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George Dolbier CTO Interactive Media IBM

Looking at Machine Learning in Games

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Page 1: Looking at Machine Learning in Games

George Dolbier

CTO Interactive Media

IBM

Page 2: Looking at Machine Learning in Games

What is Machine Learning?

Software that

Learns and Produces

Without

Explicit

Programming

Page 3: Looking at Machine Learning in Games

ASK DISCOVER EXPLORE 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/

Page 4: Looking at Machine Learning in Games

Machine Learning in Games

Creating better play experience

4

Case Study

Page 5: Looking at Machine Learning in Games

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?

Page 6: Looking at Machine Learning in Games

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

Page 7: Looking at Machine Learning in Games

How tradeoff is used to balance the game

Show Watson Tradeoff call

Page 8: Looking at Machine Learning in Games

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

Page 9: Looking at Machine Learning in Games

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

Page 10: Looking at Machine Learning in Games

Machine Learning in Games

Truly Interactive NPC

10

Case Study

Page 11: Looking at Machine Learning in Games

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/

Page 12: Looking at Machine Learning in Games

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

Page 13: Looking at Machine Learning in Games

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”

Page 14: Looking at Machine Learning in Games

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.

Page 15: Looking at Machine Learning in Games

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

Page 16: Looking at Machine Learning in Games

[email protected]

@NoirTalon

https://www.linkedin.com/in/georgedolbier