Cognitive amplification for contextual game-theoretic analysis of courses of action addressing physical engagements

Abstract

A method employing cognitive amplification that reasons within a comprehensive context suited to making decisions in a time constrained scenario, such as battle planning. Select embodiments meld military science with the military art needed for relevant and timely decision making. In select embodiments, a Battlefield Terrain Reasoning Awareness, Battle Command (BTRA-BC) Battle Engine (BBE) significantly reduces the time a staff requires for battle planning. BBE “cognitively amplifies” the ability of planners to conduct Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP). Consequently, a resultant “human-computer reasoning team” develops and analyzes tactical Courses of Action (COAs) much faster than humans alone and better than computers alone. By exhaustively comparing a multitude of variables that comprise Friendly Courses of Action (FCOAs) and Enemy Courses of Action (ECOAs), embodiments permit a user to expend intellectual energy considering the effect of these variables rather than trying to identify them.

1 . A system employing cognitive amplification allowing planners to efficiently conduct Intelligence Preparation of the Battlefield (IPB) and the Military Decision Making Process (MDMP), said system enabling reasoning within at least one context and facilitating decision making in time constrained scenarios, comprising:
 at least one specially programmed computer;   computer readable media in operable communication with said computer; and   a Battlefield Terrain Reasoning Awareness, Battle Command (BTRA-BC) Battle Engine (BBE) contained on said computer readable media for processing on said computer,   wherein said computer compares a multitude of variables that comprise Courses of Action (COAs) including at least Friendly Courses of Action (FCOAs) and at least Enemy Courses of Action (ECOAs), and   wherein said system melds military science with military art needed for relevant and timely decision making, and   wherein said system significantly reduces the time said planners require for battle planning by cognitively amplifying the ability of planners to conduct said IPB and said MDMP, and wherein a human-computer reasoning team employing said system develops and analyzes said COAs much faster than humans alone and better than said computer alone, and   wherein said system permits a user to expend intellectual energy considering the effect of said variables rather than trying to identify all said variables.   
     
     
         2 . A process facilitating timely and efficient mission planning in context, comprising:
 providing fast-abstract war gaming scenarios;   providing realistic estimates of combat effects of terrain for said war gaming scenarios;   providing realistic combat attrition estimates for said scenarios;   providing comprehensive integration of MDMP and IPB doctrinal processes in said scenarios; and   providing computer reasoning in harmony with, and at the direction of, human users to facilitate evaluation and comparison of various said scenarios.   
     
     
         3 . A semi-automated method, steps in said method closely adhering to cognitive processes used in exploiting intelligence and in decision making, comprising:
 providing at least one specially programmed computer and computer readable media;   on said at least one computer, performing a network-pulse analysis of mobility corridors to yield an Articulated Modified Combined Obstacle Overlay (MCOO) as a set of virtual lanes (V-lanes) on a Game Board establishing a network of mobility corridors from a start line to an objective line,   wherein said analysis offers approximately all the information of a doctrinal said MCOO while providing information about battlefield physics;   providing a Mission, Enemy, Terrain, Troops, and Time (METT-T) Parser loaded on said computer readable media,   wherein said METT-T Parser examines battlefield physics of inputs thereto, producing at least an Enemy Course of Action (ECOA) Variable set and a Friendly Course of Action (FCOA) variable set, and   wherein said METT-T Parser establishes sets of all possible instances for each of said ECOA and said FCOA Variables;   providing to said METT-T Parser first data from an MCOO data base developed from a software application termed MCOO-Maker, said first data provided together with a Braswell Index establishing a logical partition of an area of operation (AO);   wherein articulated detail of said Articulated MCOO aid a computer to explicitly reason through issues that human experts implicitly understand, and wherein said Articulated MCOO establishes maneuver options for units by identifying obstacles to movement, said mobility corridors between said obstacles, and logical groupings of said mobility corridors, and   wherein said Articulated MCOO used together with said Braswell Index establishes a game board upon which an automated planner develops attrition estimates for an engagement established via at least one pre-specified Friendly Course of Action (FCOA) and at least one pre-specified Enemy Course of Action (ECOA), and   providing second data to said (METT-T) Parser from at least one Enemy Order of Battle (EOB) data base,   wherein said EOB is a representation of equipment quantity and type that may be displayed as Game Pieces on said Game Board;   providing third data to said (METT-T) Parser from at least one Friendly Order of Battle (FOB) data base;   wherein said FOB data base provides a set of friendly said game pieces for use with said game board and said EOB game pieces,   providing fourth data to said (METT-T) Parser from at least one Missions and Postures data base,   wherein said Mission and Posture inputs set the beginning Game State, and wherein said Mission and Posture inputs provide game context to said METT-T, and   wherein said user assigns ratings to both said FOB and said EOB sets comprising:
 Unit Strength ratings, 
   wherein said Unit Strength ratings at least reflect attrition from previous combat operations;
 Unit Posture ratings, 
   wherein said Unit Posture ratings are provided from a set of well-established tasks; and
 Unit Morale ratings, 
   wherein said Unit Morale ratings supplement said Terrain Informed War Game Model with effects due to training, fatigue, leadership, psychology, moral issues, and combinations thereof; and   wherein said Braswell Index provides an abstracted index of terrain effects that enables said METT-T Parser to load a realistic representation of said game board into RAM, and   wherein METT-T Battle Context Mapping employs a human-computer set of procedures enabling exploration of a game-theoretic dynamic of a pending engagement consistent with said military MDMP and IPB doctrine, and   wherein said METT-T Parser and associated said FCOA and ECOA Variable Sets provide a Terrain Informed Articulation of major elements of an abstracted concept decision, and   wherein said METT-T Parser develops possible battlefield physics instances for each said ECOA and each said FCOA Variable, and arranges sets of instances to reasonably maximize neighborliness describing the correlation between any two adjacent instances of said FCOA and said ECOA Variables and their contributions to the final evaluation score of a solution when all other variables are controlled, and   wherein said process facilitates FCOA optimization through a genetic algorithm;   providing a Terrain Informed War Game Model on said computer readable media, said War Game Model employing an attrition model to determine likely results of combat;   wherein an attrition calculation based on said attrition model employs estimates of relative combat power of opposing forces, and   wherein said War Game Model employs the Quantitative Judgment Method of Analysis (QJAM), that incorporates an historical basis for assessing relative power of individual weapons;   receiving as input to said War Game Model relative weapons estimates from a BTRA-BC Battle Engine Weapons Assessment and Calculation Tool (B-WACT),   wherein said B-WACT implements said QJMA concept to develop relative combat power for individual weapons as well as weapon systems that aggregate said weapons, and   wherein a user may provide characteristics of a weapon to said B-WACT and said B-WACT provides a QJMA relative combat power for said weapon, and   wherein said B-WACT enables said user to aggregate weapons into weapon systems that also receive a QJMA relative combat power, and   wherein said B-WACT publishes lists of weapon systems as a data file for use as a possible input;   wherein said user employs the same process described for said Enemy OB, except that said QJMA relative combat power is calculated for friendly units by aggregating estimates from said B-WACT of relative combat power for weapons within said friendly units comprising said FOB;   inputting Superiority Toggle ratings to said Terrain Informed War Game Model, said ratings comprising:
 ratings for Intelligence, Surveillance, and Reconnaissance; 
 ratings for Command and Control; and 
 ratings for Air Superiority; and 
   Game Time Slice;   wherein said Game Time Slice supplements said Terrain Informed War Game Model model allowing said user to adjust temporal resolution;   inputting to said method 5 th  data comprising criteria for a Desired End State, wherein said 5 th  data facilitates an articulated, user-adjustable multi-criteria process to evaluate said FCOAs;   inputting to said method  6 h data comprising said user's ECOA IPB set; wherein said ECOA and said FCOA variable sets enable user selection of at least one FCOA and at least one ECOA.   providing a Graphic User Interface (GUI) for visualization, displaying selection of at least said ECOAs,   wherein a pull-down menu is displayed for each of said ECOA Variables, enabling selection of variable instances desired in constructing and displaying each said ECOA with an associated set of variable instance selections;   selecting a variable instance for each of said variables in said FCOA variable set to assert a said FCOA by:
 analyzing a said set of candidate FCOAs by comparing selected said FCOAs to selected said ECOAs in said War Game Model; 
 deciding which tactics to employ; 
   choosing options from a said FCOA Variable set to establish FCOAs to form an FCOA candidate set;   conducting a risk analysis for each said FCOA in said FCOA candidate set;   identifying possible battlefield physics options via said METT-T Parser for each said ECOA and each said FCOA variable set;   wherein dominate variables are defined as those upon which others may depend and dominated variables as those that may depend on other variables;   analyzing the effect of reasonable tactical dynamics a user may employ; and   estimating a logical, representative set of options for said ECOAs against which a battle analysis is conducted for each said FCOA in a said FCOA candidate set;   producing a set of defensive said ECOAs that become said ECOA IPB set, wherein said user selects a set of offensive said FCOAs, rather than a single said FCOA for possible execution;   selecting a representative said ECOA IPB set and a said FCOA set for a specified engagement,   wherein said ECOA IPB set is stabilized for much of a game-theoretic analysis, thus avoiding a co-evolutionary paradigm in which a late-generation said FCOA may be vulnerable to an early-generation said ECOA; and   implementing an FCOA Evaluator that estimates an end state of a submitted said FCOA for each said ECOA in said ECOA IPB set,   wherein said user retains an ability to modify said ECOA IPB set until the start of a systematic evaluation of said FCOAs, at which time said ECOA IPB set is locked, and   wherein if said user later adjusts said IPB ECOA set, all relevant changes to said FCOAs are re-submitted to said FCOA Evaluator to insure a corresponding updated said evaluation, and   wherein standardization of an evaluation metric is guaranteed by locking in said IPB ECOA set as well as evaluation criteria for said Desired End State with a process termed FCOA Optimization Employing a Genetic Algorithm (GA), and   wherein locking in said IPB ECOA set insures that all said FCOAs considered by said GA use the same evaluation metric;   standardizing said evaluation metric by locking in said IPB ECOA set and said evaluation criteria for said Desired End State via said FCOA Optimization Employing a Genetic Algorithm (GA),   wherein said locking in of said IPB ECOA set insures that all said FCOAs considered by said GA use the same evaluation metric;   performing an automated secondary analysis termed an FCOA Vulnerability Analysis that employs steps of said method recursively similar to a Reverse IPB Analysis, to establish those said ECOAs that are optimized against a selected said FCOA,   wherein said FCOA Vulnerability Analysis outputs a set of Most Dangerous ECOAs with associated scripts;   establishing a first said ECOA IPB set;   employing said FCOA Vulnerability Analysis to yield a second said ECOA set;   comparing said first and second ECOA sets;   re-initiating said step if said FCOA Vulnerability Analysis identifies a, said ECOA or said ECOA set different from said ECOA IPB set;   adding said newly identified ECOA or ECOA set to original said ECOA IPB set;   employing Terrain Informed War Game Model to provide Game Rules for said Game Board established in said Articulated MCOO and said Braswell Index inputs, and Game Pieces established in said Enemy and said Friendly OBs,   wherein said user, or an automated process working on behalf of said user, selects one said ECOA from said ECOA IPB set and one said FCOA from said FCOA Candidates set;   employing said Terrain Informed War Game Model to conduct a simulation of combat for selected said ECOAs and said FCOAs and outputting a time-phased estimate of location and strength of selected said subordinate units during said engagement,   wherein said simulation is fast because said Game Board, said Game Pieces, said Game Strategies, and said Game Rules are abstracted to facilitate fast calculations in said RAM;   employing said Terrain Informed War Game Model to output time-phased snapshots of disposition and strength of selected said units,   wherein abstractions of all said Game Pieces are crafted to retain only that information pertinent to aggregate both attrition and maneuver posture of said selected units;   employing repeated, game-theoretic submissions of said FCOAs and said ECOAs to said Terrain Informed War Game Model directed by strategy of said FCOA Optimization Employing a Genetic Algorithm,   wherein said submissions facilitate development of an emergent intelligence on appropriate candidate tactics to use in a situation specified by said METT-T Parser;   implementing said FCOA Evaluator to direct said Terrain Informed War Game Model to engage to-be-evaluated said FCOAs iteratively from said FCOA Candidate set against said ECOA IPB set,   comparing said Desired End State against a final said snapshot produced by said Terrain Informed War Game Model during each said iteration; and,   employing a set of unique protocols and weights assigned to specified criteria for   said Desired End State to yield a numeric score for said criteria of said Desired End State;   implementing an articulated, automated Risk Deprecation Analysis, comprising:
 iterating evaluation results incorporated in each Results Matrix associated with said FCOA Evaluation for a plurality of iterations, deprecating a different said ECOA Results column from a said Results Matrix each time to yield a Deprecated Ranking (DR) score reflecting the merit of an individual said FCOA relative to other candidate said FCOAs when said deprecated ECOA is deprecated from said ECOA IPB set and a Change in Ranking (CR) reflecting the change in said DR score from a non-deprecated analysis, 
   wherein said Risk Deprecation analysis enables said user to quickly understand the risk of each said FCOA candidate relative to any ECOA in said ECOA IPB set, and wherein said Risk Deprecation Analysis allows trade-off analysis of alternatives to address inherent limitations of said ECOA IPB set and said aggregated scores of said FCOAs, and   wherein each said DR score in said FCOA Evaluation reflects a war gaming analysis against non-deprecated said ECOAs within said ECOA IPB set;
 implementing an automated, articulated Evaluation Criteria Deprecation Analysis, comprising:
 deprecating said criteria for said Desired End State individually 
 rather than deprecating each said ECOA from said ECOA IPB set, 
 
   wherein said Evaluation Criteria Deprecation Analysis enables said user to fully evaluate the cost of each said evaluation criterion for said Desired End State in terms of finding a said FCOA that would otherwise score well against remaining non-deprecated said evaluation criteria; and   wherein, as a result of said Evaluation Criteria Deprecation Analysis,, said user may decide to accept a said FCOA with an otherwise low score, since the cost of a specific said deprecated evaluation criterion is much more than originally anticipated, and   wherein automation for said Evaluation Criteria Deprecation Analysis parallels said Risk Deprecation Analysis with said Results Matrix for each said Evaluation Criteria Deprecation Analysis and said Risk Deprecation Analysis being similar, and   wherein said Evaluation Criteria Deprecation Analysis allows said user to quickly understand the relative cost of each of said criteria used to evaluate said FCOA candidate set for the original said Desired End State;
 selecting a said FCOA evaluation criterion and deleting all other said FCOA evaluation criteria and implementing said selected FCOA evaluation criterion in said simulation; 
 conducting an FCOA Vulnerability Analysis of a selected said FCOA, 
   wherein said user executes said FCOA Vulnerability Analysis in the same manner as said Reverse IPB process, except that said user submits only said selected FCOA versus a single to-be-re-evaluated said ECOA in a said ECOA IPB set;
 conducting an optimization analysis to find those said FCOAs that are optimized against a selected said ECOA, 
 wherein said user employs said FCOA Vulnerability Analysis to identify said ECOAs optimized against said selected FCOA, and 
   wherein said identification of said ECOAs optimized against said selected FCOA enumerates vulnerabilities of said selected FCOA and facilitates employment of countermeasures to reduce said vulnerabilities, and
 submitting each said identified ECOA to said Terrain Informed War Game Model to produce snapshot sets that pre-inform intelligence collection activities; 
 developing Projected Scripts for at least one said Most Dangerous ECOAs v. said selected FCOA, said Projected Scripts comprising:
 snapshot sets produced by said Terrain Informed War Game Model in a last phase of said FCOA Vulnerability Analysis after said at least one Most Dangerous ECOA has been identified; 
 information facilitating development of an IPB Event Template with associated Event Matrix, 
 
   wherein employing said Projected Scripts for at least one said Most Dangerous ECOA provides said user sufficient time to react to a potential vulnerability, and wherein said Projected Scripts for at least one said Most Dangerous ECOA maintain a time-phased estimate of location and status of all said units relative to said mobility corridors;
 developing Projected Scripts for Most Likely ECOAs v. said selected FCOA, said Projected Scripts for Most Likely ECOAs comprising:
 snapshot sets produced by said Terrain Informed War Game Model in a last phase of said FCOA Vulnerability Analysis after said Most Likely ECOAs have been identified; 
 
 information facilitating development of said IPB Event Template with said associated Event Matrix, 
   wherein employing said Projected Scripts for Most Likely ECOAs provides said user sufficient time to react to a potential vulnerability, and   wherein said Projected Scripts for Most Likely ECOAs maintain a time-phased estimate of location and status of all said units relative to said mobility corridors;
 maintaining a subset of said ECOA IPB set that represents Most Likely Candidate ECOAs, 
   wherein said user retrieves said subset of said ECOA IPB Set to resubmit to said Terrain Informed War Game Model with said selected FCOA; and
 developing two IPB products, an IPB Event Template and corresponding Event Matrix in accordance with military IPB doctrine, 
   wherein said IPB Event Template displays where to collect information indicating said COA adopted by said opposing force, and   wherein said Event Matrix supports said IPB Event Template by providing narrative details, and   wherein said IPB Event Template and said Event Matrix together pre-inform intelligence collectors by focusing collection requirements, and   wherein said user manually develops said IPB products by comparing relative disposition of forces in both said Most Dangerous ECOAs and said Most Likely Candidate ECOAs Scripts by conducting a differential analysis to find unique disposition indicators, and   wherein said user establishes a Named Area of Interest (VAI) on said IPB Event Template as a polygon at an entry point of said mobility corridor, and   wherein said user records into said Event Matrix associated activity.   
     
     
         4 . The method of  claim 3  in which said METT-T Parser populates the following said ECOA Variables:
 Total Unit Variables,   wherein said METT-T Parser establishes at least one ECOA Variables set for a total unit, and   wherein a user chooses instances of said ECOA variables in each of multiple pull- down menus by clicking a submit button after selecting said instances;   Variables for Units Subordinate to a Total Unit,   wherein said METT-T Parser establishes a set of said ECOA Variables for each said subordinate unit;   Task Organizable Units Variables,   wherein said Task Organizable Units Variables assigns selected smaller said subordinate units to larger said subordinate units as implemented in said Terrain Informed War Game Model.   
     
     
         5 . The method of  claim 3  in which said ECOA variables comprise:
 Num Abreast,   wherein said Num Abreast variable describes the number of columns said unit employs in formation;   Unit Boundaries,   wherein said Unit Boundaries variable describes the location of internal boundaries between subordinate said units, subject to selection of said Num-Abreast variable;   Unit Formation,   wherein said Unit Formation variable describes a set of possible arrays of subordinate units, given selection for said Num-Abreast COA variable;   Unit Assignments,   wherein said Unit Assignment variable binds specified said subordinate units to specified formation slots;   Anchor LDT,   wherein said Anchor LDT variable assigns a game board LDT as the location of a primary defensive array of said subordinate units;   Priority of General Support (GS) Units by Formation Slot,   wherein said GS Units by Formation Slot variable facilitates supporting all said subordinate units;   Severity of GS by Formation Slot;   wherein said Severity of GS by Formation Slot variable provides a percentage distribution of said GS for each said subordinate unit.   
     
     
         6 . The method of  claim 3  in which said support unit FCOA variables comprise:
 Left and Right Boundaries variable,   wherein said METT-T Parser assigns control measures that constrain physical deployment of each said subordinate unit beyond assigned said left and right boundaries;   Anchor Line Setback variable,   wherein said METT-T Parser assigns a physical distance that a selected said subordinate unit should displace behind mid-point of said Anchor Line LDT;   Reinforce Policy variable,   wherein said METT-T Parser assigns categories of Neither, Left, Right, or Both as a policy for a selected said subordinate unit that when not attacked, allows said selected subordinate unit to reinforce a neighboring said subordinate unit on defense;   Withdrawal Criteria variable,   wherein said METT-T Parser assigns said Withdrawal Criteria in a range from approximately 95% to approximately 5% strength, an attrition threshold that, when met, directs said Terrain Informed War Game Model to withdraw a said unit from combat;   Delay-or-Reserve variable,   wherein said METT-T Parser directs a selected said unit to either a Delay or a Reserve mission, if said selected unit is not participating in a main anchor line defense, as prescribed by a formation selection;   Delay Depth variable,   wherein said METT-T Parser directs the depth of a delay if a selected said subordinate unit is assigned that task in said Delay-or-Reserve Variable;   Reserve Lag Distance variable,   wherein said METT-T Parser directs the distance a reserve emplacement is located behind an anchor line, if said selected subordinate unit is assigned a reserve task in said Delay-or-Reserve Variable;   Reserve Threshold variable,   wherein said METT-T Parser directs a threshold of total unit attrition required before commitment of a said subordinate unit in reserve, if said selected subordinate unit is assigned a Reserve task in said Delay-or-Reserve COA Variable;   Reserve Guidance variable,   wherein said METT-T Parser directs the employment philosophy of said selected subordinate unit, when committed, if said selected subordinate unit is assigned a Reserve task in said Delay-or-Reserve Variable;   Reserve Lane variable,   wherein said METT-T Parser directs V-Lane emplacement of said selected subordinate unit, if a specified said selected unit is assigned a Reserve mission by said Delay-or-Reserve COA Variable; and   Upon Penetration,   wherein said METT-T Parser directs actions of said selected subordinate unit if penetrated by an attacking force, assuming said selected subordinate unit is selected as part of an anchor-line defense.   
     
     
         7 . The method of  claim 6  in which said Left and said Right Boundary variables cooperate to identify a set of contiguous said Virtual (V)-Lanes for deploying each said unit, all said units inside assigned said boundaries for each said unit. 
     
     
         8 . The method of  claim 3  in which the distribution of said instances for said Unit Boundaries and said Unit Formation variables follows Pascal's Triangle for Binomial Expansion. 
     
     
         9 . The method of  claim 3  in which said METT-T Parser develops said instances for n-factorial bindings, where n is the number of said subordinate units. 
     
     
         10 . The method of  claim 3  in which said METT-T Parser, with said Anchor LDT, identifies as possible options all said LDTs input to a said game board. 
     
     
         11 . The method of  claim 3  in which Anchor Line Setback represents a common military technique, and said METT-T Parser provides a set of instances enabling modeling of said Anchor Line Setback option for each said selected subordinate unit. 
     
     
         12 . The method of  claim 3  in which Offensive said COAs are similar to Defensive said COAs. 
     
     
         13 . The method of  claim 3  in which said METT-T Parser populates said FCOA Variables comprising:
 Total Unit variables,   wherein said Total Unit variables establish a set of FCOA Variables for a total unit;   Subordinate Unit Variables,   wherein said METT-T Parser establishes a set of said FCOA Variables for each said subordinate unit;   Task Organizable Unit Variables,   wherein said Task Organizable Units display a tactical assignment of selected small said subordinate units to larger said subordinate units as implemented in said Terrain Informed War Game Model.   
     
     
         14 . The method of  claim 3  in which said METT-T Parser establishes a set of Offensive FCOA Total Unit Variables, comprising:
 Num Abreast Variables,   wherein said Num Abreast variables describe the number of columns said unit employs in formation as mitigated by the number of available said subordinate units and number of available said V-lanes;   Unit Boundary Variables,   wherein said Unit Boundary variables describe the location of internal boundaries between subordinate said units, subject to selection of said Num-Abreast variable;   Unit Formation Variables,   wherein said Unit Formation variables describe a set of possible arrays of subordinate units, given selection for said Num-Abreast variable;   Unit Assignment Variables,   wherein said Unit Assignment variables bind specified said subordinate units to specified formation slots;   Priority of General Support (GS) Units by Formation Slot Variable,   wherein said Priority of GS Units variables establish priorities of selected said subordinate units for allocation of general support resources and said GS Units support all said subordinate units;   Severity of GS by Formation Slot Variable;   wherein said Severity of GS by Formation Slot variable provides a percentage distribution of said GS for each said subordinate units.   
     
     
         15 . The method of  claim 3  in which said METT-T Parser establishes a set of Offensive FCOA Variables reflecting user selections for each said subordinate unit, comprising:
 Subordinate Unit Variables,   wherein said METT-T Parser establishes said FCOA Variables for each said subordinate unit to reflect selections of a user for each selected said subordinate unit.   Left and Right Boundary Variables,   wherein said METT-T Parser assigns control measures that constrain physical deployment of a said selected subordinate unit by identifying a set of contiguous said V-Lanes for said selected subordinate unit to deploy in;   Stutter Start Variable,   wherein said METT-T Parser specifies a wait-time before initial movement for lead attacking said subordinate units, enabling said total unit to create common military formations for movement;   Bypass Criteria Variable,   wherein said METT-T Parser establishes a policy for how much defensive force an attacking said selected subordinate unit can bypass once said defense force has been breached;   Withdrawal Criteria Variable,   wherein said METT-T Parser assigns an attrition threshold that when met, directs said Terrain Informed War Game Model to withdraw said selected subordinate unit from combat;   Follow-and-Support (F&S) or Reserve Variables,   wherein said METT-T Parser establishes guidance to direct said selected subordinate unit to either a F&S or a Reserve mission if said selected subordinate unit is not attacking;   Reserve Lane Variable,   wherein said METT-T Parser directs emplacement of said selected subordinate unit in a said V-Lane, if said F&S-or-Reserve Variable assigns said selected unit a reserve mission;   Reserve Threshold Variable,   wherein said METT-T Parser directs the level of overall unit attrition that must be tolerated before committing said selected subordinate unit if said selected subordinate unit is assigned a reserve task;   Reserve Guidance Variable,   wherein said METT-T Parser establishes the employment philosophy of said selected subordinate unit when committed to attack if said selected subordinate unit is assigned said reserve task in said F&S-or-Reserve Variable;   Reserve Lag Distance Variable,   wherein said METT-T Parser establishes the distance of reserve emplacement behind said anchor line if said selected subordinate unit is assigned said reserve task in said F&S-or-Reserve Variable;   Upon Penetration Variable,   wherein said METT-T Parser establishes actions of attacking said selected subordinate unit should said attacking subordinate unit penetrate a defense, employing four policy instances of Stay (stop), Left Envelop (turn left), Right Envelop (turn right), and Turn Deep (go straight);   At OBJ Variable,   wherein said METT-T Parser establishes the actions of attacking said selected subordinate unit upon reaching an assigned objective via alternative said policy instances of said Stay (at the objective) or Expand (to neighboring objectives), with respect to said Unit Boundary Variables; and   Task Organizable Unit Variables,   wherein said METT-T Parser enables display of the tactical assignment of selected small units to larger said subordinate units that are components of said total unit.   
     
     
         16 . The method of  claim 15  in which said METT-T Parser provides a full set of instances for said Reserve Guidance variable in four alternatives comprising:
 Stay in Lane,   wherein said Stay in Lane alternative directs said selected reserve unit to remain in said initial V-lane;   Best Dent,   wherein said Best Dent alternative directs said selected reserve unit to the defense location closest to penetration;   Best Hole,   wherein said Best Hole alternative directs said selected reserve subordinate unit to the most significant penetration, and   First Hole,   wherein said First Hole alternative commits said selected reserve subordinate unit to the first penetration, regardless of whether said Reserve Threshold has been met.   
     
     
         17 . The method of  claim 3  in which a user develops ECOAs by hand-selecting ECOA Variables. 
     
     
         18 . The method of  claim 3  in which a user develops said ECOA IPB set by conducting a Reverse IPB analysis made from the perspective of an opposing force, comprising:
 running said IPB process in reverse to render choices, using the same said terrain game board as input with said Articulated MCOO and said Braswell Index;   swapping friendly and enemy Orders of Battle and Mission Postures; and   recursively employing said Reverse-IPB procedure to enable said user to identify at least one said ECOA from said ECOA IPB set available to an opposing force,   wherein said Reverse IPB analysis optimizes mapping of the game-theoretic context of an engagement.   
     
     
         19 . The method of  claim 3  in which said user manually adjusts said ECOA IPB set after an initial analysis of the relative merits of two or more FCOAs,
 wherein, if said user manually adjusts said ECOA IPB set, changes to said FCOAs are re-submitted to said FCOA Evaluator to update said evaluation.   
     
     
         20 . The method of  claim 3  in which said user employs Manual FCA Optimization, said FCOA Optimization thru a Genetic Algorithm and said FCOA Evaluator to submit improved said FCOAs to said FCOA Candidate Set. 
     
     
         21 . The method of  claim 3  in which said Terrain Informed War Game Model employs at least sub-processes comprising:
 Arraying Initial said game pieces in accordance with settings of said ECOA and said FCOA Variables,   wherein said Terrain Informed War Game Model translates directions from submitted said ECOA IPB and said FCOA sets, and develops appropriate said game pieces representing said units from said Enemy and said Friendly OBs, deploying said game pieces to start positions on said Articulated MCOO Game Board;   Incrementing a Time-Slice Counter,   wherein said user inputs said Missions and Postures and specifies a desired game time slice and said Terrain Informed War Game Model iterates a time-phased series of sub-steps until termination criteria are met, said sub-steps facilitating maneuvering and engaging said game pieces in accordance with stored policies, thresholds, and guidance for specific engagements, said sub-steps comprising:   positioning said game pieces in accordance with current situation and Variable Settings of said FCOAs and said ECOAs,   wherein said Terrain Informed War Game Model acknowledges physical constraints and moves each said game piece in accordance with said user's selections of said FCOA and said ECOA Variables;   Calculating Attrition for Game Pieces in Contact,   wherein said Terrain Informed War Game Model places selected said game pieces of friendly forces in a firefight when said game pieces of opposing forces move within a predetermined engagement distance in the same said mobility corridor occupied by selected said game pieces of friendly forces, and   wherein said Terrain Informed War. Game Model compares the status of each said game piece with said Desired End State criteria, said thresholds, and said policies in executing each said selected FCOA and simulates action;   Assessing Attrition and Updating Status for Each said Game Piece, wherein if two said game pieces are participating in an active firefight, then said Terrain Informed War Game Model assesses said attrition by reducing current strength of participating said game pieces;   Creating Snapshots of Locations of said Game Pieces and Stat using Same, wherein a record is created of location and status of every said game piece on said game board during a specified said time slice;   Testing of Battle Termination Criteria,   wherein if said Engagement Termination Criteria have not been met, said Terrain Informed War Game Model iterates at said step of Incrementing a Time Slice Counter and when said current engagement passes said Testing of Battle Termination Criteria, then said Terrain Informed War Game Model finalizes said Snapshot Set for use by said FCOA Evaluator and for said Visualization;   Outputting Battle Snapshot Sets,   wherein said Terrain Informed War Game Model develops a time-phased set of said snapshots taken during the course of said engagement, one per said time slice, that is used for later evaluation by said FCOA Evaluator, for said Visualization, or for both, and wherein said Terrain Informed War Game Model finalizes a set of said snapshots upon termination of said engagement, and   wherein if said user executes a war game for the purpose of said Visualization, said Terrain Informed War Game Model outputs an entire set of said snapshots to a Visualization device, and   wherein said Visualization device provides a display of said Game Board with controls for directing which said snapshot to permit said user to quickly run an animation on, said animation able to be presented in either forward or reverse, and   wherein if a purpose of said sub-steps is to support said FCOA Evaluator, said Terrain Informed War Game Model outputs only the last said snapshot of said engagement.   
     
     
         22 . The method of  claim 21  in which said Terrain Informed War Game Model in Calculating Attrition for Game Pieces in Contact performs the steps of:
 determining relative combat power of each said game piece, as modified by local terrain effects abstracted by said Braswell Index in a pre-specified said mobility corridor,   wherein said step of determining relative combat power upgrades said game pieces that leverage terrain, and downgrades said game pieces that disregard terrain characteristics;   consulting an implementation of the Dupuy QJMA attrition model to determine how much said attrition each said game piece should suffer during subsequent said time slices,   wherein since said Terrain Informed War Game Model assesses attrition during each of a plurality of said time slices, yielding a discretized approximation of the Lanchester Differential Equation for combat attrition.   
     
     
         23 . The method of  claim 3  supporting said doctrinal Military Decision Making Process (MDMP) requiring an analysis, via war gaming, of said FCOA candidates against said ECOA IPB set developed during said IPB by providing said FCOA Evaluator with a Desired End State as an evaluation criterion. 
     
     
         24 . The method of  claim 3 , said Desired End State criteria further comprising:
 Overall Unit Criteria,   wherein said Overall Unit Criteria Candidates are used to establish a goal of optimizing overall percentage end strength of a said unit, and   wherein said Overall Unit Criteria forms an enemy-based objective in which said enemy attrition is a prime consideration;   Time Criteria,   wherein said Time Criteria are employed to establish performance indicators for specified said FCOA candidates, and   wherein said Time Criteria allow said user to visualize how time affects performance;   Specific Unit Criteria,   wherein said Specific Unit Criteria establish goals of optimizing end strength of specified said subordinate units of a said unit, and   wherein said Specific Unit Criteria permit a user to specify a said unit regardless of employment or may specify uncommitted said reserve units, regardless of which said units said FCOA assigns as a reserve unit;   Mobility Corridor Criteria,   wherein said Mobility Corridor Criteria establish terrain-based objectives with goals of maximizing or minimizing end strength of a said unit at specified locations within said mobility corridors on said game board, and   wherein said user develops said Desired End State by selecting a combination of said Desired End State criteria that reflects how said user prefers said game board to appear at the end of a successful mission, and   wherein said user establishes a weighting scheme to reflect relative preferences for each of said Desired End State criteria.   
     
     
         25 . The method of  claim 24  providing a default set of said Desired End State criteria, wherein said default set supports planning and analysis prior to formally establishing said Desired End State criteria. 
     
     
         26 . The method of  claim 3  implementing an FCOA optimization technique to facilitate finding a finite number of sufficient FCOA candidates to consider, said FCOA optimization technique selected from the group consisting of: Manual FCOA Optimization, FCOA Optimization Employing a Genetic Algorithm, and any combination thereof in any order of implementation. 
     
     
         27 . The method of  claim 26  providing said FCOA optimization technique as Manual FCOA Optimization, employing said ECOA IPB set and said FCOA candidate set via said Terrain Informed War Game Model as input to said FCOA Evaluator together with input for said Desired End State to accomplish one or more evaluations of said FCOA candidates;
 wherein said Manual FCOA Optimization iterates a reasonable number of times to permit timely evaluation.   
     
     
         28 . The method of  claim 26  providing said FCOA optimization technique as said FCOA Optimization Employing a Genetic Algorithm, employing said ECOA IPB set and said FCOA candidates via said Terrain Informed War Game Model as input to said FCOA Evaluator together with input for said Desired End State to accomplish one or more evaluations of said FCOA candidates,
 wherein said FCOA Optimization thru a Genetic Algorithm may iterate as many as several thousand times employing automation available on said specially programmed computer.   
     
     
         29 . The method of  claim 3  providing techniques to further analyze relative merits of said FCOA candidate set, said techniques considering factors that were previously encapsulated in a cumulative score for said FCOA candidate set, said techniques consisting of additional analysis of previously abstracted information selected from the group consisting of: Manual FCOA Optimization, FCOA Optimization Employing a Genetic Algorithm, and any combination thereof in any order of implementation,
 wherein said techniques to further analyze relative merits of said FCOA candidates facilitate selection of a said FCOA.   
     
     
         30 . The method of  claim 29 , stopping said FCOA Optimization Employing a Genetic Algorithm when said user decides an FCOA Candidate Set is optimized,
 wherein said user makes an MDMP FCOA decision by selecting or modifying one of said FCOAs in said optimized FCOA Candidate set.   
     
     
         31 . The method of  claim 29  providing at least two techniques to analyze relative merits of said FCOA candidates: risk deprecation and evaluation criteria deprecation, wherein said two techniques consider factors previously encapsulated in said cumulative score, and
 wherein said two techniques optimize selection of an FCOA.   
     
     
         32 . The method of  claim 3  further providing opportunity for said user to highlight values in a column of said Results Matrix to further facilitate decision making by choosing any selections from the group consisting of: “greater than selection,” “less than selection,” or “no highlight.” 
     
     
         33 . The method of  claim 3  further providing for FCOA comparison tools selected from the group consisting of: color coded highlighting, filters and combinations thereof. 
     
     
         34 . The method of  claim 33  said color coded highlighting comprising Red-Amber-Green color coding and said filters as COA-variable filters. 
     
     
         35 . The method of  claim 3  in which one said countermeasure is to identify within a pre-specified time interval if an opposing force is executing a Dangerous ECOA.