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Game Theoretic Validation of Air Combat Simulation Models

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Jirka Poropudas and Kai Virtanen Systems Analysis Laboratory Helsinki University of Technology jirka.poropudas_at_hut.fi, kai.virtanen_at_hut.fi – PowerPoint PPT presentation

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Title: Game Theoretic Validation of Air Combat Simulation Models


1
Game Theoretic Validation of Air Combat
Simulation Models
  • Jirka Poropudas and Kai Virtanen
  • Systems Analysis Laboratory
  • Helsinki University of Technology
  • jirka.poropudas_at_hut.fi, kai.virtanen_at_hut.fi

2
Air combat simulation
  • Air combat is analyzed to compare effectiveness
    of tactics and ways for conducting missions as
    well as system performance
  • Test flights are expensive and time consuming ?
    constructive simulation
  • Discrete event simulation models provide a
    controlled and reproducible environment that may
    be complex and convoluted with many levels of
    sub-models
  • Air combat simulation model
  • Aircraft, weapon systems, radars, other
    apparatus
  • Pilot decision making and situation awareness
  • Uncertainties

Validation of the model?
3
Existing validation and optimization approaches
  • Simulation metamodels
  • Mappings from simulation input to output
  • - Response surface methods, regression models,
    neural networks, etc.
  • Validation methods
  • Real data, expert knowledge, statistical methods,
    sensitivity analysis
  • Simulation-optimization methods
  • Ranking and selection, stochastic gradient
    approximation, metaheuristics, sample path
    optimization

One-sided approaches ? Action of the adversary
is not taken into account
The game theoretic approach!
4
The game theoretic approach
  • Definition of the scenario
  • Aircraft, weapons, sensory and other systems
  • Initial geometry
  • Objectives ? Measures of effectiveness (MOEs)
  • Available tactics and systems Tactical
    alternatives
  • Simulation of the scenario using the simulation
    model
  • Input tactical alternatives
  • Output MOE estimates
  • Estimation of games from the simulation data
    using statistical techniques
  • Use of the games in validation

5
Games in validation
  • Goal Confirming that the simulation model
    performs as intended
  • Comparison of the scenario and properties of the
    game
  • Symmetry
  • Symmetric scenarios gt symmetric games
  • Dependence between decision variables and payoffs
  • Dependence between tactical alternatives and MOEs
  • Best responses and Nash equilibria
  • Explanation and interpretation based on the
    scenario
  • Initiative
  • Making ones decision before or after the
    adversary gt Advantageous/disadvantageous?
  • Explanation and interpretation based on the
    scenario

6
Validation example Aggression level
  • Two-on-two air combat scenario
  • Identical aircraft, air-to-air missiles, radars,
    data links, etc.
  • Symmetric initial geometry
  • Identical tactical alternatives
  • - Aggression levels of pilots Low, Medium, High
  • Objectives gt MOEs
  • - Blue kills, red kills, difference of kills
  • Simulation using X-Brawler
  • Many versus many air combat simulation
  • Discrete event simulation methodology
  • Aircraft, weapons and other hardware models
  • Elements describing pilot decision making and
    situation awareness

7
Validation results
Payoff Blue kills
  • Expert knowledge
  • Increasing aggressiveness ?
  • Increasing causality rates
  • MOE blue kills
  • Low aggressiveness for red
  • High aggressiveness for blue
  • Dependence
  • Increasing aggressiveness
  • gt Increase of blue kills
  • Best responses Nash equilibria
  • Medium or high for blue, low for red
  • Medium and high leading to the same outcome gt
    Possible shortcoming

8
Validation results
Payoff Blue kills Red kills
RED, min
  • Expert knowledge
  • Increasing aggressiveness
  • gt Increasing causality rates
  • Symmetric scenario
  • gt Symmetric game

BLUE, max
  • Symmetry
  • MOE estimates approximately zero when the
    decisions coincide
  • E.g., low, high gt best, worst AND high, low gt
    worst, best
  • Dependence
  • Increasing aggressiveness gt Increasing causality
    rates for both sides
  • Medium and high for blue leading to the same
    outcome gt Possible shortcoming
  • Best responses Nash equilibrium
  • Low for blue, low for red

9
Conclusions
  • Novel way to analyze air combat
  • Combination of discrete event simulation and game
    theory
  • Extension of one-sided validation and
    optimization approaches
  • Validation
  • Properties of games ? Air combat practices
  • Simulation data in an informative form
  • Comparison of tactical alternatives using games
  • Systematic means for analyzing air combat
  • - Single simulation batch
  • Other application areas involving game settings
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