Candy Crush Saga is one of the biggest mobile games today with more than 1000 levels of difficulty—and users continue to ask for more. When building new content, it is extremely important to ensure that the level of difficulty is balanced and that the user does not experience crashes or problems through some unforeseen level of play. Stefan Freyr shows you how King is training artificial intelligence (AI) programs (bots) to test its games by mimicking human interactions. Join Stefan as he discusses how King is taking testing to the next level by employing Monte Carlo Tree Search, automatic heuristic construction, and NeuroEvolution of Augmenting Topologies (NEAT) to train bots to test and evaluate difficulty levels. He discusses ways to extend and use AI bots to predict game success rates and conduct automatic performance testing. Stefan explains how this AI approach can be generalized to test other applications. Learn how AI can help you with testing that's getting very difficult to master with traditional testing techniques.

1. 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/

2. 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)
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© King.com Ltd 2017
Products
Franchise Games
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© King.com Ltd 2017
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
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© King.com Ltd 2017
QA Problem
More than 2000 levels in Candy
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© King.com Ltd 2017
Introduction
History of AI
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© King.com Ltd 2017
Introduction
So, what is a bot exactly?
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© King.com Ltd 2017
Introduction
Application that performs an automated task
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© King.com Ltd 2017
Introduction
AI-bot –
why?
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• Level designers
• Quality Assistance (QA)
• Game-domain research
© King.com Ltd 2017
AI-bot
How would an AI-bot actually think(heuristic,
simulation)?
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© King.com Ltd 2017
Heuristic
Heuristic is good, maintenance is bad!
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• 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?
© King.com Ltd 2017
MCTS
Simulation – Monte Carlo Tree Search
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© King.com Ltd 2017
MCTS
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Simulate example
© King.com Ltd 2017
MCTS
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Win Loss Win
Is this reliable?

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© King.com Ltd 2017
MCTS
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36% 28% 19%
Repeat simulations – Monte-Carlo simulations
© King.com Ltd 2017
MCTS
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36% 28% 19%
Gradually we build a tree
=> Monte-Carlo Tree Search
40% 32%

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© King.com Ltd 2017
MCTS
Search tree for level 13 in Candy
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© King.com Ltd 2017
MCTS
Available actions
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© King.com Ltd 2017
MCTS
Future states
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© King.com Ltd 2017
MCTS
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After 299 simulations

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© King.com Ltd 2017
MCTS demo
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MCTS demo
© King.com Ltd 2017
MCTS - Impact
What do we have?
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• Generic method
• Works for all levels
• Works for other games

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© King.com Ltd 2017
MCTS - summary
MCTS summary
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• 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?
© King.com Ltd 2017
NEAT
NEAT – NeuroEvolution of Augmented
Topologies!
Page 24
• Artificial Neural Network(ANN)
• Reinforced learning(action, learn, evolve)
• Guided playout

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© King.com Ltd 2017
NEAT
Play Candy Crush!
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4 1 0 0
© King.com Ltd 2017
NEAT
NEAT – Steps
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• 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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© King.com Ltd 2017
NEAT
The jungle law in Candy
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Focus on
blockers!
Look at
jellies!
Special candies are tasty!
© King.com Ltd 2017
NEAT
Making babies
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Mom Dad

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© King.com Ltd 2017
NEAT
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Newborn
Mom Dad
© King.com Ltd 2017
NEAT
Product of evolution
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© King.com Ltd 2017
NEAT demo
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NEAT training
• Red=Fittest Bot
• Yellow=Avg Bot
fitness
• Green=# Bot’s
• Blue=Fittest Bot
ANN nodes
© King.com Ltd 2017
NEAT demo
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NEAT training
demo

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© King.com Ltd 2017
Results
Guiding the
playout helps
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© King.com Ltd 2017
NEAT
NEAT – Performance
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• 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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© King.com Ltd 2017
Content QA
Content QA – No bot
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© King.com Ltd 2017
Content QA
Content QA – With bot
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© King.com Ltd 2017
Benefits
Bot benefits
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• Level development: level difficulty, less
tweaks
• Quality assistance: crash testing,
performance testing, regression testing
• Data scientists: Game domain
knowledge, fun levels, game balancing
© King.com Ltd 2017
Challenges
Bot challenges
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• Ownership(Knowledge, Resources, Bot
team)
• Integration(Headless mode, C++/Lua/JS,
Hackday/Sprint, BotApi)
• Maintenance(Bot Training, Infrastructure,
Extend)

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© King.com Ltd 2017
Q&A
alexander.andelkovic@king.com
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Questions?
© King.com Ltd 2017
Thank you!
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