Download Artificial intelligence models within the JOCASTS war

ISMOR22/29
Simulation of Modern Warfare Approaches in the
JOCASTS War-Gaming System
S.G. Lucek, NSC1
Abstract
JOCASTS is a tri-service war-gaming simulation that is used to exercise
UK and overseas HQ commanders and their staffs in a realistic manner
encompassing operations up to single and multi theatre-level. These
games are designed to exercise the next generation of commanders in
decision making using the latest military thinking and doctrine, inclusive
of the philosophies of the Comprehensive Approach (CA), which
incorporates the Effects-Based Approach to Operations (EBAO), and
Networked-Enabled Capability (NEC) 2. Here we discuss how the fidelity
of the detailed resolution of the JOCASTS model is important in support
of this. Sophisticated Artificial Intelligence (AI) algorithms in JOCASTS
allow for rapid tasking of large theatre-scale orders of battle and flexible
use of the system whilst maintaining the fidelity of the resolution model.
This flexibility enables Courses of Action studies, where the decision
maker can see the range of outcomes, effects and feasibility of a variety
of plans. It is discussed how such AI algorithms can also be used to
represent the behaviour of non-military entities and so model the wider
diplomatic and economic aspects of a campaign as well as the more
traditional kinetic effects. This is important to the support of an exercise
where the students expect to utilise the full spectrum of the CA and
EBAO, as it enables a representation of the Diplomatic, Military and
Economic (DME) instruments of power within the simulation. By
modelling the human dimension of war it is possible to exercise the
commander in the full spectrum of conflict, from peace to crisis to war to
post-conflict resolution.
Introduction
JOCASTS, the Joint Operational Command and Staff Training System, is a PCbased simulation environment developed and maintained by Newman & Spurr
Consultancy Ltd (NSC), targeted at training HQ commanders and their staffs in joint,
combined or single-component operations from the formation to the theatre level.
JOCASTS provides realistic training for officers from army major equivalent to
one-star level in command decision-making, potentially within a complex multi-sided
coalition environment. It is currently in use by a number of staff colleges across the
world, the UK’s Joint Services Command and Staff College (JSCSC), Australia and
Kuwait, on a variety of exercises with student participation ranging from a
1
Newman & Spurr Consultancy Ltd, Norwich House, Knoll Road, Camberley, Surrey, GU15
3XX, UK, [email protected]
2
It is recognised that the terms and concepts in these areas of doctrine are still developing
and that the Joint Doctrine and Concepts Centre are still to publish the doctrinal definitions for
them. However in concert with our customers the development of JOCASTS is attempting to
support training with as up-to-date definitions, understanding and representations as is
possible during this evolutionary process.
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Simulation of modern warfare approaches in the JOCASTS War-Gaming System
single-service syndicate game with about 10 students through to concurrent,
tri-service, operational-level exercises with in excess of 300 students.
The exercises that JOCASTS is used to support provide a simulated environment for
future commanders to exercise decision making using the latest military thinking and
doctrine. Thus recent and current development in JOCASTS has focused on ways
simulation can be used to better support the tenets of the Effects-Based Approach to
Operations (EBAO). This document gives an overview of these developments, the
techniques currently being used in Artificial Intelligence (AI) algorithms to aid the
mission tasking process, and how simulation can be used to support, train and
exercise the evolving approaches to warfare.
The Comprehensive Approach
Approach to Operations
and
an
Effects
Based
The CA and EBAO are not new. This point is emphasised during teaching at JSCSC.
What the CA does is explicitly link military operations to political goals; The Joint
Doctrine and Concepts Centre states that ‘The Comprehensive Approach, focused
on the use of military and non-military effects and employing all Instruments of Power
(Diplomatic, Information, Military and Economic), underpins all future operations’.
EBAO is evolutionary not revolutionary, and builds on concepts such as Manoeuvre
Warfare in offering a viable alternative to a purely attritional approach.
The central premise is that it is the effect(s) visited upon the adversary (or
environment) that is critical, so all friendly forces activity should be designed to
deliver the required effect(s). The challenge for the modeling community is how to
represent a realistic link between blue force actions and the effects these create on
the adversary.
Networked-Enabled Capability
‘NEC is a vehicle to guide the coherent integration of sensor, weapon, decisionmaker and support capabilities. NEC aims to improve our operational effectiveness in
the future strategic environment by permitting the more efficient sharing and
exploitation of information within the British Armed Forces and our coalition
partners.’3
The ultimate endstate for NEC is a position of perfect knowledge gathering and
sharing, where everyone has immediate access to all information; this enables a
faster cycling through the OODA4 loop with a resultant increase in operational tempo
relative to the enemy.
JOCASTS simulation
JOCASTS is designed to support training from the higher tactical (unit and formation)
to the operational level (divisional and above in a joint/combined campaign). The
order input process uses sophisticated planning toolsets to assist in the rapid tasking
of large theatre-scale orders of battle (ORBATS). The system then conducts combat
resolution and the results are presented to the players as a visual Joint Operations
MOD Capability Manager, Networked Enabled Capability – An Introduction, Version 1.0,
April 2004.
3
4
Observe – Orientate – Decide – Act
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Picture (JOP). This presents a fused intelligence view of the updated battlespace,
supported by a series of detailed Excel-based reports.
As illustrated in the
adjacent
figure,
the
JOCASTS
resolution
model evaluates combat
at
a detailed level.
Maritime and air conflict is
resolved at the individual
platform level.
Land
conflict
is
resolved
between units, which can
represent an appropriate
level of detail for the
particular scenario being
played, but typically range
from brigade down to
company.
Development Strategy
Tasking large theatre-scale ORBATS whilst maintaining detailed resolution at a
tactical level has traditionally meant large-scale human support of the simulation,
which is both inflexible and expensive. Nevertheless, the fidelity of such a level of
resolution is important in representing modern approaches to warfare. Resolving the
tactical detail of platform types, intelligence sharing and mission timings allows a
representation of systems of systems, core features in the desire to move to NEC
enabled force. Such fidelity also allows the representation of the full range of the
kinetic effects of warfare, which are resolved both geographically and temporally.
This is vital for the representation of Manoeuvre Warfare as local and time sensitive
force ratios are modelled. By definition, increased aggregation of the fundamental
models would smear out these local and time sensitive effects into a more global
effect, which would tend to result in a more attrition-based model. As discussed
EBAO is an evolutionary approach to warfare building in areas such as Manoeuvre
Warfare, and so the development programme should maintain the support such
detailed models give for manoeuvrist principles.
Recent development strategy has therefore concentrated on the use of Artificial
Intelligence (AI) algorithms to aid the tasking process, enabling rapid and easy
tasking at a higher tactical level. The AI then translates these higher-level plans into
the detailed tactical taskings required by the JOCASTS resolution model. Thus
large, theatre-scale ORBATS can be tasked, without loss of the high fidelity of the
detailed JOCASTS resolution model. An important aspect of EBAO is how actions
control the behaviour of an opponent. It is therefore necessary that the AI algorithms
give realistic behaviour according to the evolving situation encountered, so that
students’ actions restrict or force the behaviour of opposing AI controlled units.
Increased use of AI in the detailed tactical decision process provides the model with
the capability to resolve long periods of conflict with minimal intervention.
Sophisticated scenario adjudication tools allow exercise staff to control or modify any
aspect of the situation throughout the war game. This allows the control staff to
replicate in the simulation any desired effect. The combination of the high fidelity of
the model, with rapid, computer assisted tasking, the control afforded by the
adjudication tools, and rapid assessment of results and the situation through
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Simulation of modern warfare approaches in the JOCASTS War-Gaming System
sophisticated reporting tools gives great flexibility in use. This allows the decision
makers and exercise control staff to concentrate on the effects and feasibility of a
range of Courses of Action (COA), making JOCASTS an excellent simulation
environment for the support of exercises in which the students expect to utilise the
full spectrum of the CA and EBAO.
Development is currently underway to build on the AI techniques already used to
model non-military entities in a flexible, generic framework that will allow the
representation of the behaviour of a wide range of bodies from insurgency cells
(terrorist or resistance groups, paramilitary forces or special forces) and local
populations, to national and international political, economic and diplomatic bodies.
Extending JOCASTS to more fully support a range of diplomatic and economic
aspects of a campaign will enable the students to see the effect of CA and EBAO
within a common framework rather than through exercise control staff adjudications
which may cause disjointedness to the exercise in terms of time and effect if not very
carefully controlled and reported. However any toolset development must be
transparent so as to avoid the ‘black box’ approach that can lead to a mistrust of the
generated outcomes, especially in such a complex area as warfare.
Land and maritime component artificial intelligence
The land and maritime components have a similar overall approach for computer AIassisted tasking. The user groups together units or ships, giving the group overall
objectives. The simulation then generates the detailed movement and behaviour to
meet these objectives. The following discussion concentrates on the techniques
used in the land component, with similar techniques being used for maritime.
The land component AI implementation is based on three building blocks. The first is
the definition of an optimal relative position of units in the formation, the unit
dispersal, when undertaking specific tasks. The unit dispersal together with route
finding algorithms allows the simulation to automatically route and co-ordinate unit
movement. The second building block is the decision-making rules and algorithms
that allow the simulation to automatically assess the current situation and select
appropriate actions. The last building block is the action resolution model. This
enables the simulation to automatically resolve specific actions by the task force.
In
the
land
component, the user
selects units for a
mission and specifies
a final objective. An
order of march may be
defined by selecting
from a range of predefined templates of
unit
dispositions.
These
allow
the
simulation to perform
a best fit for the units
in the formation to
those in the template
to obtain a relative
position for each unit
for the march. The
adjacent figure shows
the tasking of a
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movement order, illustrating the selection of the final objective and also the order of
march template. Routing algorithms then enable the simulation to generate best
paths for each unit, whilst co-ordinating unit position during the movement using the
order of march template. A combination of routing techniques (a geometrical method
that finds the shortest distance route, and a simulated annealing method that
optimises the route in terms of time taken) is used for speed of processing. On
reaching the destination, the units disperse into a final disposition around the target
location. Again, the user may select from a range of templates for the final dispersal,
and the simulation will obtain the final unit positions by fitting each unit in the
formation to the template, and route the units to their final position.
Behaviour of the formation en route and on reaching the destination is determined by
standard operating procedures (SOPs). The adjacent figure illustrates the interface
used to specify the
SOPs. There are a
number of situations
that the formation may
react to, including
sighting other units,
route
blocked
by
units, being attacked,
taking damage, and
activities at the final
destination.
There
are also a number of
actions that the model
knows
how
to
prosecute, such as
attacking, dispersing,
holding, diverting and
withdrawing. Actions
are linked to situations
by a series of rules.
As an example, for a sighting situation, rules may be created based on the relative
size, distance and relationship (friendly, neutral or hostile) of the units detected.
Situations and rules have a priority order so that when encountering a new situation,
or reassessing an evolving situation, the model will work through the rules in order,
taking actions based on the highest priority rule that is applicable. Typically, a
relatively large number of rules are required. Furthermore, specifying rule sets that
behave in a credible fashion is not trivial. For these reasons, pre-defined rule sets for
common behaviours (for example, administrative move, advance, attack/assault,
recce/avoid, defend, and delay) are available which the user may select and modify.
This allows for ease of use and rapid tasking.
Each action that can be prosecuted by the model has an associated unit disposition
template. This is used, with the location of units that the formation is reacting to, to
enable the simulation to co-ordinate the movement of the formation in order to
prosecute an activity. Prosecution of an action includes automatic handling of
artillery, calling of air or helicopter support, engineering activity, such as bridging and
mine clearing, and logistics management.
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Simulation of modern warfare approaches in the JOCASTS War-Gaming System
Templates for marching order and final dispersals, together with the rules by which
the model prosecutes specific actions have all been specified with reference to the
Staff Officers’ Handbook. These algorithms have been used for a number of
exercises at the JSCSC (Advanced Command and Staff Course (ACSC) 2003, 2004
& 2005) to successfully represent behaviour of divisional sized formations in a
credible fashion.
The land and maritime representations have been demonstrated to work well for
formations or task groups acting independently. Co-operation between formations is
an inherently non-linear problem, and so complicated non-stable solutions are
possible, where the simulation oscillates through a range of behaviours. Currently,
this is controlled by care in setting up the SOPs, and the way in which the simulation
executes the formation actions. These non-stable solutions could also be damped by
extending the AI algorithms to represent hierarchies of formations, with rules of cooperation between individual formations. This would give a versatile and robust AI
solution with general applicability, and is the focus of the current development plan.
Understanding the complex behaviour of the interaction of hierarchies of co-operating
formations, and the effect the level of responsiveness of individual entities within
these hierarchies is important background work when attempting to incorporate the
understanding of the fundamental issues involved when moving to an EBAO
environment.
Air component artificial intelligence
The JOCASTS air-planning tool allows the user to supply a joint prioritised target list
for each of the phases of a campaign based on the effect required. The optimisation
of finding the best fit of aircraft to target is carried out by the JOCASTS interface,
which automatically assigns the aircraft, routing and timing required to service each
target in the prioritised list. This includes the specification of payloads of attack
aircraft and the selection of escort and support aircraft, such as en-route SEAD, ECM
and recce assets.
The
automatic
generation
process
can also specify the
targeting of particular
components
at
a
target location, for
example
runways,
facilities, aircraft or
logistics
at
an
airbase.
The
adjacent
figure
illustrates
the
prioritised target list
generation tool along
with feedback of the
missions to service
the target list on the
map and a chart
showing
aircraft
utilisation.
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The user controls the aircraft allocation process by mission templates, which specify
generic aircraft types, roles and numbers that will be used for that mission type. The
preference of aircraft and payload types against specific target types is also
configurable. These preferences provide a measure of fitness for a specific
combination of aircraft and payloads for each mission. This allows JOCASTS to
analyse all possible combinations to find an optimal fit of aircraft to target. Breaking
the optimisation into discrete steps, each processed in order, allows for rapid
processing of the problem. The fit considers aircraft availability both chronologically
and geographically.
Routing algorithms
ensure that missions avoid user-defined
Restricted Operation Zones (ROZ), as well as
known enemy SAM
threats.
The
adjacent
figure
illustrates the results
of the automatic
mission generation
with mission routes
shown in dashed
green lines.
The
missions have routed
around the ROZs
(shown
in
red),
routing
along
corridors (shown in
pink).
The chart
showing timelines of
aircraft
availability
illustrates how the
optimiser has timed
the missions.
It is also possible for squadrons to be reserved specific mission types. As the
simulation runs, air missions will be generated in response to specific situational
requirements, based on land and maritime SOPs, without the need for human
intervention, allowing the prosecution of time sensitive targets to occur.
The air-planning tool is fully integrated with the map display and graphical feedback
tools that detail the feasibility and weights of effort of the plans as they are
generated. Combined with the ease and speed of plan entry, this makes the tool
ideal for COA studies, allowing the user to see the effect and feasibility of a range of
possible actions. The tool has also been used as a standalone planning facility
outside the main JOCASTS resolution model.
Future development plans include the extension of the AI algorithms to build rules for
the simulation to perform target selection. The user would specify weights of effort in
specific regions, and JOCASTS would automatically generate target lists, from which
the current tools could generate the missions to service those target lists. This would
allow tasking at a higher operational level.
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Simulation of modern warfare approaches in the JOCASTS War-Gaming System
Conclusions
In this document the approach of recent development has been presented. The
focus of this development has been to achieve rapid tasking of theatre-scale
ORBATS whilst retaining the high fidelity of the level of detail of the existing
JOCASTS model. This is achieved through computer AI providing the decision
making of the tactical details of order generation, rather than increased aggregation
in the fundamental models that would lead to loss of resolution. This supports a
detailed representation of an NEC environment, as the tactical detail of platform
types, intelligence sharing and mission timings are resolved, facilitating the
representation of systems of systems.
An approach that maintains the level of detail that JOCASTS provides is also
important in supporting Manoeuvre Warfare and EBAO, as the full range of the
kinetic effects of war-fighting, both geographically and temporally, are preserved.
The high fidelity of the model, with rapid, computer assisted tasking and the control
afforded by the adjudication tools provides great flexibility, allowing the decision
makers and exercise control staff to concentrate on the effects and feasibility of a
range of COA.
Development is underway to build on the AI techniques already used with the land
component to model the behaviour of a range of non-military entities from insurgency
cells (terrorist or resistance groups, paramilitary forces or special forces) and local
population, to national and international political, economic and diplomatic bodies.
The increased representation of the DME instruments of power within the campaign
simulation will enable the students to plan and practice the full range of the CA and
EBAO and see the outcome in a common framework. Modelling a complete
synthetic environment of all aspects of a campaign will provide an unrivalled
capability in a training system that will allow the full spectrum of command and staff
training to be exercised.
Biography
Stephen graduated from Imperial College in 1994 with a PhD in Theoretical
Astrophysics. He continued his research interests at Imperial College as a Research
Associate for a further 6 years. His papers in galactic plasma jet formation, and highenergy cosmic ray acceleration enjoy success, and are still widely cited within the
community. In 2000 Stephen joined NSC, working mainly in the field of artificial
intelligence algorithms within war-gaming systems and support of HQ commander
and staff training exercises.
Acknowledgements
NSC gratefully acknowledge the guidance offered by JSCSC during these
developments, and the support of this work through funding from the Joint &
Battlefield Trainers Simulations & Synthetic Environments (JBTSE) team in the
Defence Procurement Agency (DPA).
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