Machine Learning platforms (like IBM's Watson or Google's initiatives) are just now starting to make their way into games, whether adding conversational interfaces, providing better player behavior modeling, or improving game design, ML platforms are going to make games more fun, engaging and natural to play. This talk with go through 2 different case studies - Plight of the Zombie (SparkPlug) and The Suspect - to show how ML platforms have been used in games, and the challenges of interfacing with an ML platform from a game engine.

1. George Dolbier
CTO Interactive Media
IBM

2. What is Machine Learning?
Software that
Learns and Produces
Without
Explicit
Programming

3. ASK DISCOVEREXPLORE DECIDE VISUALIZE
Ask questions
for greater
insight
Natural language
dialogue &
robotics
Image Video
Audio spam OCR
Evidence-based
decisions with
with traceability
Consolidate and
visualize
Loci of Machine Learning
Try this for fun Google “Personality Insights Demo” or use this QR code to go to
https://personality-insights-livedemo.mybluemix.net/

4. MachineLearninginGames
Creatingbetterplayexperience
4
Case Study

5. Balance and Engagement
One of the hardest problems in game design is Play Balance.
Poor progression ramps, too steep, too shallow, awkward jumps, sited as #1 factor in dis-engagement
Questions posed to the team:
• Can an ML be used to balance the play experience?
• How can you tailor an experience for a specific player?
• Can the individual’s telemetry data be used to make better balance decisions?
• Can Curated content be delivered to players programmatically to produce a better overall
experience?

6. Case Study : Plight of the Zombie
Situation:
• Simple puzzle game
• Dozens of short 30 to 90 second experiences
• Designed by level designers
• Each level given a “curated/seeded” difficulty
For this initial use case 3 variables are considered:
1. Time it took player to complete level
2. Number of retries the player took to complete level
3. Curated difficulty metric assigned to each level
John O’Neil: Sparkplug Games

7. How tradeoff is used to balance the game
Show Watson Tradeoff call

8. Result
Player plays through tutorial levels, and seeded “easy” levels
3 Tutorial levels, 2 “easy” levels
By the 5 level enough data has been collected for Watson to begin suggesting ramp in
difficulty

9. Next Steps
For this simple use case next obvious steps will be:
Use all player telemetry to correct difficulty settings
Have Watson identify play patterns in player base
Have Watson tell developers progression, engagement, trends
− How long to players play
− Are players playing longer, shorter
− What is triggering in game purchase or conversation
Have Watson automatically generate freebies or offers based on struggle

10. MachineLearninginGames
TrulyInteractiveNPC
10
Case Study

11. Real time interaction with a fictional character
A Long time desire in the industry
Can you create a character, that a human can interact with?
Can you have a conversation with a character that includes
A Backstory
An Attitude
Likes, Dislikes
Conversation Not just scripted QnA tree
https://www.thesuspect.com/

12. The Suspect - Immersive chat thriller
Second screen app
with synchronised news alerts
and live 3D brain scan
Main screen with dynamic video, AI chat powered by Watson,
gamified experience, and transmedia storyline
Gamified conversation with
simulated points, level and rank
Contextually-served video
to match suspect’s responses

13. Case Study : The Suspect
Can we “Throw everything we can” into a character
And through natural conversation, bring the player into the character’s
world
3 Types of information make up the personality
1.Mind Map
2.Traditional Q and A (Word)
3.Conversation loops (how does character react to repeated questions)
8 to 10 people total, core team maxed at 6, calendar time 18
months
Core team focused development 4 months
Guy Gandy: Lead Developer for “The Suspect”

14. Use of Conversational technology
Conversational chat bot associated with Brazilian TV show
Average session was 20 minutes
8% of chats lasted for over an hour (Target Audience)
Site traffic increased 15%.
This lead to an increase in advertising revenue around the project's pages.

15. But Wait! There is More:
Alpha Go! https://deepmind.com/alpha-go
Project Aries http://goo.gl/eMAQMu
Guy Gandy HowWeGetToNext article on chatbots https://goo.gl/6xTCaf
Medical Minecraft http://goo.gl/dD8BMx
Fashion Design http://goo.gl/Ps9EBC
Google machine learning recipes : https://goo.gl/9k2ASx
Mari/o Using evolution to train neural networks https://goo.gl/Jxf73V

16. georged@us.ibm.com
@NoirTalon
https://www.linkedin.com/in/georgedolbier