The document discusses artificial intelligence (AI) and its applications to combat decision-making. It notes that decision superiority will be key to future conflicts and that AI can help mitigate human cognitive limitations by assisting with complex decisions. The document also discusses how countries like Russia, China, and the U.S. are investing in AI for military applications including autonomous weapons. It proposes that human-AI teaming through technologies like augmented reality can help speed up decision-making at both tactical and strategic levels. Overall trust and transparency in AI systems will be important for their adoption.

1. Combat Decision-Making in
High Intensity Conflict
Williams Foundation March 2018
JD McCreary
Chief, Disruptive Technology Programs
Georgia Tech Research Institute

2. “Engage with what they expect; occupying their
minds while you wait for that which they cannot
anticipate.” – Sun Tzu
• Decision superiority is a key element to (future) conflict
• Complex decision space will require focus on technologies that mitigate
limitations on human sensory and cognitive capacity.
• Must develop trust in AI and its reliability through exposure and transparency

3. AI Arms Race
• “Artificial intelligence is the future, not only for Russia, but for all
humankind. Whoever becomes the leader in this sphere will become the
ruler of the world.” – Vladimir Putin, Sep 2017
• Artificial intelligence will be able to replace a soldier on the battlefield and a pilot in
an aircraft cockpit - Viktor Bondarev (former commander of Russia’s Aerospace Force
and Chairman of the Federation Council Defense and Security Committee) Nov 2017
• China’s State Council issued plan to lead the world in AI by 2030; “Next
Generation Artificial Intelligence Development Plan” - Jul 2017
• Highlights an approach of military-civil fusion (or civil-military integration) to ensure
that advances in AI can be rapidly leveraged for national defense
• Pursue advances in big data, human-machine hybrid intelligence, swarm intelligence,
and automated decision-making, along with in autonomous unmanned systems and
intelligent robotics

4. AI-assisted decision-making:
platforms to personal assistant, tactical to strategic
• Russia builds lethal autonomous weapons systems, platforms, swarms
• China plans to use AI to assist submarine commanders
• U.S. Data to Decision, Fleet Tactical Grid, Multi-Domain Command and Control
• Individuals, sensor/weapon/platform (man-unmanned teaming), Force design/employment
• Time scales, scope of complexity: human, machine
• Continuous learning, transfer of knowledge (mission, ready room, deployment)
• Integrated Tasking Order cycle (72 hrs), time sensitive targeting, kinetic or non-kinetic
• Grey Zone, Hybrid Warfare
• Deception, unconstrained solutions (not all playing the same game)
• Decision superiority preparedness (requirements)

6. Operational AI-assisted decision-making
• Cognitive, netted, distributed force (manned/unmanned systems, BM, MDC2)
• Algorithmic or prototype warfare (speed of software development)
• Human judgment will remain essential, but decision allocation between humans and machines will be shifting
• Man-machine teaming (Centaur or R2D2 at individual, platform, AOC/MOC)
• Human expertise/experience, workload, pace, complexity
• AI-assisted learning, COA generation, decision-making
• Trust, transparency, training
• Live, Virtual, Constructive (LVC)
• AI assist blue with complexity/scale: training, T&E, ops rehearsal, force/TTP design/validation
• AI augment/supplant red/white force, dynamic adaptation of force behaviors, environment
• Extend speed of command, decision superiority
• Computer, network, (sensor) data…knowledge, learning, decisions

7. Discussion

8. OODA vs OODA
Observe Orient
Decide
Act
Observe Orient
DecideAct
Traditional investment
• Decision superiority
• Man-machine teaming
• AI, visualization
• Big data, COAs
• Keep in orient phase
• Introduce “false” decisions

9. Force Design
• Outpace adversary decision speed
• Enhance battlespace awareness, decision speed, and dynamic
synchronized actions
• Computer aided data fusion, info analysis, present warfighting
options…enable decision speed
• Automated, intelligent analytics and AI designed to improve
Commander/Warfighter resource allocation decisions…providing COA
analysis, planning, automated synchronization, as a means of creating
effective human-machine teaming

10. The Intelligent Digital Mesh
• Intelligent: How AI is seeping into virtually every technology and
with a defined, well-scoped focus can allow more dynamic,
flexible and potentially autonomous systems.
• Digital: Blending the virtual and real worlds to create an
immersive digitally enhanced and connected environment.
• Mesh: The connections between an expanding set of people,
processes, devices, content and services to deliver operational
outcomes.
• Data to Decision, Fleet Tactical Grid, Multi-domain C2
• Sensors, networks, data, knowledge, decisions

11. Decision support technology
• The current wave of progress and enthusiasm for AI has been driven by three mutually reinforcing
factors: the availability of big data which provided raw material for dramatically improved
machine learning approaches and algorithms, which in turn relied on the capabilities of more
powerful computers. – Dec 2016 White House report
• Task vs data representation
• Supervised (classification), unsupervised (pattern analysis)
• Deep learning neural network
• Value, policy optimization
• Rational reasoning (IBM, Google)
• Memory augmented (deliberative; Differentiable Neural Computer)
• Imitation learning (Simulated data)
• Reinforcement learning (Deep Mind - Alpha Go Zero, OpenAI - DOTA 2, selfplay)
• Transfer learning

12. User Interface (UI)
• Enders Game, Star Trek, Interstellar, Minority Report, Watson, Alexa, chat bots, Hololens
• Computer’s job: interpret the user’s instructions, process the data, present to user.
• Human’s job: provide Commander’s intent, understand the information presented in
context of intended goals or surrounding environment.
• Moving away from designing Graphical User Interfaces (GUIs), which require the user’s full
attention, and moving towards designing less obtrusive interaction
• proactively initiate conversation about goals
• limit interaction to a few natural questions and responses
• factor in a large number of observations and assumptions
• present with the results
• Ipad vs laptop, mobile apps
• Conversational, visual, tactile/haptic
• Personalized UI/AI

13. • Develop 4 slides for APC
• 2 tech
• Computing, edge, fog, neural nets, AI algorithms, learning, transfer, DNC (inflective)
• Interface/trust, AR/VR/MR, conversational, haptic, LVC (transition to ops)
• 2 ops value
• Tactical/Operational (indiv/collective, platforms, ops centers)
• Force (execution/design/experiment)
• “static” decision making (pre-conflict) vs “dynamic” (real-time)
• 5th gen – more valuable as “decision makers”? Persistence of combat loads (kinetic vs non-kinetic;
persistence of info)
• Centaur approach
• Russia – hybrid warfare; China – informationized warfare (info dom)
• Even if you are not a fan of cyber/ew, you must respect manned/unmanned teaming potential
• Algorithmic or prototype warfare (speed of software development)
• Human judgment will remain essential, but the line of decision allocation between humans and machines will be shifting
in coming years.

14. AI beats pilot
• The secret to ALPHA's superhuman flying skills is a decision-making
system called a genetic fuzzy tree, a subtype of fuzzy logic algorithms.
The system approaches complex problems much like a human would,
says Ernest, breaking the larger task into smaller subtasks, which
include high-level tactics, firing, evasion, and defensiveness. By
considering only the most relevant variables, it can make complex
decisions with extreme speed. As a result, the A.I. can calculate the
best maneuvers in a complex, dynamic environment, over 250 times
faster than its human opponent can blink.
• ALPHA is currently viewed as a research tool for manned and
unmanned teaming in a simulation environment.

15. • Hmi – nlp, perception, visualization, audio, verbal, conversational, tactile
• Decisions - Autonomous machines, coa generation
• The software agent is an intelligent nonhuman agent (IA) that has clear objectives,
able to monitor its environment, and autonomous in the sense that it can generate
courses of action (COAs) to obtain its objectives
• mixed initiative autonomy wherein humans and IAs share decision-making. The IA
has a degree of autonomy and communicates with its human supervisor. The focus
of this report is on the decision-making relationship between IAs and its human
supervisor(s) in future battle spaces that are dynamic, dangerous, and complex
• Trust, transparency
• Industry, gaming

16. • operator’s understanding of the IA’s decision process: L1) operator
perception of the IA’s actions and plans, L2) comprehension of the
IA’s reasoning process, and L3) understanding of the IA’s predicted
outcomes.
• Whatever the IA’s knowledge representation, it must translate to
formats that humans understand to help foster a common language
between the IA and its operator.
• 3 gradations of language processing: command processing, controlled
language processing, and open-ended NLP.

17. Decisions
• We have not been making all the wrong decisions. AI just presents us
with available options based on more data and with less instinct than
we are accustomed to.
• Assessing situations without perfect information is not just a human
trait but a daily necessity and something we may not realize is
constantly happening. From negotiations to ranking new business
opportunities, we make informed decisions towards our desired
outcomes. Moving the needle upward involves having more relevant
details from more relevant sources.

18. Discussion of COP
• Crew and user specific task-related information, rather than commonality.
• Implications: The system needs to understand each users’ task context and what data would be relevant to those tasks, and
to present the relevant data elements in a way that makes sense to the task(s).
• Data for decision support tools and user views of networks and effects, rather than a single (picture)
portrayal of platforms.
• Implications: We need to agree on open standards for data models, and protocols for exchanging that data, not just a
standard set of icons.
• Prioritising near over distant, and aggregating distant things, rather than treating all information the same
way. This is in accordance with Tobler’s law: ‘everything is related to everything else, but near things are
more related than distant things’. In geospatial terms, these distances are in metres; in network terms, they
are in milliseconds.
• Implications: We need a system architecture that provides decentralised fusion and aggregation, to support decentralised
execution, and to manage throughput in a congested or contested network connection when things are getting emotional.
This requires adaptive rules for filtering; policy for when and how aggregation can support security outcomes for “special”
capabilities; and detailed understanding of how the filtering and aggregation affect decision support tools.
• Alerting of exceptions, rather than providing ‘as expected’.
• Implications: System definition of what is exceptional given the operational context, and the level of alerting that is
appropriate given the user needs, network capability and the other alerts.

19. Interactions
• On the Rise
• Human Augmentation
• Ambient Experiences
• Spatial Computing
• Gait Analysis
• Volumetric Displays
• Brain-Computer Interface
• Electrovibration
• VPA-Enabled Wireless Speakers
• At the Peak
• Smart Robots
• Transparent Displays
• Gesture Control Devices
• Emotion Detection/Recognition
• Virtual Assistants
• Machine Learning
• Muscle-Computer Interface
• NLP
• Intelligent Apps
• Sliding Into the Trough
• Mixed Reality
• Gaze Control
• Speech-to-Speech Translation
• Flexible Display
• Smart Fabrics
• Augmented Reality
• Head-Mounted Displays
• Sensor Fusion
• Climbing the Slope
• Face Recognition
• Virtual Reality
• Biometric Authentication Methods
• Ambient Displays
• Entering the Plateau
• Speech Recognition
• Handwriting Recognition

20. Trust
• Data scientists want to build models with high accuracy, but they also
want to understand the workings of the model so that they can
communicate their findings to their target audience.
• Users want to know why a model gives a certain prediction. They
want to know how they will be affected by those decisions and
whether they are being treated fairly. They need to have sufficient
information to decide whether to opt out.
• Regulators and lawmakers want to protect people and make sure the
decisions made by models are fair and transparent.

21. • Department of Defense (DOD) Directive 3000.09 (2012) mandates safeguards for
autonomous weapons, as shown in the following:
• “Semi-autonomous weapon systems that are onboard or integrated with
unmanned platforms must be designed such that, in the event of degraded or
lost communications, the system does not autonomously select and engage
individual targets or specific target groups that have not been previously selected
by an authorized human operator.”
• “The system design . . . addresses and minimizes the probability or consequences
of failures that could lead to unintended engagements or to loss of control of the
system.”
• “In order for operators to make informed and appropriate decisions in engaging
targets, the interface between people and machines for autonomous and semi-
autonomous weapon systems shall: (a) be readily understandable to trained
operators, (b) provide traceable feedback on system status, and (c) provide clear
procedures for trained operators to activate and deactivate system functions.”

22. AI & ML Terminology
Mythology
Centaur
AI
Centaur
Close the orient & decision loop
Outpace adversary decision speed

23. Terms
• The term "artificial intelligence" is applied when a machine mimics
"cognitive" functions that humans associate with other human minds,
such as "learning" and "problem solving"
• Goals of AI research – perception, knowledge, reasoning, planning,
learning, natural language processing, and the ability to move and
manipulate objects
• Capabilities generally classified as AI include understanding human
speech, images and videos, autonomous cars, competing at a high
level in strategic game systems (e.g – DOTA 2 and Go), military
simulations, and interpreting complex data.