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All rights reserved, California Institute of Technology © 2002
Argumentation for Coordinating Shared Activities
(a talk on distributed planning) Brad Clement, Tony Barrett, Steve Schaffer
Artificial Intelligence Group
Jet Propulsion Laboratory
California Institute of Technology{bclement,barrett,srschaff}@aig.jpl.nasa.gov
http://www-aig.jpl.nasa.gov/
All rights reserved, California Institute of Technology © 2002
MotivationOver 40 multi-spacecraft missions proposed!
– Autonomous single spacecraft missions have not yet reached maturity.
– How can we cost-effectively manage multiple spacecraft?
Earth Observing System Sun-Earth Connections
Origins Program
Structure & Evolution of the Universe
Mars Network
NMP
NMP
All rights reserved, California Institute of Technology © 2002
Motivation
• Considerable ground operations effort and cost involved in coordinating mission plans for interacting missions.
• Human collaboration can be error-prone and slow to react.
• Automating this coordination reduces operations costs and increases science return.
• On board a team of spacecraft, it can be too expensive to centralize information and processing
All rights reserved, California Institute of Technology © 2002
Why Decentralized Planning?
• Why plan?– near-term actions can effect subsequent ones in
achieving longer-term goals
• Why decentralize?– competing objectives (self-interest)– control is already distributed– communication constraints/costs (b/w, delay, privacy)– computation constraints (parallel processing)– robustness to failure?
All rights reserved, California Institute of Technology © 2002
Prior Work
• Treats decentralized planning as an offline, collaborative problem– planners collaborate on resolving state conflicts,
ignore communication costs
• Space missions present real-time problems with self-interested agents– scientists compete for instrument/spacecraft use– missions compete for bandwidth to Earth– remote explorers may need to respond to
dynamics autonomously
All rights reserved, California Institute of Technology © 2002
Problems
• How should planning agents communicate with each other?– shared activities
• How can they coordinate joint actions during execution?– continual coordination algorithm– consensus window
• How can coordination algorithms be developed efficiently?– protocol classes that manipulate shared activities
All rights reserved, California Institute of Technology © 2002
Shared Activity Coordination (SHAC)
– continual coordination algorithm– language for coordinating planning agents– framework for defining and implementing automated
interactions between planning agents (a.k.a. coordination protocols/algorithms)
– software• planner-independent interface• protocol class hierarchy• testbed for evaluating protocols
All rights reserved, California Institute of Technology © 2002
ExecutiveExecutive
Planner
ExecutiveExecutive
Planner
ExecutiveExecutive
Planner
Shared Activity Coordination
Shared activities implement team plans, joint actions, and shared states/resources
All rights reserved, California Institute of Technology © 2002
SHAC Applications• Simulated Mars network
– Detailed s/c model– Coordination in real time– Restricted communication (orbital
constraints)– Focus on communication quality of
service
• MISUS – mechanism for delegating goals for a team of rovers
• Techsat-21– Coordinating ground planning– Abandoned when mission de-
scoped
• Deep Space Network resource allocation (future)
MGS MEX Odyssey
MER A MER B
Mission Planning
Simulation Env
Commanding SOH displayTelemetry
ASPEN
SCL
Fight Dynamics
Payload Ops W/S
Cmd Verification Engineering Models
PPC ClusterCmd Verification
TT&C W/S TT&C W/S
Data Center
Pass PlaybackSOH displayTrendingAnom Res
SCLMatlab
All rights reserved, California Institute of Technology © 2002
Shared Activity Model
• parameters (string, integer, etc.)– constraints (e.g. agent4 allows start_time [0,20], [40,50])
• decompositions (shared subplans)
• permissions - to modify parameters, move, add, delete, choose decomposition, constrain
• roles - maps each agent to a local activity
• protocols - defined for each role– change constraints– change permissions– change roles
• includes adding/removing agents assigned to activity
All rights reserved, California Institute of Technology © 2002
Argumentation
• Proposals and counterproposals with justifications
• In distributed constraint satisfaction– Proposals are variable changes– Justifications are no-goods
• For distributed planning– Proposals are shared activity changes– Justifications are constraints
All rights reserved, California Institute of Technology © 2002
SHAC AlgorithmGiven: a plan with multiple activities, including a set of
shared_activities, and a projection of plan into the future.1. Revise projection using the currently perceived state and any
newly added goal activities.2. Alter plan and projection while honoring constraints and
permissions of shared_activities.3. Release relevant near-term activities of plan to the real-time
execution system.4. For each shared activity in shared_activities
– apply each associated protocol to modify the activity5. Communicate changes in shared_activities.6. Update shared_activities based on received communications.7. Go to 1.
All rights reserved, California Institute of Technology © 2002
Protocol CapabilitiesDefining/extending protocol classes1. modify permissions2. modify local parameter constraints3. add/delete sharing agents4. change roles of sharing agents
Default protocol class• joint intention• mutual belief• resource sharing• active/passive roles• master/slave roles
All rights reserved, California Institute of Technology © 2002
Control Protocols for a Shared Activity
• Chaos– A free-for-all among planners
• Master/Slave– The master has permissions, slaves don’t
• Round Robin– Master role passes round-robin among planners
• Asynchronous Weak Commitment (AWC)– Neediest planner becomes master
• Variations– how many planners share activity
– use of constraints
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Experiments – Abstract Problem
• joint measurements
• capability matching
• 3-9 spacecraft
• 1-7 capabilities
• 1-9 joint goals each requiring 1-4 of each capability
All rights reserved, California Institute of Technology © 2002
Chaos - invalid solutions
M/S - not complete
Experimental Results(Progress over cpu time)
num
ber
of p
robl
ems
max cpu time (seconds)
AWC
RR
ChaosM/S
All rights reserved, California Institute of Technology © 2002
num
ber
of p
robl
ems
max actual time (seconds)
AWC
RR
ChaosM/S
Experimental Results(Progress over clock time)
M/S – solves quickly or not at all
All rights reserved, California Institute of Technology © 2002
num
ber
of p
robl
ems
max number of messages
AWC
RR
ChaosM/S
Experimental Results(Number of messages sent for problems solved)
M/S – order(s) of magnitude fewer messages sent
RR – performance flip flops with rest
All rights reserved, California Institute of Technology © 2002
num
ber
of p
robl
ems
max data sent (bytes)
AWC
RR
Chaos
M/S
Experimental Results(Data sent for problems solved)
M/S – order(s) of magnitude less data sent
Performance flip flops for rest
All rights reserved, California Institute of Technology © 2002
Experimental Results - Sharing(Progress over cpu time)
num
ber
of p
robl
ems
max cpu time (seconds)
AWCRR
Chaos-BM/S-B
AWC-B
M/SChaosRR-B
Share with all (broadcast) or share only with assigned s/c
RR – performance best without and worst with broadcast
Chaos – much better with broadcast
All rights reserved, California Institute of Technology © 2002
Experimental Results - Sharing(Progress over clock time)
num
ber
of p
robl
ems
max actual time (seconds)
AWC
RR
Chaos-B
M/S-B
AWC-B
M/S
Chaos
RR-B
Share with all (broadcast) or share only with assigned s/c
RR – performance best without and worst with broadcast
Chaos – much better with broadcast
All rights reserved, California Institute of Technology © 2002
Experimental Results - Sharing(Number of messages sent for problems solved)
num
ber
of p
robl
ems
max number of messages
AWC
RR
Chaos-B
M/S-B
AWC-B
M/SChaos
RR-B
Share with all (broadcast) or share only with assigned s/c
Many order of magnitude separations
In general, protocols that solve more problems send more messages
RR – performs much better on “hardest” 500 problems
All rights reserved, California Institute of Technology © 2002
Experimental Results - Sharing(Data sent for problems solved)
num
ber
of p
robl
ems
max data sent (bytes)
AWC
RR
Chaos-B
M/S-B
AWC-B
M/SChaos
RR-B
Share with all (broadcast) or share only with assigned s/c
Many order of magnitude separations
In general, protocols that solve more problems send more data
RR – performs much better on “hardest” 500 problems
All rights reserved, California Institute of Technology © 2002
Summary• SHAC
– communication language for distributed planning
– general algorithm for continual coordination
– framework for developing coordination protocols
– software with planner independent interface
• Characteristics and performance of argumentation-based protocols– Round-robin (with limited sharing) performed fastest with
somewhat heavy communication costs
– AWC is all around best with high communication costs
– M/S has least communication costs but only works for restricted domains
All rights reserved, California Institute of Technology © 2002
Future Directions
• evaluate other simple protocols in other domains
• different constraint representations• abstraction techniques for limiting
communication and preserving flexibility• use group communication techniques to give
consistency guarantees to protocols like chaos
• find a customer