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Pythagoras - an Agent-based Distillation. A Presentation to ABSVal Workshop. 25 March 2008 Edd Bitinas, Chief Modeler – 703-968-1196 Brittlea Sheldon, Analyst – 703-968-1137 Northrop Grumman Corporation. NORTHROP GRUMMAN PRIVATE/PROPRIETARY LEVEL 1. Introduction To Pythagoras

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Pythagoras an agent based distillation

Pythagoras - an Agent-based Distillation

A Presentation to ABSVal Workshop

25 March 2008

Edd Bitinas, Chief Modeler – 703-968-1196

Brittlea Sheldon, Analyst – 703-968-1137

Northrop Grumman Corporation

NORTHROP GRUMMAN PRIVATE/PROPRIETARY LEVEL 1


Overview

Introduction To Pythagoras

What Is Significance Of Pythagoras And Why We Developed It

How Pythagoras Works

What We Have Done To Improve Pythagoras

What Our Clients And Other Users Have Done With It

Overview


The pythagoras environment

The Pythagoras Environment

  • Decision Making

  • Intelligence

  • Communication

  • Inspiration

Leadership

  • Morale

  • Training

  • Confidence

  • Fatigue

  • Fear

  • Comradeship

  • Political Leanings

  • Anger

  • Influencers

  • Sensors

  • Platforms

  • Resources

  • Terrain

  • Weather

Human

Factors

Physics

The Three Regions Are Tightly Linked


Types of models simulations

Physics-Based

Verifiable through Experimentation

May Not Account for All Interactions

Example: Weather Models

Outcomes Could Be Calculated, but Math Is Too Hard

Probability-Based

Includes Some Physics

Account for Some/Most Random Effects

Range of Outcomes Could Be Calculated (See Above)

Agent-Based

Decision-making Entities

Includes Probabilities

Outcomes Not Predictable

Types of Models/Simulations

  • Software Objects That:

    • Are Capable of Choice

    • Have Autonomous Behaviors and Are Distinct from the Background or Environment and Each Other

    • Perform Actions Like:

      • Sense

      • Order

      • Communicate

      • Move

      • Influence

Pythagoras


A brief history of pythagoras

July, 2001 – Northrop Grumman Wins USMC Contract To Support Project Albert via Archimedes Development and Modification

September, 2001 – Northrop Grumman States ‘It Can’t Be Done.’ Project Albert Leadership States ‘We Need an Agent-Based Tool That:

Uses Fuzzy Logic

Can Run Both in a Batch Mode at Maui High Performance Computing Center (MHPCC) and from a GUI on a PC

Is Easy Enough That a Non-Computer Science Major Can Use It.’

Pythagoras Development Timeline

April 2002 – Pythagoras v1.0 Is Delivered: Reproduces Archimedes Functionality

June 2003 – Pythagoras v1.6.3 Is Delivered: Added Complete Behavior Change Triggers

September 2005 – Pythagoras v1.9.0 Is Delivered: Influencers Replace Weapons

September 2006 – Pythagoras v1.10.5 Is Delivered: Adds Communications Devices

April 2008 – Pythagoras v2.0.0 is Delivered – Upgrade to JAVA 1.5, JAXB 2.0, Add More Attributes, More...

A Brief History of Pythagoras


What is pythagoras

Pythagoras Is an Agent-Based Simulation System That Puts the Complexity of Creating Behaviors in the Hands of the Analyst, Not in the Software

It Offers the Analyst the Ability To:

Mirror Actual Groups, Including Their Characteristics, Beliefs, and Standards of Behavior

Examine Outcomes Based on Behavior

Data Farm Over a Trade Space Using High Performance Computer Clusters

Execute a Range of Runs

Similar to Sensitivity Analysis

Design of Experiments Can Reduce Runs and Preserve Interactions

Assess Impact of:

Uncertainty in Inputs

Some Idea of the Minimum-To-Maximum Range

Unclear Interactions Among Inputs

User Interface Is Easy To Understand and Manipulate

The Structure Allows the Functionality To Evolve as New Scenario or Operational Requirements Are Identified

What is Pythagoras?


Pythagoras software components

Pythagoras Software Components

Engine

MOE

Output

CLI

Scenario

Movement

Output

Legend

XML File

GUI

JAVA Software

Text File


How does pythagoras work

Soft Decision Rules

A Way To Capture Variability

Agent’s Decisions

Movement– Influencing

Generic Attributes

Characteristics To Measure

Controls On Behavior

Dynamic Affiliation

Same Unit– Same Side

Enemy– Neutral

Rule-Based Influencers

Lethal– Non-Lethal

Triggers

Change Behavior Rules When Triggered

MOEs

Sensors (Three Bands)

Comms (Three Channels)

Terrain Features

Mobility– Protection – Height

Concealment (Three Bands)

How Does Pythagoras Work?


Agent characterization

Affiliation/Uniform (Shades of Red, Green, Blue)

Vulnerabilities (Multiplier on Lethality and Affiliation Changes)

Up to 10 Optional Attributes

Attribute Vulnerability (Multiplier on Influencers Affecting Attributes)

Leadership (Charismatic or Hierarchal)

Obedience – Repeating Orders Results in Nagging

Knowledge Retention

Communications Effectiveness

Marksmanship – Strength of the Ability To Influence

Height and Altitude

Detectability (Multiplier on Sensor Detection)

Vulnerability (Multiplier on Lethality)

Speed, Speed Variability

Movement Selection Mode (Mode 1, 2, 3 or 4)

Has Weapon Type (Up to Ten)

Has Sensor Type (Up to Ten)

Has Comm Type (Up to Ten)

Next Waypoint

Waypoint Proximity (How Close Is Close Enough?)

Target Value

Hold-Fire Desire

Target Persistence

Fuel Tank Capacity, Fuel Initial Load, Usage per Step

Resource X,Y,Z Capacity, Resource X,Y,ZInitial Load, Usage per Step

Agent Characterization

Italics Indicate Possible Soft Decision Rule Variable


Agent time cycle

Agent Time Cycle

Die

Self Evaluation

Clear Knowledge

Record Info

Draw GUI Info

Sense Environment

Change Color/Attributes

(if Necessary)

Communicate Knowledge

Choose Leader

Move (if Can)

Re-Supply (Resources)

Pick Direction

Load Balance (Resources)

Set Speed

Change Behavior

(if Trigger Tripped)

Influence

Check if at Waypoint

Reset Influencers &

Pick Targets


Time series moes

MOEs Recorded Over Time Include:

Agent Red, Green, Blue

Agent Attributes

Agent Behavior

Agent Fuel, Resource X, Resource Y and Resource Z

# Detected Units (Friend, Enemy, Neutral)

# Known Units (Friend, Enemy, Neutral)

Distance to Final Objective

Killed By

Kills Scored

Shots Fired

Survival (Alive, Dead, Injured)

Recording Methods Include:

Individual Agent

Entire Class Average

Frequency Options:

Not Collected

End of Run

Beginning and End of Run

Beginning, End and Every x Time Steps

Time Series MOEs


End of run moes

Initial, Minimum, Maximum and Final Number of Agents Near the Final Objective

This Can Measure How Agents Force the Enemy To “Run Away”

Initial, Minimum, Maximum and Final Average Number of Unit, Friendly, Enemy, Neutral and Unknown

Initial, Minimum, Maximum and Final Value of Red, Green, Blue, Attributes 1-10

Initial, Minimum, Maximum, Final and Used Amount of Resource X, Resource Y and Resource Z, Fuel

Number of Survivors by Class

Number of Casualties by Class

Both Injured and Dead

Number of Shots (by Influencer) and Effects

End of Run MOEs


New in pythagoras 2 0 0

Modernization

Java 1.5

JAXB 2.0

4000 X 4000 Pixel Playbox

Multi-Dimension Visualization

Map Location, Color, Health, Attributes and Resources to x, y, r, g, b, and Transparency

Ten Generic Attributes

Changed By:- Changer Types

Weapons • Incremental

Communications • Absolute

Events • Relative

Terrain • Multiplier

Can Be Normalized

New in Pythagoras 2.0.0


Pythagoras 2 0 0 cont

Agents Carry Up To Ten Each Of

Sensors

Weapons

Communications Devices

Agents May Now Shoot At Multiple Targets Per Time Step

Pythagoras 2.0.0 (cont.)

Status: Delivered To OAD & NPS, April 2008


How has pythagoras been used

Terrorist Development (Sandia Labs)

Conceptual Model of Human Factors: Explored Terrorist Recruiting

Population Dynamics (USMC)

Disaster Relief and Counter-Insurgency: Examined Impact of MAGTF Commander’s Alternatives on Insurgency

MAGTF Optical Requirements (Night Vision Lab/Ft. Belvoir)

Peacekeeping at Night – Comparison of Alternative Optical Technologies/Suites

Historical Analysis (USNA)

Battle of Midway

Battle of Ia Drang, Viet Nam

Thermobaric Weapons Assessment (MCCDC)

Urban Environment

Shallow Water Obstacle Clearing (MCWL)

Use of JDAM and Robotics

Homeland Defense (Northrop Grumman)

Pre-Proposal Analysis of Critical Node Defense from WMD

Environmental Concerns (MITRE)

Spread of Hemlock Woolly Adelgid

User Community

NPS– SOCOM – USNA

OSD– USMC/MCCDC – TRAC/Monterey

Northrop Grumman– USMA – Many Others

How Has Pythagoras Been Used?


Summary

Government-Owned, Open Source

Wide Variety of Potential Applications

Able To Deal with Uncertainty

Scenarios Are Expandable as New/More Data Become Available

Not Limited to One Theory/Approach

Compare Theories with the Same Software

No ‘Scripts’

Group Behavior Emerges from Individual Behaviors

Scenarios Can Be Built In Hours

Complexity Can Be Added As Needed

Summary


Backups

Backups


What is data farming

Similar To Sensitivity Analysis

Multiple Simultaneous Variations In Inputs

Stochastic Scenarios May Require Tens Of Thousands Of Runs

Use Super Computer Clusters

Design Of Experiments Can Reduce The Number

Near Orthogonal Latin Hypercube,

D-Optimal, Others

Identify Correlations And Trends

Outliers Also Provide Insight

Why Was It Different?

Can That Difference Be Exploited?

What Is Data Farming?


Sample animation output

Sample Animation Output

Improvised

Explosive

Devices

Convoy of Vehicles

Detonator


What s next

Event-Driven Engine Option

Current Version Is Time-Stepped

Arc/Node Terrain

Network-Oriented Terrain Option Would Provide New Capabilities

Configurable GUI

Start Simple, Add Capability as Needed

Additional Functionality

Imperfect Knowledge About Affiliation

More Fire And Movement Desires

“OR” Options for Attributes and Affiliations

Moving and Shooting Detection

Explicit Initial Locations

Many Others...

Application-Driven Upgrades

What’s Next?


Soft rules

An Attempt To Turn English Language Orders Into Numbers

Example: “Don’t Communicate Until The Enemy Gets Close Enough”

How Close Is Close?

People Interpret And Apply Orders Differently

Example: A Team Agrees That ‘Close’ Is 100 Meters.

Some Start Talking At 115 Meters; Others Wait Until 90 Meters

People Interpret And Respond To Input Differently

Example: MAGTF Optical Scenario

Hungry Villagers Move Toward Marines, Terrorists Move Away From Marines

Soft Rules Attempt To Capture This Variability

Each Agent Selects His Own Thresholds From An Input Range

Random Distribution May Cause Emergent Behavior

Expect The Unexpected

Soft Rules


Example of individualized numbers

Example of Individualized Numbers

Fraction of Total

Common Decision Threshold: Mean = 5

Firm/Low Individuality: Range = 1

Soft/Medium Individuality: Range = 2

Loose/High Individuality: Range = 4

Homogenous Populations React Alike

Heterogeneous Populations May React Differently or at Different Times


Agent decision variables

INFLUENCER/WEAPON:

Hold Influence Unless Being Influenced/Actively Influence

Highest Effectiveness/Lowest Effectiveness

MOVEMENT:

Toward (if > d) or Away From (if < d) Closest Leader

Toward (if > d) or Away From (if < d) Closest Unit Member/Friendly

Toward Furthest Unit Member/Friendly (if > d) – Group Cohesion

Toward Next Waypoint (if > d)

Toward (if > d) or Away From Nearest Enemy (if < d)

Toward Small Number of Enemy (if # Enemy or Ratio within d < e) – Advance

Away From Large Number of Enemy (if # Enemy or Ratio within d > e) – Withdraw

Toward Objective

Toward Injured Friend – Restore or Heal

Toward Friend (if > d) Needing Fuel, Resource X, Resource Y, Resource Z

Toward Friend (if > d) Supplying Fuel, Resource X, Resource Y, Resource Z

At Random – Crowds

Continue Same Direction

Stay in Place if Desire To Move Is Weak –Sloth

TERRAIN:

Avoid Bad Terrain if (Low Mobility or Poor Concealment or No Defense)

Prefer Good Terrain if (Azimuth, High Mobility, Good Concealment, or Protection)

Agent Decision Variables

d – Distance, e – Enemy Count All Breakpoints Are Soft


Movement implementation alternatives

Each Agent Calculates the Movement Direction for Each Non-Zero Desire

They Are Then Combined Either by:

Using Vector Algebra, with the Desires as a Weight, or

Picking the ‘Strongest’ One (the Highest Desire), or

Averaging the Top Two, or

Using Monte Carlo, Weighted by Desire

Thus, Movement Directions Are Calculated Based on the Current Situation, Not Scripted in Advance

Movement Implementation Alternatives


Sensor modeling

Each Terrain Grid Point (1 x 1) Has a Concealment Value for Each of Three User-Defined Bands

For Each Grid Box Traversed by the Line of Sight (Rather Than Proportional), the Concealment Value Is Multiplied

V = V * (1 - C)

Each Agent Pair Draws a Random Number for Each Band at the Beginning of the Run

Determines the Range at which Detection Occurs from Input Pd Curves

Detectability Is Held Constant during the Run

Modified by Current Concealment

Sensors Are Either Personal (Owner Only) or Broadcast (Owner and All Friends within Range)

Target Location Error (TLE)

Only Used for Communicating Sensed Agents’ Locations

Sensor Modeling


Sidedness

Three Primary Alternatives (Represented by RGB)

Agents Could Have Multiple Sympathies

User Decides Which Colors To Use for Color-Distance Calculations (1, 2 or 3 Dimensions)

Affiliation Changes Add or Subtract Color

Example: Shot At = +.01 Green, -.01 Blue, -.01 Red

Distances and Values Are All Soft Rules

Behavior Change Triggers Occur When Sidedness Thresholds Are Exceeded

Example: Green > .5 or Red < .5, Read New Behavior = Surrender

Alpha (Transparency) of 0.0 Means Agent is Dead

Sidedness


Example two dimensional affiliation

Example Two-Dimensional Affiliation

Me

My Unit

Friendly

Neutral

Enemy

255

Blue-ness

0

0

255

Green-ness


Attributes

Ten Alternatives: 1-10

Similar to Sidedness Except “Affiliation” Is Optional

Attribute Change Rules Add To or Subtract From an Attribute

Example: Shot At = +.01 Att[0], -.01 Att[1], -.03 Att[6].

Distances and Values Are All Soft Rules

Behavior Change Triggers Occur When Attribute Thresholds Are Exceeded

Example: Att[4] > .5 or Att[6] < .5

Read New Behavior = Follow Leader or Become Suicide Bomber

Attributes


Side or attribute change events

Know About Leader

Leader from Same Unit Found Out About

Don’t Know About Leader

No Leader from Same Unit Found Out About

See Leader

Too Many Enemies

Too Few Friends

Outnumbering by a Ratio

Outnumbered by a Ratio

Being Influenced

One Change for Each Occurrence

Influencing

At Waypoint

At Objective

All Is Well

Used To Restore Original Conditions

Example: No Leader for a While, Then Leader Is Reacquired

Side or Attribute Change Events


Influencer types

Two General Types

(Up To Ten InfluencersPer Agent)

Directed Target

Requires Line of Sight (LOS)

P(hit) and P(influence) Are a Function of Range

Examples: Leaflets, Food

Only Affects Target

Area Effect

No LOS Required

Accuracy (Hit Vs. Aim) a Function of Range

P(influence) a Function of Distance from Hit Point

Examples: Bull Horns

Affects All Agents within Radius

User Chooses Effect

Carlton Damage Function Effect (Degrades with Distance), or

Cookie Cutter Effect (Constant within Radius)

InfluencerTypes

Non-Lethal Effects

  • Suppression

    • Requires a Hit by Directed Target

    • Requires Possible Influenceby Area Effect

    • Causes Agent To Stop All Activity for Input Time

      • No Movement

      • No Sensing/Broadcast

      • No Influencing

  • Restore/Resurrect

    • Remove Damage (e.g., Medicine)

  • Paint Balls

    • Change the Color/Attribute of Targeted Agent

      • Absolute, Incremental, or Relative to Agent Firing the Weapon

      • May Cause Triggers To Execute


Abstract terrain

Fixed Box Size of Up To 4000 X 4000 Pixels

User Decides What a Pixel Equates To

Terrain Attributes

Mobility (Affects Agent Speed: 0-100% = Impassable to No Effect)

Concealment (Affects Agent Detection: 0-100% = No Effect to Hidden)

Up To Three Detection Bands

Protection (Affects Agent Vulnerability: 0-100% = No Effect to Invulnerable)

Terrain Feature Height (Enables Looking Down From Above)

Additional, User-Defined Polygons Can Be Overlaid

Feature Floor & Ceiling (Enables Clouds)

Terrain Can Change Agent’s Attributes (v2.0)

Abstract Terrain


Triggers

Events That Cause an Agent To Change Behavior:

Loss of Leader (No Leader Within d)

Red, Green, or Blue Becoming Greater or Less Than v

Attributes Becoming Greater or Less Than v

Resource X, Resource Y, or Resource Z Becoming Greater or Less Than v%

Being Shot At

Detecting an Enemy

Detecting a Friend

Arriving at a Waypoint or Objective

Friendly Casualties (Fewer Than x% Known Remaining)

Fuel Becoming Greater or Less Than v%

Time Step (Absolute and Relative)

Trigger Action Reads a New, Complete, Explicitly Named Behavior Template (Including Setting New Triggers)

Delay Before Action (Future Capability)

Simulates Communication/Spin-up Time

Triggers

x– Force Strength Threshold, d – Distance Threshold,

v –Value


Activities when a trigger fires

Triggers Are User-Prioritized

If More Than One Fires, Only One Is Acted Upon

Each Trigger Is Associated with a Named Behavior

Example: Loss of Leader = ‘Surrender’

When Fired, a Trigger Reads in New Agent Behavior

Movement Desires & Terrain Preferences

Changes to Other Attributes (Speed, Altitude, Vulnerability, Leadership, etc.)

New Waypoints

New Current Color or Change Color by Actions

New Current Attribute or Change Attribute by Actions

New Weapon, Sensor, Comm Possessions

New Engagement Desires (Hold-Fire, Weapon Selection Logic, Target Persistence)

New Fuel, Resource X, Resource Y, or Resource Z, Settings

New Behavior Change Triggers (and Priorities)

No Limit To Number of New Behavior Changes (One Per Time Step)

Records Time Step, Agent Number and Class

Activities When A Trigger Fires


Example behavior tree

Example Behavior Tree

Attack

If Lose Leader

If Take Casualties

Initial: Patrol

If See Enemy

If Lose Leader

If Take Casualties

If Influenced

Surrender

Withdraw

If Lose Leader

Take Cover

If Lose Leader

If Take Casualties


Backup

BACKUP


Pythagoras

Pythagoras

USAF

February 2008

Edmund J. Bitinas

Donna Middleton

John McGillvray

Pythagoras:

Helping the World Look at Problems from the Right Angle


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