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ATTEND

ATTEND

Analytical Tools To Evaluate Negotiation Difficulty

Alejandro Bugacov and Robert NechesUSC - Information Sciences Institute

ANTs PI Meeting

May 28, 2002

AFRL Cooperative Agreement No.: F30602-00-2-0533Award start date: 5/22/00 Award end date: 5/21/03

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ATTENDAdministrative

• ATTEND: Analytical Tools To Evaluate Negotiation Difficulty

• PM: Dr. Vijay Raghavan (IXO)• PI: Dr. Robert Neches

USC Information Sciences Instituteemail: rneches@isi.eduphone: (310) 448-8481

• AFRL Cooperative Agreement No.: F30602-00-2-0533• AO Number: K277• Award start date: 5/22/00 Award end date: 5/21/03• Agent: Dan Daskiewich, AFRL.

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ATTENDSubcontractors and Collaborators

• Subcontractors: n/a• Collaborations:

– Past: • Cornell: Modular SAT encoding that resulted in phase-transition

based prediction mechanism for the Marbles 1 problem• Altarum (Erim) : Help in experimentation to improve dynamics

robustness of the system (results covered in their presentation)• WUSL: Help in their complexity experimentation of Marbles 1

problems (results covered in their presentation) – Planned:

• Cornell: Extending the SAT encoding to the Marbles 2 problem specification

• Vanderbilt: Finer grain negotiations between SNAP and MAPLANT to improve system-of-systems performance

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ATTENDProblem Description, Objective:Technical Problems and DoD Relevance

• Technical Problem that we are trying to solve:

1. Partitioning of task space into groups that can potentially be resolved in parallel

2. Provide negotiation difficulty warnings based on problem complexity analysis

• Relevance to DOD

1. Marine Corps flight scheduling system capable to negotiate solutions for very large problems

• A system capable of generating multiple alternative detailed flight schedules for planning horizons as long as 18 months

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ATTENDProblem Description, Objective:Contribution to Goals and Current Status of the ANTs Program

• Much larger numbers of ANTs in GE/SE negotiation– Performance improvement of SNAP, the

current application of the ANT’s logistics project CAMERA

– Improve the performance of systems-of-systems negotiations between SNAP and MAPLANT

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ATTENDProblem Description, Objective: Success criteria

• Demonstrate a 10X speedup when SNAP uses ATTEND’s complexity-based tools

• Enable the solution of SNAP problems with large planning horizons in a few minutes

• Provide problem-complexity awareness to the negotiations engine to enable GE/SE trade-off

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ATTENDProject Status: Technical Approach

A. Determine size of partitions by deriving schedulabilityestimates from empirical formula derived from complexity profiles

B. Assign to each partition non-highly conflicting tasks

Steps involved in the approach: 1. Extract resource allocation problem from SNAP’s Marbles 2 API

2. Automatically encode the problem into a SAT formula

3. Analyze formulae to extract critical parameters from their phase-transition curves. Derived equation which computes schedulability estimates

4. Using schedulability estimates, perform dependency management to partition into groups of non-highly conflicting tasks

5. Feed the partitioning information to the negotiations-based solver

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ATTENDProject Status: Changes in Technical Approach since last PI Meeting

• Some changes to make the SAT encoding of the Marbles 2 problem more compact

– Extended the modular SAT encoding to a multi-hierarchical structure of activation variables in a time-discretized planning horizon

Task Activation Variables

Anchor Segment Activation Variables

Requirement-Resource Pair Activation Variables

Task-Requirement-Time Slot Resource Variables

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ATTENDProject Status: Progress Made Since Last PI Meeting

Implementation is on its way (~30% completed)

Using schedulability estimates, perform dependency management to partition negotiations

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5

3

2

1

StatusApproach Step

No started yetFeed the partitioning information to the negotiations-based solver

Depends on completion of Step 2. BUT we already have the real problems(Step 1)

Analyze formulae to extract critical parameters from their phase-transition curves. Derived equation which computes schedulabilityestimates

Implementation is at about 70% completed

Automatically encode the Marbles 2 problem into SAT

Implementation is completed

Extract resource allocation problem from CAMERA’s Marbles API. (SNAP to Marbles2 translator module)

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ATTENDProject Status: Deliverables and Publications

• Deliverables– Real SNAP problems in Marbles 2 format– Centralized (SA inspired) anytime solver

for Marbles 2 problems• Manuscripts and Publications

– The Marbles Manifesto: A Definition and Comparison of Cooperative Negotiation Schemes for Distributed Resource Allocation; Martin Frank, Alejandro Bugacov, Jinbo Chen, Gordon Dakin, Pedro Szekely and Bob Neches. Paper presented at the 2001 AAAI Fall Symposium. November 2, 2001.

– SAT Encoding of a Resource Allocation Problem with Modular Constraints. Alejandro Bugacov,Donghan Kim, Carla Gomes and Bart Selman.

– Modularity and Complexity Profiles in Overconstrained Resource Allocation Problems. AlejandroBugacov, Donghan Kim, Carla Gomes and Bart Selman.

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ATTENDProject Status:Milestones Accomplished

• Defined the scheme for partitioning of the task space

• Adopted a common API (Marbles 2) for analyzing real SNAP problems

• Implemented a translator from real SNAP specifications to Marbles 2

• Implemented a reference centralized Marbles 2 solver

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ATTENDProject Status: Characteristics of the Marbles 2 Problem Specification (I)

• Simple problem with 3 Tasks and 7 Resources• Task ! Requirements ! Time Segments

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ATTENDProject Status: Characteristics of the Marbles 2 Problem Specification (II)

• Simple problem with 3 Tasks and 7 Resources

Resource Availability Intervals

Valid Beginning of the Anchor Segment

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ATTENDProject Status: Solving Marbles 2 Problems, Early Results

• Comparison of results obtained with centralized and distributed any-timesolvers (distributed results by Min Cai and Martin Frank)

• Distributed Reference Solver has low value solutions but good solution time• We are currently studying the impact of message loss

distributed

centralized (SA)

Problems size: 70 tasks and 70 Resources

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ATTENDProject Plans: The Next Six Months & Performance Goals

• Given a 3 months long SNAP problem

• Partition it in 10 (or more) groups of tasks

• Show a 10X speedup when resolved in parallel by a Marbles 2 solver

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ATTENDProject Schedule and Milestones

• Partitioning •M1: Automatically encode the problem into a SAT formula. (Extension of

SAT encoding to Marbles2)•M2: Analyze formulae to extract critical parameters from their phase-

transition curves. Derived schedulability estimates equation•M3: Using schedulability estimates, perform dependency management to

partition negotiations• Execution

•M4: Feed the partitioning information to the negotiations-based solver• Merging

•M5: Generate a full schedule merging individual solutions

JUN JUL AUG SEP OCT NOV DEC

M1 M2

M3

M4 M5

2003

Systems-of-Systems Negotiations

MaintMaint..Negotiation:Negotiation:

MMA A intenanceintenancePPLLAANN ningningTT oolsools

Ops Negotiation:Ops Negotiation:

S S cheduleschedulesNN egotiatedegotiated

bybyA NTA NT--basedbasedP lannersP lanners

COMMANDS:Coordinated Operations and Maintenance Management

Assisted by Negotiated Decision SupportMaintenanceOperations

Initial ScheduleInitial Schedulebased upon assumed aircraft availabilitybased upon assumed aircraft availability

Initial Initial MaintMaint. Plan. Plan(A/C status, projected consequences)(A/C status, projected consequences)

MaintenanceConsequence

Projection

Operations PlansOperations Plans

OperationalConsequence

Projection

Maintenance ProposalsMaintenance Proposals

OperationsAdvisor

OperationsOperationsAdvisorAdvisor

MaintenanceAdvisor

MaintenanceMaintenanceAdvisorAdvisorCoordinatedCoordinated

SchedulesSchedules

GuidanceGuidance

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ATTENDProblem Description, Objective:System-of-Systems Negotiations

• Goals:– Moving from (current) asynchronous communication to a richer form of

interleaved negotiations based on common (“Commander's Guidance”) constraints

• Complexity and Dynamics Issues– Impact of finer grain interleaved negotiations– Can we do better than asynchronous communications using C&D tools?– Impact of SNAP communicating ranges of A/C availability – Shared ontology issue in communicating constraints across systems

• Identified Approaches– Transform the system-of-systems negotiations into multiple Marbles 1

problems – MAPLANT uses Oz which can be explored as a mechanism for doing the

finely interleaved communication and negotiation

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ATTENDTechnology Transition/Transfer

• All DARPA contractors in the ANTs community are considered to be potential users of our analytical package

• While we hope to demonstrate a methodology useful for multiple negotiation systems, we will test it inCAMERA’s flight mission scheduling system (SNAP)

• ATTEND’s technology will mainly transfer as a component of SNAP

• SNAP transfers via CACE ACTD, which has been deployed to the Marines. CACE transfers to CARTE (a Future Naval Capability program), which in turn has commitments for technology adoption by USMC HQ and the Joint Strike Fighter program

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ATTENDEND

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ATTEND: Analytical Tools To Evaluate Negotiation Difficulty

New ideas:

Impact:

• Adaptive control of negotiation systems enabled by analytical methods to estimate negotiation difficulty

• Management of resource contention facilitated by SAT encoding of complex allocation problems

• Complexity reduction via phase-transition aware partitioning of task space

• Control over real-time performance/quality tradeoffs via task space partitioning

• 10x speed up of negotiations in scheduling system used by the Marines

• Gold standards to evaluate, fine-tune negotiation algorithms

• Effective control of negotiation processes via timely prediction of computation needed for “good enough” solutions

–Effective negotiation difficulty prediction algorithms developed for negotiations in USMC flight scheduling system

–Scalability demonstrated in simulation

–Demonstration of negotiation control techniques interfaced to running USC ISI CAMERA negotiation system

–Evaluation and demonstration of 10x speed-ups in flight scheduling for USMC via ATTEND adaptive control techniques

FY 0

0/01

FY 0

2FY

03

Schedule

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ATTENDProblem Description, Objective (): System-of-Systems Negotiations

• Current state of SNAP and MAPLANT integration

–Both applications communicate asynchronously through a file system or web browser–SNAP writes Flight Schedules and MAPLANT writes Aircraft Availability information–Flight Schedules are used by MAPLANT to generate Aircraft Availability–Aircraft Availability is used by SNAP to create Flight Schedules

–Main Issue: Chicken-and-Egg problem–SNAP (MAPLANT) polls for the Aircraft Availability (Flight Schedule) file and asks the user if he

wants to create a new Flight Schedule (Aircraft Availability)

File System

Flight Schedule w/ time stamp

Aircraft Availability w/ time stamp

SNAPSNAP

Poll

MAPLANTMAPLANT

Poll

File System

Flight Schedule w/ time stamp

Aircraft Availability w/ time stamp

SNAPSNAP

Poll

SNAPSNAP

Poll

MAPLANTMAPLANT

Poll

MAPLANTMAPLANT

Poll

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ATTENDProject Status: Changes in Technical Approach since last PI Meeting

• Some changes in the SAT encoding of the Marbles 2 problem

– Extended the modular SAT encoding to a multi-hierarchical structure of activation variables in a time-discretized planning horizon

Task Activation Variables

Anchor Segment Activation Variables

Requirement-Resource Pair Activation Variables

Task-Requirement-Time Slot Resource Variables

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ATTENDProject Status: SAT Encoding of the Marbles 2 Problem, Details

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valid beginning of anchor segment

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ATTENDProject Status: SAT Encoding of the Marbles 2 Problem, Details