Using Artificial Intelligence to Test the Candy Crush Saga Game

T18
Session - Mobile Testing
4/27/17 3:15 PM

Using Artificial Intelligence to Test the
Candy Crush Saga Game

Presented by:

Alexander Andelkovic
King/Midasplayer AB

Brought to you by:

350 Corporate Way, Suite 400, Orange Park, FL 32073
888---268---8770 ·· 904---278---0524 - info@techwell.com - https://www.techwell.com/

Alexander Andelkovic
King/Midasplayer AB

Alexander Andelkovic is the agile testing lead for Sweden-based
King/Midasplayer AB, developer of the popular mobile game Candy Crush Saga,
Alexander Andelkovic has worked on multiple complex test projects ranging from
using session-based test management for quality assuring MED-Tech devices for
life critical systems to establishing a world class approval process for Spotify apps
used by Fortune 500 companies. Now he teams with developers in testing big
data, business analytics, and game level regression testing using AI. Alex performs
both system testing and exploratory testing with a focus on assisting teams with
high-quality deliveries. He is a frequent speaker at international conferences
including both STAREAST and STARWEST.

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Using Artificial Intelligence to Test the Candy Crush
Saga Game
© King.com Ltd 2017
About us
King/Midasplayer AB
•  Founded 2003
•  +200 Games
•  2000 employees
•  13 Studios(Stockholm, London, Barcelona,
Seattle…)
•  394 million MAU(Q3 2016)
•  Acquired by Activision/Blizzard(2016-02-23)
Page 2

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Products
Franchise Games
Page 3
QA at King
King QA roles
•  ATL – Agile Testing Lead
•  Ninja – Exploratory tester
•  QRT – Quick Regression Team
•  TAE – Test Automation Engineer
•  DS – Data Scientist
•  Dev – Frontend/Backend
Developer
Page 4

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QA Problem
More than 2000 levels in Candy
Page 9
Introduction
History of AI
Page 6

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Introduction
So, what is a bot exactly?
Page 7
Introduction
Application that performs an automated task
Page 8

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Introduction
AI-bot –
why?
Page 9
•  Level designers
•  Quality Assistance (QA)
•  Game-domain research
AI-bot
How would an AI-bot actually think(heuristic,
simulation)?
Page 9

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Heuristic
Heuristic is good, maintenance is bad!
Page 11
•  Having a function that ranks moves – heuristic – could
help a lot
•  Each simulation much richer
•  Has to be generic
•  Increasing complexity with more features
•  Can we create the heuristic automatically?
MCTS
Simulation – Monte Carlo Tree Search
Page 12

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MCTS
Page 13
Simulate example
MCTS
Page 14
Win Loss Win
Is this reliable?

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MCTS
Page 15
36% 28% 19%
Repeat simulations – Monte-Carlo simulations
MCTS
Page 16
36% 28% 19%
Gradually we build a tree
=> Monte-Carlo Tree Search
40% 32%

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MCTS
Search tree for level 13 in Candy
Page 17
MCTS
Available actions
Page 18

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MCTS
Future states
Page 19
MCTS
Page 20
After 299 simulations

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MCTS demo
Page 21
MCTS demo
MCTS - Impact
What do we have?
Page 22
•  Generic method
•  Works for all levels
•  Works for other games

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MCTS - summary
MCTS summary
Page 23
•  Closer to human success rate
•  Easy to tune with more simulation per
decision
•  Time consuming, one level takes
~ 5-10 mins
•  How can we improve?
NEAT
NEAT – NeuroEvolution of Augmented
Topologies!
Page 24
•  Artificial Neural Network(ANN)
•  Reinforced learning(action, learn, evolve)
•  Guided playout

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NEAT
Play Candy Crush!
Page 25
4 1 0 0
NEAT
NEAT – Steps
Page 26
•  Get training data - Predict score (hours)
•  Create random bots
•  Compete (hours)
•  Create new bots
•  Choose the best bot - ANN
•  Play Candy choosing moves in playout with
ANN

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NEAT
The jungle law in Candy
Page 27
Focus on
blockers!
Look at
jellies!
Special candies are tasty!
NEAT
Making babies
Page 28
Mom Dad

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NEAT
Page 29
Newborn
Mom Dad
NEAT
Product of evolution
Page 30

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NEAT demo
Page 31
NEAT training
•  Red=Fittest Bot
•  Yellow=Avg Bot
fitness
•  Green=# Bot’s
•  Blue=Fittest Bot
ANN nodes
NEAT demo
Page 32
NEAT training
demo

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Results
Guiding the
playout helps
Page 33
NEAT
NEAT – Performance
Page 34
•  Simulation time, game engine needs to replay
each game state
•  Traversing time, game engine does not
support jump to state
•  More GPU’s for bot training(5-6 hours > on
commit)

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Content QA
Content QA – No bot
Page 35
Content QA
Content QA – With bot
Page 36

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Benefits
Bot benefits
Page 37
•  Level development: level difficulty, less
tweaks
•  Quality assistance: crash testing,
performance testing, regression testing
•  Data scientists: Game domain
knowledge, fun levels, game balancing
Challenges
Bot challenges
Page 38
•  Ownership(Knowledge, Resources, Bot
team)
•  Integration(Headless mode, C++/Lua/JS,
Hackday/Sprint, BotApi)
•  Maintenance(Bot Training, Infrastructure,
Extend)

1/20/17
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Q&A
alexander.andelkovic@king.com
Page 39
Questions?
Thank you!
Page 40

Using Artificial Intelligence to Test the Candy Crush Saga Game

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