and procedures
used for generating contents
in an automatic way rather
than manually.”
➤ Machine Learning
- 28. ➤ Challenge: Curation of the
content. What is “good”?
What is useful?
GROUND BREAKING TECHNOLOGIES ALREADY AT OUR FINGERTIPS
➤ Procedural generation
PCG is a family of techniques,
algorithms and procedures
used for generating contents
in an automatic way rather
than manually.”
➤ Machine Learning
- 32. SOLUTIONS FOR PCG CHALLENGE:
CURATED SEEDS AND PATTERNS FOR
WHAT SEEDS THE GENERATION OF
CONTENT
- 39. TAROT BASED NARRATIVE GENERATION RESOURCES
➤ Application: http://www.asdesigned.com/tarot/
➤ Paper: Tarot-Based Narrative Generation – Sullivan, A.
Eladhari M.P., and Cook M, PCG 2018 Workshop, FDG2018,
Malmö, Sweden, August 2018.
➤ Code: https://github.com/anneandkita/tarot
- 40. ➤ Challenge: Curation of the
content. What is “good”?
GROUND BREAKING TECHNOLOGIES ALREADY AT OUR FINGERTIPS
➤ Procedural generation
PCG is a family of techniques,
algorithms and procedures used for
generating contents in an
automatic way rather than
manually.”
➤ Machine Learning
ML provides systems the ability to
automatically learn and improve
from experience without being
explicitly programmed. ML focuses
on the development of computer
programs that can access data and
use it learn for themselves.
- 41. A Style-Based Generator Architecture for Generative Adversarial Networks
Tero Karras, Samuli Laine, Timo Aila, 2018
Generative Adversarial Networks
- 43. 8 YEARS AGO, TWO CLEVERBOTS TALKING (2011)
By Igor Labutov, Jason Yosinski, and Hod Lipson of the Cornell Creative Machines Lab (http://creativemachines.cornell.edu/)
- 44. MICROSOFT TWITTERBOT TAY PICKED UP RACISM - ONLY UP ONE DAY (2016)
Machine Learning -
what the machine
learns, depends on
what knowledge is
given to to learn from
- 45. ➤ Challenge: Curation of the
content. What is “good”?
➤ Challenge: Knowledge-base
design. What data to give the
neural nets to learn from?
GROUND BREAKING TECHNOLOGIES ALREADY AT OUR FINGERTIPS
➤ Procedural generation
PCG is a family of techniques,
algorithms and procedures used for
generating contents in an
automatic way rather than
manually.”
➤ Machine Learning
ML provides systems the ability to
automatically learn and improve
from experience without being
explicitly programmed. ML focuses
on the development of computer
programs that can access data and
use it learn for themselves.
- 46. REASONS
(for not immediately jumping into using
data mining and machine learning when making games/interactive narratives)
Unpredictability:
➤ Quality of the content
➤ Nature of the content
In order for a machine to learn - you give it a data set. The nature of the data set defines what the machine can learn.
That works when you take materials that exists in the world. But when you create a world -> you don’t have data
until it exits.
Q1: What real world data could be used when building worlds?
(for example, create richness in backstories?)
If Q1, then Q2: How can big sets of data be curated to be useful in narrative systems?
(starting from where, and how to scrape the data?)
- 47. WHAT’S AROUND THE CORNER
➤ Unity! Build me an 18th century sitting room!
➤ Manipulate video - edit out people and objects (tool out soon)
➤ Deep fake videos - high fidelity speech synthesis + image
- 48. WHAT’S AROUND THE CORNER?
➤ Unity! Build me an 18th century sitting room!
➤ Manipulate video - edit out people and objects (tool out soon)
➤ Deep fake videos - high fidelity speech synthesis + image
actress Amy Adams in the original (left) is
modified to have the face of actor Nicolas Cage
(right).
- 49. WHAT’S AROUND THE CORNER?
➤ Unity! Build me an 18th century sitting room!
➤ Manipulate video - edit out people and objects (tool out soon)
➤ Deep fake videos - high fidelity speech synthesis + image
➤ The holy grails of the open-world emergent drama - the
technology is coming along!
actress Amy Adams in the original (left) is
modified to have the face of actor Nicolas Cage
(right).
- 51. A DOWN TO EARTH WISH LIST OF A NARRATIVE DESIGNER
➤ Story logics - AI making sure sprawling story lines don’t have
dead ends and logical fallacies (correcting my human errors)
➤ Usable interface for creating rich, round, NPCs in the world
for players to get to know
➤ Curation of player created content: Allow players to add own
content, but not have to curate it manually, or building
systems for player community ratings - instead have an AI
doing the labour (detecting too provocative entries, curating
out repetitive themes etc.)
➤ Q: What more?
- 52. A LOFTY WISH LIST OF AN INT/AI RESEARCHER
➤ Using ML and GANs to enable players to create their own
symbolic languages - taking dream symbolics into the digital
space
➤ Be able to raise the dead: make a true to life representation of
long lost relatives - I know their behaviour, I’d author them,
and then they would grow in the now - eg my grandmother at
age 30 - what dance club would she go to? What would we
talk about? (eg Synthetic Human AI + Big Data + Machine
Learning)
➤ …What would you put on this list?