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Who Are We

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

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  1. Negotiation Technology In Real Use Who Are We

  2. Users:MARINE AIR GROUP 13

  3. Using When Who What Where Hard to Write Evaluation Function to Characterize Good Schedules Our Problem: Help Them Write the Schedules

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. Marbles FAMILY of negotiation: • Tasks bid for resources using marbles • Tasks can move marbles among resources • Tasks withdraw enabling others to win Individual marbles algorithms differ on strategies for bidding, moving marbles and withdrawing Approach: Marbles

  12. 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

  13. 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

  14. What Does It Take To Transition Your Technology Hotel clerks are your friends

  15. Upcoming Technology Pull in CARTE(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

  16. Objective: 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.

  17. SNAP Generates N Missions for the fly day 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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