In this paper, air combat simulation data is reconstructed into a dynamic Bayesian network. It gives a compact probabilistic model that describes the progress of air combat and allows efficient computing for study of different courses of the combat. This capability is used in what-if type analysis that investigates the effect of different air combat situations on the air combat evolution and outcome. The utilization of the dynamic Bayesian network is illustrated by analyzing simulation results produced with a discrete event air combat simulation model called X-Brawler.

A signal processing method is described, which separates harmonic sounds by applying linear models for the overtone series of sounds. Time-varying sinusoidal parameters are estimated in an iterative algorithm which is initialized using a multipitch estimator that finds the number of concurrent sounds and their frequency components. The iterative process then improves the estimates using the least-squares criterion. The harmonic stucture is retained by keeping the frequency ratio of overtones constant over time. Overlapping frequency components are resolved by using linear models for the overtone amplitudes. In practice, the models retain the spectral continuity of natural sounds. Simulation experiments were done using some basic structures for the linear models. These include polynomial, mel-cepstal and frequency-band model. Demonstration signals are available at

This paper presents two problem formulations for scheduling the maintenance of a fighter aircraft fleet under conflict operating conditions. In the first formulation, the average availability of aircraft is maximized by choosing when to start the maintenance of each aircraft. In the second formulation, the availability of aircraft is preserved above a specific target level by choosing to either perform or not perform each maintenance activity. Both formulations are cast as semi-Markov decision problems (SMDPs) that are solved using reinforcement learning (RL) techniques. As the solution, maintenance policies dependent on the states of the aircraft are obtained. Numerical experiments imply that RL is a viable approach for considering conflict time maintenance policies. The obtained solutions provide knowledge of efficient maintenance decisions and the level of readiness that can be maintained by the fleet.

In this paper, influence diagrams (IDs) are used as simulation metamodels to aid simulation based decision making. A decision problem under consideration is studied using discrete event simulation with decision alternatives as simulation parameters. The simulation data are used to construct an ID that presents the changes in simulation state with chance nodes. The decision alternatives and objectives of the decision problem are included in the ID as decision and utility nodes. The solution of the ID gives the optimal decision alternatives, i.e., the values of the simulation parameters that, e.g., maximize the expected value of the utility function measuring the attainment of the objectives. Furthermore, the constructed ID enables the analysis of the consequences of the decision alternatives and performing effective what-if analyses. The paper illustrates the construction and analysis of IDs with two examples from the field of military aviation.

This paper presents a new approach to the construction of game theoretic metamodels from data obtained through stochastic simulation. In this approach, stochastic kriging is used to estimate payoff functions of players involved in a game represented by a simulation model. Based on the estimated payoff functions, the players' best responses to the values of the decision variables chosen by the other players are calculated. In the approach, the concept of best response sets in the context of game theoretic simulation metamodeling is applied. These sets contain the values of the players' decision variables which cannot be excluded from being a best response and allow the identification of the potential Nash equilibria. The utilization of the approach is demonstrated with simulation examples where payoff functions are known a priori. Additionally, it is applied to data acquired by using a discrete event air combat simulation model.