all rights reserved, california institute of technology © 2002 argumentation for coordinating...

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/


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


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


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?


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


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


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


ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

Shared Activity Coordination

Shared activities implement team plans, joint actions, and shared states/resources


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

http://nssdc.gsfc.nasa.gov/thumbnail/spacecraft/mars_express.gif


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


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


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.


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


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


Experiments – Abstract Problem

• joint measurements

• capability matching

• 3-9 spacecraft

• 1-7 capabilities

• 1-9 joint goals each requiring 1-4 of each capability


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


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


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


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


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


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


RR – performance best without and worst with broadcast

Chaos – much better with broadcast


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


Many order of magnitude separations

In general, protocols that solve more problems send more messages

RR – performs much better on “hardest” 500 problems


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


Many order of magnitude separations

In general, protocols that solve more problems send more data

RR – performs much better on “hardest” 500 problems


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


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

all rights reserved, california institute of technology © 2002 argumentation for coordinating...

Documents

processingall rights

shared activitiesall

multispacecraft missions

coordination algorithms

shared activitieshow

team of spacecraft

multiple spacecraft

plannersmany plans