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Negotiation Technology In Real Use. Who Are We. Users: MARINE AIR GROUP 13. Using. When. Who. What. Where. Hard to Write Evaluation Function to Characterize Good Schedules. Our Problem: Help Them Write the Schedules. Yearly Training Plan TEEP Flight Hour Program.

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Who are we

Negotiation Technology In Real Use

Who Are We


Users marine air group 13
Users:MARINE AIR GROUP 13


Using

When

Who

What

Where

Hard to Write Evaluation Function to Characterize Good Schedules

Our Problem: Help Them Write the Schedules


Status of snap schedules negotiated by agent based planners

Yearly Training Plan

TEEP

Flight Hour Program

Monthly Training Plan

Weekly Training Plan

Daily Flight Schedule

Active Flight Schedule

Status of SNAP: Schedules Negotiated by Agent-Based Planners

Builds and repairs fully-detailed flight schedules for any planning horizon, without losing sight of command objectives, providing new opportunities to explore and manage alternative futures, in 1/10th-1/100th of current time

  • Prioritized Guidance

  • Squadron focus o Pilot focus o Sortie cycle

  • Pilot builds o Pilot specific training code o Fly day

  • Pilot snivels o Ranges o No. aircraft of each type

SNAP Agents: Trade-off Exploration, Win-Win Scheduling Solutions

  • Flow manager

  • Pilots

  • Aircraft

  • Missions

  • Ranges

  • PMCF

  • Simulators

  • Sim. Monitors

  • ODO

  • Ordnance

  • Academics

Knows the situation

Lets users adjust priorities

Accepts guidance at any level of specificity

Inputs

Outputs

Obeys the law

  • Constraints

  • Training code pre-requisitesfrom T&R Manual

  • Fly day

  • Day & night missions

  • Crew day rules

  • Turn-around & briefing time

  • Instructor requirements

  • Range capabilities

    • Availability & suitability

    • Merging and splitting

  • Range board

  • Pilot SNIVELs

  • Aircraft availability

  • Simulator schedule

ElectronicFeed toMaintenance

Compares results to guidance

Monthly Sched.

Scheduling OfficerFeedback

Weekly Sched.

Range Use

Identifies needed ranges

Daily Schedule

Pilots’ View

Produces schedules

Tracks pilots


Methodology technology and application tracks

SNAP vn

User

Evaluation

SNAP

UCAV

SNAP v2

SNAP v1

Tech

Tech v1

Tech v1

Tech v1

Evolve

Generalize

Tech

Tech

Evolve

Methodology: Technology and Application Tracks

Goals

  • Build generic resource allocation technology

  • … address requirements of the real world application

  • Build application prototype for real use


Resource allocation problems
Resource Allocation Problems

Basic resource allocation problem

NP-Hard (Wayne Zhang)

  • Desiderata:

    • Distributed: tasks & resources are distributed agents

    • Robustness: add/remove resources & tasks, dropped messages

    • Good enough, soon-enough solutions


Resource allocation problems1
Resource Allocation Problems

Basic resource allocation problem

NP-Hard (Wayne Zhang)

  • Desiderata:

    • Distributed: tasks & resources are distributed agents

    • Robustness: add/remove resources & tasks, dropped messages

    • Good enough, soon-enough solutions


Resource allocation problems2

Resources

Task [value]

A

B

C

D

E

F

G

H

I

20

30

R1

Task 1 [300]

10

25

R2

10

10

R3

0

-10

R1

Task 2 [600]

10

-10

R2

10

20

R3

10

0

R1

Task 3 [200]

0

R2

30

R3

-5

10

0

R1

Task 4 [400]

0

0

R2

10

-30

0

R3

Resource Allocation Problems

Basic resource allocation problem

+ bonus for resource usage

NP-Hard (Wayne Zhang)

  • Desiderata:

    • Distributed: tasks & resources are distributed agents

    • Robustness: add/remove resources & tasks, dropped messages

    • Good enough, soon-enough solutions


Resource allocation problems3

Resources

Task [value]

A

B

C

D

E

F

G

H

I

20

30

R1

Task 1 [ ]

10

25

R2

10

10

R3

0

-10

R1

Task 2 [ ]

10

R2

10

20

R3

10

0

R1

Task 3 [ ]

0

R2

30

R3

-5

10

0

R1

Task 4 [ ]

0

0

R2

10

0

R3

Resource Allocation Problems

Basic resource allocation problem

+ bonus for resource usage

+ time: resources and tasks available only at certain times

NP-Hard (Wayne Zhang)

  • Desiderata:

    • Distributed: tasks & resources are distributed agents

    • Robustness: add/remove resources & tasks, dropped messages

    • Good enough, soon-enough solutions


Resource allocation problems4

Resources

Task [value]

A

B

C

D

E

F

G

H

I

20

30

R1

Task 1 [ ]

10

25

R2

10

10

R3

0

-10

R1

Task 2 [ ]

10

R2

10

20

R3

10

0

R1

Task 3 [ ]

0

R2

30

R3

-5

10

0

R1

Task 4 [ ]

0

0

R2

10

0

R3

Resource Allocation Problems

Basic resource allocation problem

+ bonus for resource usage

+ time: resources and tasks available only at certain times

+ dependencies:

- task pre-requisites

- resource bundles

NP-Hard (Wayne Zhang)

  • Desiderata:

    • Distributed: tasks & resources are distributed agents

    • Robustness: add/remove resources & tasks, dropped messages

    • Good enough, soon-enough solutions


Approach marbles

Individual marbles algorithms differ on strategies for bidding, moving marbles and withdrawing

Approach: Marbles


Preliminary ra marbles quality evaluation randomly generated problems

Tolerance to Message Loss

QUALITY (more better)

Same Quality

Problem Number

COMPUTATION TIME (less better)

Scalable to ~2000 Tasks

10X faster

Problem Number

2048

Preliminary RA-Marbles Quality Evaluation(Randomly Generated Problems)

  • Well-known

  • Centralized Schemes

  • Simulated Annealing

  • SAT Encoding

MarblesDistributed Schemes


Ants technology transition chronology usc isi camera project vanderbilt isis maplant project

  • Oct 2001: Demo to Deputy Under Secretary of Defense for Advanced Systems and Concepts, and the Assistant Deputy Under Secretary for Advanced Systems and Concepts in Logistics

  • Nov 2001: First “system of system” negotiations

  • Nov 2001: Demo for NAVAIR results in Navyinitiating designation as a “preferredapplication” for NALCOMIS (key Navy-USMC standard information system)

Impact on daily operations

Demonstrations

ANTS Technology Transition Chronology(USC ISI CAMERA Project, Vanderbilt ISIS MAPLANT Project)

  • October 2003: Follow-on to start on extension to all Navy and USMC tactical aircraft (Expected funding, $7.5 M over 3 yrs under ONR Future Naval Capabilities Knowledge Superiority Assurance Program)

  • June 2002: Users deploy to Japan and the Pacific Region

  • May 2002: Scheduled initial fielding

  • Jan - Apr 2002: USMC Deputy Commandant for Aviation arranges briefings/demos for all Generals in USMC Aviation

  • Dec 2001: DARPA Director reports work to Under Secretary of Defense for Acquisition, Technology and Logistics

  • July 2001: Operational users lobby for full use -- “We want this for daily use throughout the entire Air Group.” ONR funds fielding to Marine Air Group 13

  • February 2000: first demonstration to users (VMA 513 selected)

  • June 1999: contract initiated for DARPA research demonstration; plan is demos with input from a single USMC Harrier aircraft squadron


What does it take to transition your technology
What Does It Take To Transition Your Technology Advanced

Hotel clerks are your friends


Upcoming technology pull in carte onr future naval capabilities
Upcoming Technology Pull in CARTE Advanced (ONR Future Naval Capabilities)

  • FY04-FY05Scale & control:

    • Bigger probs.

    • Much longer planning horizons

    • Control of higher level architecture with support for parallel exploration

  • FY03-FY04System of System Interactions:

    • N-way

    • Shared Resources

Today: Coordinated Ops/Maint. pairs


Objective distributed adaptive real time weapon target pairing for ucav swarms
Objective: Advanced Distributed, Adaptive & Real-TimeWeapon-Target Pairing for UCAV Swarms

Enable UCAVs to autonomously and effectively adapt weapon-target pairings in the face of

targetgone

UCAVlost

intermittent link

newtargetdetected

laser designatornon-operational

link out

Communication Disruptions

Degraded Capabilities,

Changing Situations,

by developing distributed algorithms that are

poor connectivity...let’s go with a lesscommunication-intensivesynchronization protocol...

abandoning target 4 toattack higher-valuedtarget 5 with UCAV D...

not enough time...not initiating optimizationof munitions for target 1...

adaptive,

real-time,

and robust

with quantifiably measurable effectiveness.


Attend complexity results applied to camera example from snap

SNAP Generates N Missions for the fly day Advanced

ATTEND Partitioning to balance load at time-slots

N4

Marble-based SNAP

Negotiations

Marble-based SNAP

Negotiations

N2

N3

N1

SNAP Generates N Missions for the fly day

N

ATTEND Complexity Results Applied to CAMERA:Example from SNAP


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