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Mission Plan Recognition: Developing Smart Automated Opposing Forces for Battlefield Simulations and Intelligence Analyses.

ADA486796

Publication Date	2008
Personal Author	Levchuk, G. M.; Grande, D.; Pattipati, K. R.; Levchuk, Y.; Kott, A.
Page Count	42
Abstract	A key challenge for battlefield simulation is the estimation of enemy courses of action (COAs). Current adversarial COA development is a manual time-consuming process prone to errors due to limited knowledge about the adversary and its ability to adapt. Development of decision aids that can predict adversary's intent and range of possible behaviors, as well as automation of such technologies within battlefield simulations, would greatly enhance the efficacy of training and mission rehearsal solutions. In this paper, we describe the development of OPFOR agents that can intelligently learn BLUEFOR's mission plan. This knowledge will allow OPFOR agent to reason about the intent of BLUE and counteract accordingly to prevent/influence the future BLUEFOR's operations by affecting current operations, challenging BLUE's resources, and preparing OPFOR for future battles.
Keywords	Algorithms Enemy Recognition Missions Combat simulation Military planning Learning machines Automation Intelligence Symposia Command and control systems Battlefield simulation Plan recognition Mission plans Intelligence analyses Coa(Courses of action) Opfor(Opposing forces) Blue forces Sbir(Small business innovation research) Sbir reports Sbir phase 1
Source Agency	Non Paid ADAS
NTIS Subject Category	74G - Military Operations, Strategy, & Tactics 74F - Military Intelligence
Corporate Authors	Aptima, Inc., Woburn, MA.
Supplemental Notes	Presented at the International Command and Control Research and Technology Symposia (13th) (ICCRTS 2008) held in Seattle, WA on 17-19 Jun 2008. Document includes briefing charts (25 slides, title same as report). The original document contains color images.
Document Type	Technical Report
Title Note	Conference paper.
NTIS Issue Number	200902

Mission Plan Recognition: Developing Smart Automated Opposing Forces for Battlefield Simulations and Intelligence Analyses.

Mission Plan Recognition: Developing Smart Automated Opposing Forces for Battlefield Simulations and Intelligence Analyses.
ADA486796

Publication Date	2008
Personal Author	Levchuk, G. M.; Grande, D.; Pattipati, K. R.; Levchuk, Y.; Kott, A.
Page Count	42
Abstract	A key challenge for battlefield simulation is the estimation of enemy courses of action (COAs). Current adversarial COA development is a manual time-consuming process prone to errors due to limited knowledge about the adversary and its ability to adapt. Development of decision aids that can predict adversary's intent and range of possible behaviors, as well as automation of such technologies within battlefield simulations, would greatly enhance the efficacy of training and mission rehearsal solutions. In this paper, we describe the development of OPFOR agents that can intelligently learn BLUEFOR's mission plan. This knowledge will allow OPFOR agent to reason about the intent of BLUE and counteract accordingly to prevent/influence the future BLUEFOR's operations by affecting current operations, challenging BLUE's resources, and preparing OPFOR for future battles.
Keywords	Algorithms Enemy Recognition Missions Combat simulation Military planning Learning machines Automation Intelligence Symposia Command and control systems Battlefield simulation Plan recognition Mission plans Intelligence analyses Coa(Courses of action) Opfor(Opposing forces) Blue forces Sbir(Small business innovation research) Sbir reports Sbir phase 1
Source Agency	Non Paid ADAS
NTIS Subject Category	74G - Military Operations, Strategy, & Tactics 74F - Military Intelligence
Corporate Authors	Aptima, Inc., Woburn, MA.
Supplemental Notes	Presented at the International Command and Control Research and Technology Symposia (13th) (ICCRTS 2008) held in Seattle, WA on 17-19 Jun 2008. Document includes briefing charts (25 slides, title same as report). The original document contains color images.
Document Type	Technical Report
Title Note	Conference paper.
NTIS Issue Number	200902