Multilevel Coordinator Agent
of Michigan CoABS Project
The Multilevel Coordinator Agent (MCA) has been developed
to facilitate the coordination of plans that are formulated by separate
agents. It provides the following
take as input a hierarchical plan library (plans that accomplish goals, where a
plan can include subgoals - the library implicitly represents and and/or graph
of elaborations from high-level goals into primitive actions
(or locally stores, depending on parameter settings) summary information
associated with non-primitive plans
a temporal constraint network representing allowable timings of potentially
conflicting primitive actions
possible interferences between high-level plans selected by asynchronously
executing agents, AND
resolutions to interferences (synchronizations or or-branch-eliminations)
identifies branches of plans involved in interferences, accepts elaborations
(selected by agents) of these portions of the plans, and performs interference
detection/resolution at deeper levels.
resolutions to potential interferences agreed upon by participating agents
and resolves potential interferences that were not predicted prior to execution
(that is, as agents exercise "or" branches of the hierarchy in
response to emerging, dynamic situations)
A powerpoint briefing on the
MCA can be found here. The
readme file associated with the software is here.
The MCA is being used as part
of the Coalition TIE. A description of
In a Coalition exercise, objectives and responsibilities
will be distributed among numerous functional teams, such as warfighting,
logistics, media relations, etc., with their own human and computational
agents. Occasionally, operational choices made by one team have unintended
consequences on what other teams should or can do (e.g., conflict over
transportation resources, friendly fire).
The non-warfighting (“blunt-end”) functional teams can
work with the warfighting component
through agents that employ access to operation plans so that they can
deconflict and advise better. Agent technologies thus can open the door to an
improved concept of operation by allowing the military to work in a more
dispersed way (so-called collaborative virtual working).
Michigan’s technical contributions to the Coalition TIE
Use of hierarchical plan
representations to facilitate flexible and efficient communication and
computation for plan conflict detection.
Search algorithms for identifying
candidate deconfliction strategies.
Integration with operator interfaces
(such as AIAI’s Process Panel) to alert humans of potential conflicts,
recommend conflict resolutions, and enact chosen resolutions.
Run-time monitoring and enforcement of
In the CoAX demonstrations, Michigan will provide one or
more instances of a Multilevel Coordination Agent (MCA) that implements plan
conflict detection, resolution, monitoring, and enforcement capabilities.
Triggers from events.
The MCA will receive from different functional teams
requests to analyse plans and report back (or to a higher authority) potential
conflicts and resolutions.
The MCA will receive runtime plan updates and
monitor/enforce coordination decisions that adhere to resolutions previously
Resulting Agent Tasks.
The MCA will perform deconfliction analyses and report on
potential conflicts and candidate conflict resolutions.
The MCA, can block pursuit of some teams’ plans until
sufficient coordination conditions are achieved, allowing runtime coordination
(1) Much of
the logistics area involves coordinating with civilian suppliers/transport,
which could raise extra complications (e.g., security) in Domain Management
Issues still to be Addressed.
The CoAX scenario has so far emphasized battle planning by
a single authority, and needs to be extended to include a broader array of
functional teams acting semi-independently
Knowledge engineering must be done to capture a
sufficiently rich set of plans for each of the functional teams to enable
interesting interactions to arise.
The MCA will be a component on the Grid before the 6-month
In the 9-month demonstration, the MCA will be illustrated
using simplified plan sets in a fairly contrived (researcher-developed)
scenario to highlight some of its basic technological capabilities and motivate
its role in the CoAX TIE.
In the 18-month demonstration, the MCA will interact with
AIAI’s Process Panel within an integrated demonstration.
Beyond 18 months, the MCA will be extended to include:
Coordination over plan synergies (not
just conflicts) for non-episodic cases
Guidelines for the construction of
effective hierarchical plan representations
Quantitative estimates of quality for
alternative coordination candidates
The ability to cache prior
coordination strategies for future team use
Information to be Exchanged.
MCA will communicate about plans and constraints on their
Initially, there will be a strong link to AIAI’s Process
Panel, as the interface between the automation for coordination provided by the
MCA and the human user.
To the extent that the plan spaces to be coordinated are
generated on the fly (such as by Master Battle Planner, CAMPS), those plans
need to be translated into f form in which MCA can reason about them.
There are possibilities to tie MCA activities more closely
with aspects of other CoAX TIE activities.
For example, the protocol through which plans are exchanged and
coordinated could be made more robust with MIT’s exception handling
technologies, and the negotiation leading to the selection of one of the
candidate conflict resolution strategies discovered by MCA could involve
The MCA will invest appropriate effort to impose just
enough constraints on activity timings and choices to ensure successful and
The MCA uses hierarchical plan representations to search
abstract plan spaces more efficiently in a top-down manner, allowing agents to
communicate less about each other, model less about each other, and leave
themselves more room for improvisation.
The MCA reasons at abstract levels using “summary
information” about what might or must hold over alternative plan refinements,
and interleaves planning with execution by performing dynamic analyses of
temporal constraint networks.