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This document and the information contained herein is the property of Saab AB and
must not be used, disclosed or altered without Saab AB prior written consent.
Artificial Intelligence im Kampfjet
Tommy Busk
Head of Technical Discipline Tactical Systems
Saab Aeronautics
2 April 2019
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Agenda
• What is Artificial Intelligence (AI)?
• Progress of AI
• AI in Aerospace
• Challenges
• Research
• Future
• Final questions
• Caveat: ”Due to Public nature of this
presentation only general statements can be
made”
2
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What is Artifical Intelligence?
• No commonly accepted definition, elusive, perception inflation, compare with chess computer
• The term ”AI” widely used as enhancer
• “Artificial Intelligence is the art of making computers work the way they do in the movies”
• ”Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.” Prof. John McCarthy, Stanford
• Distinction: Narrow AI vs Wide AI
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2017: Go master Lee Sedol is beaten by AI, Source: Netflix
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Progress of AI
• Research on AI since the 1950’s
• The Golden years 1956-1974
• AI Winter 1974-1980
• Boom 1980-1987
• Second Winter 1987-1993
• 1997 AI beats Garry Kasparov
• DARPA Urban Challenge 2005, 2007
• Watson 2011
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Progress of AI
• Deepmind AlphaGo Zero 2017
• DeepMind Starcraft 2019
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AI in Aerospace
Examples of applications in Aerospace:
• Design
• Generative design in combination with 3D-printing
• Software Development
• Classic code vs machine learning
• Training
• Computer Generated Forces
• Aid
• Fighter
• Sensors
• Decision support/making
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Generative Design: Autodesk in collaboration with Airbus, 30 kg lighter
Computer Generated Forces
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Software Development
7
Study the
problemWrite rules
Analyse
errors
Ready
Evaluate
Study the
problem
Train
algorithm
Analyse
errors
Ready
Evaluate
Data
set
Classic Coding
Machine Learning
”If x happens do y”
Accumulating, curating, cleaning
• Classic code versus Machine Learning
• Training of AI algorithims requires data Data
set is critical
• But code is not explainable
• Problem correction – how to find the error?
How to find the cause?
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Example - Reinforcement Learning
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Beats humans in e.g. Go, Dota and StarCraft II
But needs much training;
Montezuma’s Revenge
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Improved training effectiveness and
value
• More complex scenarios can be
realized
• Reduction of human training
personnel
Pilot Training
• Improved behaviour models for Computer Generated Forces (CGF):
• Adapt to training needs, e.g. gradually more difficult for the student
• Parameterized behaviour, similar to giving instructions to human role-players, e.g. aggressiveness
• Multi-agent collaboration
• Agent-human interaction (e.g. team members)
• AI adapts the curriculum
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Fighter – Decision Support/Makning
• How to support the pilot?
• OODA-model
• John Boyd - Fighter pilot in the
Korean war
• ”Boyd advances the idea that
success in war, conflict,
competition even survival hinges
upon the quality and tempo of the
cognitive processes”
• Rapid and right decision making
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Analysis, what does input mean?
”I understand that I will collide
unless I brake or shift direction”
”I see something
blocking the road”
Environment
”Drivning on a road”
”I will brake”
”Pushing down
brake pedal”
”Speed is
reduced”
”I process the pros and
cons with either option”
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Fighter - Decision Support/Makning
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Situation Analysis
• Rule based or DNN
• Reasoning
• Object recognition
Situation Awareness
and Decision Support
Human Machine Interface
• Displays
• Audio
Sensors
• Signal Processing
• Target tracking
• Information fusion
• Target classification
Environment
Decision Making
• Long-term objectives
• Short-term objectices
• Boundary conditions
Decision Making
• Stress
• Time to decide
• Flexibility
Human Machine Interface
• Speech recognition, NLP
• Gesture recognition
What machine
does best
What human
does bestAI Pilot Monitoring
• Autonomy level
• Information adaptation
ActionResource utilisation
and optimisation Impact
Feedback loop
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Challenges – Real World Complexity
• Balancing short and long-term goals and
adapt to unexpected situations
• Making complex predictions over very long
sequences of data where cause-and-effect is
not instantaneous and actions taken early may
not make an impact for a long time.
• Handling a large action space
• Real-time management
• Manage incomplete information
• Act in an environment that actively tries to
exploit its weaknesses
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Challenges – Real World Complexity
• Different Missions:
• Offensive/defensive counterair (DCA/OCA) scenarios, with
progressively more entities
• Attack operations: Attacks on missile sites, airfields, C2 and
infrastructure
• Suppression of Enemy Air Defenses (SEAD), Fighter Escort, Fighter
Sweep
• Destroy hostile air threats
• Identification/Combat identification and warning
• Considerations
• Weapon Engagement Zones (WEZ)
• Minimum-risk routes
• Jammed environments
• Rules Of Engagement (ROE)
• Threats
• Aircraft: Fighters, bombers, intelligence, EW, tankers, transport,
helicopters
• Air defence systems
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Challenges – Computing Power
• Similar challenges as for real-time strategygames (e.g. Dota and StarCraft II):
• Long time horizons for decision making:
• Dota ~20000 moves per game (45 min), Go ~150 moves per game, chess ~40 moves per game
• Imperfect information, e.g. sensor limitations and EW
• Complex observation and action spaces
• As a result, exploration takes longer time:
• E.g. training of Dota: 128,000 CPU cores for simulation rollouts and 256 GPUs for training of neural network model (~180 years of game play experience per day)
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Research - WASP
• Wallenberg AI, Autonomous Systems and
Software Program (WASP) is Sweden’s largest
individual research program ever
• “The program addresses research on artificial
intelligence and autonomous systems acting in
collaboration with humans…”
• 180M€ 2015 + 100M€ 2017
• Vision: Excellent research and competence in
artificial intelligence, autonomous systems and
software for the benefit of Swedish industry
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Research Arena Public Safety provides a realistic, large scale and
industrially relevant demonstration environment
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The Future
• Development
• Big Data techniques for post-flight evaluation
• Increased use of AI in aircraft design and production
• Increased use of AI in software development
• Operative use:
• Unmanned platforms augmenting manned platforms in Manned-Unmanned teaming – i.e. work with machines. Best of both worlds strengths.
• Unmanned platforms get increased autonomy over time to achieve high-level goals
• Trust. Predictability.
• Challenges
• Ethics and conventions, Rules-Of-Engagment
• Verification and validation.
• Development in US and China vs Europe?
• Competence
• Safety: That AI never takes a fatal decision, even in case of unpredicted situations?
• Security: That AI cannot be hacked by a malicious person?
• Certification: AI is intrinsically non-deterministic (since it has to face unpredicted situations) How to certify non-deterministic AI?
• Collaboration is key
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Questions and Wrap-up
• Final questions?
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