continual coordination of shared activities brad clement & tony barrett artificial intelligence...

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Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of Technology {bclement,barrett}@aig.jpl.nasa.gov http://www-aig.jpl.nasa.gov/

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Page 1: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Continual Coordination of Shared Activities

Brad Clement & Tony Barrett

Artificial Intelligence Group

Jet Propulsion Laboratory

California Institute of Technology{bclement,barrett}@aig.jpl.nasa.gov

http://www-aig.jpl.nasa.gov/

Page 2: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Overview

• Why decentralized planning?• Shared Activity Coordination (ShAC)

– multiagent modeling– framework for developing roles and

protocols– testbed for evaluating protocols– continual coordination algorithm

• Mars 2003 coordination scenario

Page 3: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Why Decentralized Planning?

• Why plan?– near-term actions can effect subsequent ones in

achieving longer-term goals

• Why decentralize?– competing objectives (self-interest)– physically distributed points of control– limited shared resources– communication constraints/costs– computation constraints (parallel processing)– greater capabilities/efficiency of distributed assets

Page 4: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Motivation for NASAOver 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

Page 5: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

• Optimize a function of variable assignments with both local and non-local constraints.

Distributed Constrained Optimization

ControlControl

ExecutiveExecutive

PlannerPlanner

AnalystAnalyst

Page 6: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

ExecutiveExecutive

Planner

Shared Activity Coordination

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

Page 7: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Shared Activity Model

• identifier• 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 roles

• changes sharing agents and protocol assignments

– handle changes received from other agents

Page 8: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

ShaC AlgorithmGiven: a plan with multiple activities, including a set of

shared_activities with constraints, 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.3. Release relevant near-term activities of plan to the real-time execution

system.4. For each shared activity in shared_activities

– if outside consensus window,• apply each associated protocol to modify the activity

– else• apply simple consensus_protocol

5. Communicate changes in shared_activities and constraints.6. Update shared_activities and constraints based on received

communications.7. Go to 1.

Page 9: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Default Protocol Capabilities

• joint intention

• mutual belief

• resource sharing

• active/passive roles

• master/slave roles

Page 10: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Extending Protocol Classes

1. modify permissions

2. modify local parameter constraints

3. add/delete sharing agents

4. change roles of sharing agents

Page 11: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Example Protocol Classes1. modify permissions2. modify local parameter constraints3. add/delete sharing agents4. change roles of sharing agents

• Argumentation (1,2)• Delegation (3)• Asynchronous weak commitment (1,2)• Constraint-based conflict resolution (2,4)• Round robin (1)• Centralized conflict delegator (extends

delegation)

Page 12: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Asynchronous Weak CommitmentModify permissions:

– if have highest priority• remove self’s modification permissions (add, move,

delete)– else

• give self modification permissionsModify parameter constraints:

– if cannot resolve local conflicts and conflicts with constraints of higher ranking agents

• set own rank to highest rank plus one• generate parameter constraints (no-good) describing

locally consistent values

Page 13: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Constraint-Based Conflict Resolution

Modify parameter constraints:– if cannot resolve conflicts involving shared activity

• update parameter constraints describing locally consistent values

Modify roles:– if reached consensus on constraints or

time_elapsed > threshold• switch to role for solution convergence

(e.g. argumentation, voting, highest rank decides)

Page 14: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Coordinating the Commanding of Mars ‘03

• Distributed Odyssey, MER A, and MER B ASPEN planners

• Schedules generated independently

• MERs share bandwidth and memory for downlinks from Odyssey

Mars 2003

Page 15: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Coordinating the Commanding of Mars ‘03

• Distributed Odyssey, MER A, and MER B ASPEN planners

• Schedules generated independently

• MERs share bandwidth and memory for downlinks from Odyssey

Mars 2003

Page 16: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Coordinating the Commanding of Mars ‘03

• Distributed Odyssey, MER A, and MER B ASPEN planners

• Schedules generated independently

• MERs share bandwidth and memory for downlinks from Odyssey

Mars 2003

Page 17: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

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.

• Applies to autonomous missions

Page 18: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario• MERs send critical data to

Earth and need uplink to direct subsequent activities

• Odyssey can often get data to/from Earth faster than MERs

• MERs’ mission planners coordinate with Odyssey’s to determine how and when data is routed

• Bandwidth and memory is shared on Odyssey, and decisions affect other local resources, such as power which can restrict other activities

Mars 2003

Page 19: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

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MER activitiesOdyssey activities

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Page 20: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

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Odyssey

MER A

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Page 21: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

Odyssey

MER A

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Page 22: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

Odyssey

MER A

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Page 23: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

Odyssey

MER A

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Page 24: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

Odyssey

MER A

comm earth

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critical pancam comm earth comm earth

comm odyssey

traversecomm earth

no pendingrequest

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Page 25: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Mars 2003 Scenario

Odyssey

MER A

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critical pancam comm earth comm earth

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no pendingrequest

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Page 26: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Summary

• Shared Activity Coordination (ShAC)– multiagent modeling– framework for developing roles and

protocols– continual coordination algorithm

• Mars 2003 coordination scenario

Page 27: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Future Directions

• Evaluate protocols in testbed• Quick group response to

anomalies and discovered opportunities

• Use summary information for abstract reasoning

• Apply to– Ground operations for

Techsat-21– Antenna array automation for

DSN– Distributed, autonomous

missions

Mars 2003

Page 28: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Related Work(partial list)

• Collaborative planning (D-SIPE)• Team activity modeling (TEAMCORE)

– TOP (Team Oriented Programming)

• Protocol Evaluation (TÆMS/GPGP)• Plan merging/coordination

– Georgeff, Ephrati & Rosenchein, Lansky– DPOCL– Summary information

• Joint Intentions– STEAM– Shared Plans

Page 29: Continual Coordination of Shared Activities Brad Clement & Tony Barrett Artificial Intelligence Group Jet Propulsion Laboratory California Institute of

Organizations and Data Flow Derived from Concepts

ESOC/LOC

MMO NAV

MMO MPSET(SEQ/ASP)

MMO MMCT LMA RTO

MSSS-MOC

MER IST/SCT/SCI

ESA Antenna

LMA SCT

MERA MERBODYBeagle MGSMEX

MMO MPSET(PLANNING)

Uplink and Downlink Data

Ground Planning and Uplink Products Essential to Relay and DTE Communications

MMO MDAPT

DSN

waggoner 6/14/01