Simulation of Modern Warfare Approaches in the Joint Operational Command And 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 JOCASTS CA & 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 framework Types 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