Multilevel Mission Coordination for Coalition Operations Edmund H.
Multilevel Mission Coordination for
Edmund H. Durfee University of Michigan
Four independent helo
Analyzes the alternative plan spaces of
coalition functional teams that plan
independently and act asynchronously
Works top-down with plans chosen by teams
to predict unintended interactions.
Identifies candidate resolutions (merging
steps that duplicate effort, inserting timing or
action constraints to deconflict plans).
Notifies operator of possible plan conflicts
and synergies, and computed resolutions.
Estimates quality of alternative resolutions,
including action costs, parallelization, and
disruption to prior commitments
Given more time, isolates and resolves
interactions more precisely and efficiently.
Allows postponing coordination decisions
until runtime conditions are better known.
Packaged as a Grid-aware component that
supports coalition commanders.
Joint mission with objectives/responsibilities
distributed among multiple functional teams
comprised of human and computer agents
Operational choices by a functional team can
unintentionally (infrequently) interact with
what others can or should do
Negative interactions: conflict over coalition
assets, friendly fire
Positive interactions: piggybacking tasks
Multilevel Coordination Agent is a grid-aware
service to ensure that these interactions are
efficiently predicted and effectively resolved
Resulting joint plan balances:
Efficient (fast, parallel) execution
Flexibility for local run-time improvisation
Avoidance of unnecessary or costly actions
Realistic runtime messaging load
Minimal disruption to prior commitments
tasks to enable
merger of others
Finds serialization constraints to avoid
conflicts (e.g., collisions over helipads),
Discovers opportunities for having one plan
accomplish (part of) mission for another.
Estimates costs, overall time needs, and
expected impact on prior commitments for
informed command decisions.
Given more time, explores at more detailed
levels to find more parallelism.
More complicated kinds of plan merging to
maximize utilization of assets.
Modeling and coordinating interactions
among agent teams.
Caching and reuse of coordination decisions.
Distribution of coordinator functionality
leading to efficient parallel implementations.
CoABS CoAX 2002 Demonstration
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