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![Page 1: AI on the Battlefield: an Experimental Exploration Alexander Kott BBN Technologies Robert Rasch US Army Battle Command Battle Lab Views expressed in this.](https://reader035.fdocuments.in/reader035/viewer/2022062408/56649f2c5503460f94c4775b/html5/thumbnails/1.jpg)
AI on the Battlefield: an Experimental Exploration
Alexander Kott
BBN Technologies
Robert Rasch
US Army
Battle Command Battle Lab
Views expressed in this paper are those of the authors and do not necessarily reflect those of the U. S. Army or any agency of the U.S. government.
Kenneth Forbus
Northwestern University
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Outline
Motivation for the experiment The experimental rig Experimental procedure Findings A surprising challenge uncovered
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The Role of BCBL-L
Exploration of new techniques and tool for Army C2 – a key focus of BCBL-LApparent emergence and maturing of multiple technologies for MDMPWhat is the right way to apply such technologies? Value? Drawbacks?BCBL-L proposed and executed the Concept Experimentation Program (CEP) - Integrated Course of Action Critiquing and Elaboration System (ICCES)
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Room for Controversy
Some call for “…fast new planning processes… between man and machine… decision aids…” Extensive training and specialization requirements?Detract from intuitive, adaptive, art-like aspects of military command?Undue dependence on vulnerable technology? Make the plans and actions more predictable to the enemy?The experiment was designed to address such concerns
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The Experimental RigCOA Creator, by the Qualitative Reasoning Group at Northwestern University - allows a user to sketch a COAThe COA statement tool, by Alphatech, allows the user to enter the COA statement Fusion engine, by Teknowledge, fuses the COA sketch and statementCADET, by Carnegie Group & BBN – elaborates the fused sketch-and-statement into a detailed plan and estimates
Input:Mission and Intelligence
Analysis
CADETTool
FusionTool
COAStatement
Tool
COACreator
Tool
Output:Detailed
Synchron.Matrix
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The COA Entry Bottleneck
The key bottleneck in MDMP digitization: Time / effort / distraction Training requirements Downstream representation language
Our approach – COA Creator, based on nuSketch Sketching = interactive drawing plus linguistic I/O Rich conceptual understanding of the domain Speech often not preferred in mix of modalities Include “speechless” multimodal interface (buttons
plus gestures) Expressible in the underlying knowledge representation
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Terrain features and characterization
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Units and control lines
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Objective and engagement areas
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Friendly tasks are defined
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The Experimental Procedure Comparison with the conventional process Exploratory vs. statistical rigor
Training
Interviews,Products Review
Team 1, Case 2Team 2, Case 2
Team 2, Case 1Team1, Case1
ConventionalManualProcess
ICCES-Based
Process
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Key Findings Low training requirements
Largely due to “naturalness” of sketching Simple, frugal CONOPS
No impact on creative aspects of the process Largely driven by human-generated sketch-and-
statement Opportunity to explore more options
Dramatic time savings (3-5 times faster) Mainly in downstream processing (e.g., planning)
Comparable quality of products Few edits of ICCES-built products Comparable quantitative measures (e.g., friendly losses)
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Parallel Experiments – Quality of Plans
0
0.05
0.1
0.15
0.2
0.25
0 2 4 6 8 10 12
CADET
Human
Rigorous experimental comparison: computer-assisted vs. conventionalMultiple cases, subject, judgesConclusions: indistinguishable quality of products, dramatically faster
Products of 5 past exercises
Grade by 9“Blind” Judges
Generate w/ CADET
Give “computer look”
inputsoutputs
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Surprise: Plan Presentation is a Key Concern
Conventional output presentation paradigms, i.e., sync. matrix is ineffective Larger number of
elements Inadequate spatial aspect Difficult to detect errors
Alternatives: Animation? Cartoon sketches?
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Conclusions
For Army professionals:Technologies like ICCES have near-term deployment potentialNo impact on creativity, predictabilityDramatic acceleration, comparable qualityChallenges in inspecting, comprehending the new MDMP products
For AI R&D community:Dominant role of HMI challenges calls for new mechanismsValue of natural sketch-based interfacesSimple, straightforward, all-in-one CONOPS for usersNo substitute for comparative experiments, from both practical and research perspectives
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