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1
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
2
ATTENDAdministrative
• ATTEND: Analytical Tools To Evaluate Negotiation Difficulty
• PM: Dr. Vijay Raghavan (IXO)• PI: Dr. Robert Neches
USC Information Sciences Instituteemail: [email protected]: (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.
3
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
4
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
5
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
6
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
7
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
8
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
9
ATTENDProject Status: Progress Made Since Last PI Meeting
Implementation is on its way (~30% completed)
Using schedulability estimates, perform dependency management to partition negotiations
4
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
13
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
14
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
16
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
18
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
19
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
20
ATTENDEND
21
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
22
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
23
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
][)(11
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24
ATTENDProject Status: SAT Encoding of the Marbles 2 Problem, Details
][)(11
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valid beginning of anchor segment
25
ATTENDProject Status: SAT Encoding of the Marbles 2 Problem, Details