Transcript VIATO - Visual Interactive Aircraft Trajectory Optimization

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
S ystems
Analysis Laboratory
Helsinki University of Technology
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Outline
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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
S ystems
Analysis Laboratory
Helsinki University of Technology
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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
a model
that imitates pilot decision making
S ystems
Analysis Laboratory
Helsinki University of Technology
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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?
S ystems
Analysis Laboratory
Helsinki University of Technology
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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
Probabilistic
or functional
dependence
Informational arc
Decision
Alternatives
available to DM
Conditional arc
Chance
Random
variables
Conditional arc
Deterministic
Conditional arc
Utility
Deterministic
variables
A utility
function
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Analysis Laboratory
Helsinki University of Technology
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Influence diagram (continued)
• State of the world is described by attributes
• States are associated with
– Utility
– Probability
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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
S ystems
Analysis Laboratory
Helsinki University of Technology
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Control decisions in one-on-one air combat
Decision maker (DM)
t=0
t=Dt
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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

t=Dt
t=0
Adversary (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
S ystems
Analysis Laboratory
Helsinki University of Technology
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ID for a control decision
Adversary's
Present
State
Present
Combat State
Present
State
Adversary's
Maneuver
Adversary’s
State
Present
Measurement
Combat
State
Measurement
Maneuver
State
Situation
Evaluation
Present
Threat Situation
Assessment
Threat Situation
Assessment
• Evolution of the players’ states described by a set of differential equations
• The behavior of the AD?
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Analysis Laboratory
Helsinki University of Technology
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ID game for a control decision
DM’s belief about
AD’s viewpoint
The game:
- Non-zero-sum
- Payoff = Expected utility
Solution:
Combat state
DM's
viewpoint
- Discrete controls =>
Matrix game
- Continuous controls =>
Nonlinear programming
- Nash or Stackelberg equilibrium
The best control of the DM against
the worst possible action of the AD
S ystems
Analysis Laboratory
Helsinki University of Technology
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Simulation example
• Initial state advantageous
for AD
• DM’s aircraft more agile
• Solution generated with
the ID game
• DM wins
altitude, km
DM
AD
X-range, km
y-range, km
S ystems
Analysis Laboratory
Helsinki University of Technology
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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
S ystems
Analysis Laboratory
Helsinki University of Technology
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