1. 1 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Jan Jelinek Model Predictive Risk Control of Military Operations

2. 2 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Our Focus Task Level Mission Execution Level (Tactical) Task Group Level Operational Level Campaign Level Battlefield Levels in the Command and Control Hierarchy Resource allocation to tasks Task definition, sequencing & resource allocation M 2 PC Role: Minimal effective force compositions Threat Assessment

3. 3 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Concept of Operations {…,T 11, T 12,…,T 1N,…} O... Task Level Mission Execution Level Task Group Level Operational Level... T 21 T 22 T 2N... TG 2 T 11 T 12 T 1N... TG 1 {T 11 } Campaign Level T 11 (k) Battlefield

4. 4 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Task Specification Slots Objective: Destroy a Given Red Asset = Degree of destruction <= 100% of the asset Deadline: In 10 Missions or Less Importance:With 95 % Certainty or Better Own Losses: - No limit = Victory-At-Any-Cost task formulation - Not more than 3 strike airplanes = Victory-With-Acceptable-Loss task formulation Asset specs: Red side: Strength Lethality BDA quality Blue side: Strength Lethality BDA quality

5. 5 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Battle Dynamics Battle dynamics as perceived by Blue Battlefield Red commander Blue BDA Red BDA Blue commander u(k) x(k) y(k) v(k) u’(k),v’(k) Red’s Intent Blue’s Intent Blue has to cope with 5 sources of uncertainty: Battlefield (= random effects of weapons) Red’s intent Red commander’s strategy to fulfill the intent Red’s Battle Damage Assessment capability His own Battle Damage Assessment capability

6. 6 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Model Predictive Task Commander (MPTC) Desired probability of win in K missions or less Max acceptable loss Combatants characteristics (strength, lethality, BDA) MPC Monte Carlo Battle Simulator Battle Damage Assessment after the k-th mission MPTC Package flying the k-th mission Minimal effective Blue forces for the whole sequence of missions up to the task completion Package composition Sensitivity analysis MPTC determines: Force-on-force Predictive Models Game-theoretic optimization

7. 7 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Predictive Models of Battle Dynamics State distributions for the first 8 rounds / missions starting from the initial state (4,11) (read row-wise, density is proportional to probability with black being 1) This information is available prior to battle and should be exploited for resource allocation and scheduling

8. 8 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control B underestimates the strength of R assets Actual Red strength was systematically underestimated by 50%

9. 9 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Optimal vs. Satisficing Solutions Military vs. mathematical requirements (e.g., target values) A minimal effective package meeting task specifications can be composed in many ways Package composition flexibility greatly complicates resource allocation Offensive part of package Defensive part of package

10. 10 KSCO 2002, Toulouse, April 23-24, 2002 Model Predictive Risk Control Resource Allocation and Task Scheduling Averaging effect increases feasibility level – demand for resources is additive – if task are independent, then bad luck is as likely as good luck Handling task infeasibility – Reporting it to the task order issuer – Task order issuer may delegate some of his authority to define task orders to the task group commander, e.g.:  relax importance (= probability of win) –We developed Multiple Resource Task Allocator  drop some tasks already in progress based on, e.g.: –probability of win –own losses task group variance = task variance ofnumber tasks