Military Operations Planning in SIPE-2
As part of the ARPA/Rome Laboratory
Planning Initiative (ARPI), SIPE-2 was applied to joint military
operations planning. It successfully generated employment plans and
expanded deployment plans for getting the relevant combat forces,
supporting forces, and their equipment and supplies to their
destinations in time for the successful completion of their mission.
Input to the system includes threat assessments, terrain analysis,
apportioned forces, transport capabilities, planning goals, key
assumptions, and operational constraints. Most of the above came from
miltary databases; around 100 plan operators in SIPE-2's input
language were developed to describe military operations.
This application of SIPE-2 was done as part of
SOCAP -- System for Operations Crisis Action Planning,
and was the core of the second Integrated Feasibility Demonstration of the ARPI.
SOCAP includes an application of SIPE-2 to military operations
planning together with a user interface tailored to this domain using
a situation-map display system. The following inputs are fed
into the Socap database from available military databases:
Other inputs come from the mission commander or his/her joint staff:
- Threat assessment: list of enemy threats, locations, and dates.
- Terrain analysis: information on terrain features affecting mobility and observability.
- Apportioned forces: list of combat forces available for planning purposes.
- Transport capabilities: list of available assets.
Most of the above information is inherently dynamic, and is best
represented in SIPE-2 as first-order predicates. There are over 2100
predicate statements in the Socap knowledge base. However, a great
deal of the available data are static (i.e., they do not change over
time as actions are executed), and for efficiency reasons are
represented in the sort hierarchy and not as predicate statements.
- Planning goals: list of goals that match mission statement.
- Key assumptions: e.g., rules of engagement, non-intervention of third party forces.
- Operational constraints: e.g., overflight privileges, troop limits in country.
A large set of SIPE-2 operators describes describe military operations that can
achieve specific employment or deployment goals.
There are operators at every level of abstraction. SIPE-2's
levels of abstraction naturally
map onto the following levels of the operations planning process:
- Level 1: Select mission type.
- Level 2: Identify threats and their locations.
- Level 3: Select employment operations, major forces, and deployment destinations.
- Level 4: Add deployment actions.
SOCAP successfully demonstrated that AI planning techniques can be
used for the generation of large-scale military operations plans. It
provides the first steps toward an operational prototype. SIPE-2
supported efficient plan generation for the scenario used. The plans
generated have approximately 200 executable actions in the final plan.
(The entire plan has many times this number of nodes, since some nodes
are conditions which help record the rationale behind the plan and
information for repairing it.) The sort hierarchy has around 250
classes and 500 objects with 15-20 properties per object. The initial
world contains approximately 2100 predicate instances. These plans
are generated in 1 to 2 minutes on a Sun Sparcstation 20. For a
large-scale military operation, such as in the Gulf crisis, the size
of both the plans and knowledge bases would probably be at least an
order of magnitude larger.
The effectiveness and efficiency stemmed from SIPE-2's hierarchical
planning process (which naturally fit the hierarchical structure of
the domain), extensive use of its sort hierarchy for encoding military
databases, the ability to place constraints on planning variables, its
powerful graphical interface for viewing plans and data, and the
interactive planning capabilities.
D. E. Wilkins and R. V. Desimone, "Applying an AI
planner to military operations planning," in Intelligent
Scheduling (M. Fox and M. Zweben, eds.), pp. 685--709, Morgan Kaufmann
Publishers Inc., San Mateo, CA, 1994.
This application was funded by the ARPA/Rome Laboratory Planning
Initiative and done by a team lead by Dr. Marie Bienkowski.
Dr. David E. Wilkins
Last modified: Mar 7 16:19:09 2007