Project Albert
M&S Review 13 November 2002
Gary Horne
Sarah Johnson
1
Project Albert Summary
• Chartered by Congress with the vision to address questions
of military decision-makers not supported by traditional methods
• Leverages High Performance Computing in innovative ways
• Stresses interdisciplinary, joint, and coalition collaboration
• Supports rapid investigation of a wide range of alternatives
through distillation models and data farming methods
• Applications include efforts in the areas of surf zone obstacles,
Command and Control, and enhanced blast weapons
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Distillations
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Distillation Models – Agent-based model that is intuitive,
transparent, transportable, and farmable.
Agents – autonomous, reactive, motivated, adaptive,
mobile, proactive, and they communicate.
A bottom up distillation of the essence of a question
Quick implementation - less than a few hours
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What Are Distillations?
• A distillation is a bottom-up abstraction of the essence of a
situation.
• Characteristics of a distillation?
 Can handle nonlinearities, binary events, sensitivity to initial
conditions, emergence
 Adaptable to massively parallel machines
 Can create data needed for analysis
 Shows promise to capture intangibles and model coevolving
landscapes
 Good for modeling complex adaptive systems
 And enhances our intuition
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Which Distillations Does
Project Albert Use?
– ISAAC
– Mana
– Socrates
– Pythagoras
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ISAAC
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ISAAC Interfaces
SELECT RUN OPTION
[1] Run ISAAC engine with new input
[2] Playback old run
[3] Quit
?1
SPECIFY FORM OF INPUT
[1] Prompt from screen
[2] Read from datafile
?2
File name ?ISAAC.DAT
SPECIFY FORM OF OUTPUT
[1] Terminal
[2] File
[3] Both
?3
NUMBER OF ITERATIONS TO STORE IN OUTPUT FILE ?20
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ISAAC Features
• Physical Characteristics
– Movement range, sensor range, fire range, communications range
• Personality Traits
– Internal value system which weights movement toward or away from
same/opposing goal, alive/injured friendly/enemy agents as applied to all other info
used by agent to choose a move
– Used to rank set of possible moves an agent can choose to take during current time
step
• Battlefield
– Two-dimensional lattice of discrete sites
• Agent States
– Alive, injured, dead
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ISAAC Features
• Movement
– Next move based on best matching the agent’s personality
• Group Sociological Factors
– Constraints such as Advance, Cluster, Combat included
• Advance: toward enemy goal if #of friendly agents exceeds defined threshold
• Cluster: stop moving toward surrounding friendly agents if # exceeds defined threshold
• Combat: move toward enemy if difference between friendly/enemy agents exceeds defined
threshold
• Scenario Generation
– Text file
– On-screen manipulation
• Data Farming
– Web Interface to MHPCC
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Mana
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Mana Interfaces
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Mana Features
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Originated by the Defence Technology Agency (DTA), New Zealand
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Event-Driven Personalities
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Squad-based memory of enemy contacts
Terrain Map
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Speed, sensor range, fire range, stealth, max targets to engage - defined for each personality state
Situational Awareness
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Triggering events such as squad shot at, squat taken shot, reached way point which cause a change in personality weight
vector for a specified length of time before reverting back to the default personality or until another event triggers
another personality type
Personality traits defined for each personality state
• Internal value system which weights movement toward or away from waypoints, alive/injured friendly/enemy
agents as applied to all other info used by agent to choose a move
Physical
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Parameters/personality traits/rules defined similarly to the ISAAC distillation, if not the same for some variables
Roads that agents can follow, impassable terrain objects
Way Points
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Establish set of points to follow (interim goals), not just an ultimate goal
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Mana Features
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Group Sociological Factors
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Battlespace
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Situational awareness map - agents communicate enemy positions to other squads outside the local area
• HQ squad sends info in their collective sensor range to the SA map where other squads of the same allegiance
can view the information
Edited/created using paint program (create object/obstacle blocks) without specifying coordinates. Agents recognize
blocks as visible while program executes
• Can also import maps into paint - can draw roads for agents to follow
Scenario Generation
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Unit Cohesion, Aggression
• Advance: toward enemy goal if # of friendly agents exceeds defined threshold
• Cluster: stop moving toward surrounding friendly agents if # exceeds defined threshold
• Combat: move toward enemy if difference between friendly/enemy agents exceeds defined threshold
Defined for each personality state
Scenario text description opens upon execution of the model - provides background information of scenario for new
users of that file
Text file with corresponding map file
On-screen manipulation using previously created scenario
Data Farming
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Gilgamesh Cluster
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Socrates
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Socrates Interfaces
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Socrates Features
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Improvements under development
Methodology
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Command hierarchy
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sensors, weapons (lethal/non-lethal), movement, communications, sentiments, inculcation,
accommodation
personality defined by list of decisions (based on level of command)
Battlefield
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Commander, Leader, Grunt
Agents
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Values-driven behaviors
Obstacles (impede movement only)
Data Farming Support
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Inputs/Scenario XML-based
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Pythagoras
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Pythagoras Interfaces
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Pythagoras Features
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Improvements under development
Methodology
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Agents
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Soft Decision Rules
sensors, weapons (lethal/non-lethal; direct/indirect), movement desires, triggers
“Sidedness”
– multiple sides possible based upon RGB color scale
– sides change dynamically throughout scenario execution
– affiliations categorized by unit, friendly, neutral, enemy
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Battlefield
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Base terrain properties of mobility, concealment, protection, height
Terrain features with same property categories can overlay base
Data Farming Support
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Inputs/Scenario XML-based
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Distillation Model
Comparison
Decision Logic
Agent States
Command &
Control
Behavior Rules
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ISAAC
Mana
Socrates
Pythagoras
Physics-based, hard-coded
decision methodology
Physics-based, hard-coded
decision methodology
Values-Driven DecisionMaking Methodology
Physics-based, “soft” decision
logic
Alive, Injured, Dead
Event-Triggered Personalities
Alive, Dead
Event-Triggered Personalities
Global Commander
Local Commander
Agent
Personality Weights
Physical characteristics
Trust, Cohesion,
Aggression
• Squad
• Personality Weighted by
Triggering Event
• Physical characteristics by
Agent Definition
• By Squad
Triggering Event
• Cohesion, Aggression,
Allegiance
• By Squad
Squad Definition
• Multiple agent
• Multiple Triggered
personalities
• Multiple physical
parameters
Personalities
• Multiple Physical
Parameters
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Commander
Leader
Agent
Hierarchical positions
determine decisions/tactics
• Physical characteristics
• Trust, Allegiances
• Threshold-defined
Leadership
• Agent
• Personality Weighted by
Triggering Event
• Physical Characteristics by
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• Individual agent defined
• Instantiate any number of
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given agents
• Leadership hierarchy
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defined
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Terrain
Physical &
Tactical
Parameters
Development
Status
Rectangular obstacles
impede movement and shot
capability, but not LOS
• sensor, fire,
communications ranges,
speed
Complete – No new
development planned
Bitmap with pre-defined
terrain-types impede
movement, but not LOS
• sensor, fire, situational
awareness ranges, speed
Movement obstacles impede
movement, but not LOS nor
shot capability
• sensor, weapons,
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Triggering Event
Cohesion, Aggression,
Sidedness
Individual agent defined
Instantiate any number of
given agents
Group personalities based on
triggers
Can be defined by
“Sidedness”
Multiple physical parameters
Terrain Editor with user
definable polygons provide
protection, concealment,
trafficability
sensor, fire, speed
communication
channels/ranges, speed
Versions released;
Versions released;
Beta released;
Under development
Under development
Under development
Distillation Models Status
ISAAC
Mana
• Development completed
• First distillation developed for Marine Corps
• Batch processing/Data Farming mode is ready
• Newer versions under development by
and in use; Web interface available
• Limited set of questions/scenarios to explore
• Continued use is includes scenario setup,
execution, and translation into other
models/validation against other models
Defence Technology Agency, New Zealand to
expand features, parameters, MOEs, batch
processing
• Batch processing/Data Farming mode is
available on Gilgamesh Cluster; in
development for MHPCC; Requires email
submission
Socrates
Pythagoras
• Developed by L-3 Communications Analytics
• Developed by TRW under contract with
under contract with Project Albert
• Data farming sessions available at MHPCC;
Web interface available
• Prioritization and development of additional
features, visualization tools for result analysis,
user interfaces for scenario creation and initial
scenario testing is in progress
Project Albert
• Version 1 delivered
• Data farming sessions available at MHPCC;
Web interface available
• Prioritization and development of additional
features in progress