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Kai Virtanen, Tuomas Raivio and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology

Simulating Pilot’s Decision Making by an Influence Diagram Game. Kai Virtanen, Tuomas Raivio and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology. Outline. Air combat simulation models Existing modeling approaches Influence diagram (ID)

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Kai Virtanen, Tuomas Raivio and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology

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  1. Simulating Pilot’s Decision Making by an Influence Diagram Game Kai Virtanen, Tuomas Raivio and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology

  2. Outline • Air combat simulation models • Existing modeling approaches • Influence diagram (ID) • Control decisions in one-on-one air combat • ID for a control decision • ID game for a control decision • Simulation example • Conclusions

  3. Air combat simulation models • Analysis of air combat and pilot training are expensive tasks • Every air combat situation cannot be analyzed in practice • Real time piloted: • Training in a realistic environment • Batch: • Controlled and repeatable environment • Discrete-event approaches Computer generated forces need amodel that imitates pilot decision making Orders Commands

  4. Existing modeling approaches • Dynamic optimization and game theory: • Optimal flight paths • Simple performance criteria • Lack of realistic uncertainty models • Non-real-time computation • Models emulating the decision making of a pilot: • Computational techniques of AI: Rule-based, Value-Driven • Capture the preferences of a pilot • Real-time computation • Short planning horizon • => Not optimal but myopic control commands • How to handle uncertainties? Behavior of the opponent?

  5. Influence diagram (ID)(Howard et al. 1984) • Directed acyclic graphs • Describes the major factors of a decision problem • Widely used in decision analysis application areas Time precedence Informational arc Alternatives available to DM Decision Random variables Conditional arc Chance Probabilistic or functional dependence Deterministic variables Conditional arc Deterministic A utility function Conditional arc Utility

  6. Influence diagram (continued) • State of the world is described by attributes • States are associated with • Utility • Probability • Utility is a commensurable measure for goodness of attributes • Results include probability distributions over utility • Decisions based on utility distributions • Information gathering and updating using Bayesian reasoning

  7. ¼ Control decisions in one-on-one air combat t=Dt t=Dt Decision maker (DM) ¼ t=0 t=0 Adversary (AD) Find the best maneuvering sequence for the DM with respect to the goals 1. Avoid being captured by the AD 2. Capture the AD by taking into account - Preferences of a pilot - Uncertainties - Dynamic decision environment - Behavior of the AD • Influence diagrams representing • the control decision of the DM: • Single stage ID (Virtanen et al. 1999), • pilot’s short-term decision making • Multistage ID (Virtanen et al. 2001), • preference optimal flight paths against given trajectories • New model: Influence diagram game

  8. ID for a control decision Adversary's Present State Adversary's Maneuver Adversary’s State Measurement Combat State Present Measurement Present Combat State Situation Evaluation Present State State Maneuver Present Threat Situation Assessment Threat Situation Assessment • Evolution of the players’ states described by a set of differential equations • The behavior of the AD?

  9. ID game for a control decision DM’s belief about AD’s viewpoint • The game: • - Non-zero-sum • - Payoff = Expected utility • Solution: • - Discrete controls => • Matrix game • - Continuous controls => • Nonlinear programming • - Nash or Stackelberg equilibrium Combat state The best control of the DM against the worst possible action of the AD DM's viewpoint

  10. Simulation example DM • Initial state advantageous for AD • DM’s aircraft more agile • Solution generated with the ID game • DM wins AD altitude, km X-range, km y-range, km

  11. Conclusions • The influence diagram game: • Models preferences under uncertainty and multiple competing objectives in one-on-one air combat • Takes into account • Rational behavior of the adversary • Dynamics of flight • Utilization: • Air combat simulators, a good computer guided aircraft • Contributions to the existing air combat game formulations • Several computational difficulties are avoided • Roles of the players are varied dynamically • Producing reprisal strategies • Other simulation applications

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