Simulation of Modern Warfare Approaches in the

Joint Operational CommandAnd Staff Training System (JOCASTS)

S.G. Lucek, NSC

August 2005

ISMOR22

Artificial Intelligence (AI) behaviour algorithms in JOCASTS

The use and development of JOCASTS AI behaviour algorithms to consider– The Comprehensive Approach (CA)– The Effects Based Approach to Operations (EBAO)

Introduction

‘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’ [The Joint Doctrine and Concepts Centre]

Explicitly links military operations to political goals

Comprehensive Approach (CA)

It is the effect(s) visited upon the adversary (or environment) that is critical

All friendly forces activity should be designed to deliver the required effect(s)

EBAO is evolutionary not revolutionary, and builds on concepts such as Manoeuvre Warfare

Effects Based Approach to Operations (EBAO)

Fully tri-service

Multiple alliances

Formation to theatre level operations– From 10 to 400 students– 50,000 entities

Exercising officers typically from army major equivalent to one-star

Used in UK (JSCSC) and abroad– Supports Higher and Advanced Command and Staff Courses

(HCSC, ACSC)

JOCASTS

Detailed & proven combat resolution engine– Air/Maritime: platform on platform – Land: aggregated units

Vital for Manoeuvre Warfare representation

Detail makes representation of

Network Enabled Capability possible

AI algorithms allow for rapid and easy tasking (all sides, friendly, neutral and enemy)

JOCASTS Fidelity

Formation: group of units with common order

3 building blocks– Dispersal + movement algorithms– Decision making rules– Action resolution model

Land Formation Model

Movement Definition

Situation

Decision

Action

Behaviour Definition

LFM Movement Movie

Part of Exercise System– Quick and flexible input of commanders intent– Control staff presented with tactical results and

situation in a format suitable for rapid assessment– Assess effects in terms of wider political context

and psychological effects– Amend orders / situation– Results fed back to the students in a realistic

fashion

Current JOCASTSCA & EBAO Exercises

AI algorithms implemented to translate directives to detailed tactical tasking– Situation assessment– Decision making– Enact resulting action

Apply algorithms to non-military entities

Representation of political, diplomatic and economic context

Feedback on military operations (morale)

CA & EBAO Development

Non-Military Representation

Generic frameworkTypes of entity represented– Insurgency cells (terrorist/paramilitary/resistance

groups or special forces)– Local populations– Refugees– Political, economic and diplomatic bodies (national

and international)– NGO– Economic/transport/communication networks

Non-Military Representation

Properties– Wealth, resources– Goals– Support for military operations– Morality– Agitation– Interests (e.g. political, economic, ecological)

Rules for property change– Change of level of resources, properties or status in specific

area for specific entities– Actions by entities in specific area– Change of territory

Non-Military Representation

Actions based on current state– Demonstration– Diplomatic incident– Public riot– Occupation of media or embassy– Robbery– Destruction of private property– Disruption to military infrastructure– Sniper / Bomb attack

Conclusions

JOCASTS is a powerful tool– AI algorithms allow it to be flexible without loosing detail and fidelity

Flexibility is core to current usefulness supporting exercises where CA and EBAO are practiced

Extending proven AI behaviour algorithms to represent the Political, Diplomatic and Economic context for campaign

Feedback on military behaviour allows for a direct representation of CA and EBAO within the system