Dynamic Course Of Action Analysis integrates the Predictive Battlespace Awareness process with the air battle plan to predict the likelihood of Time Sensitive Target occurrence, the likelihood of their discovery, the likelihood that, once discovered, they can be successfully attacked, and the overall probability that TSTs can be successfully countered given the configuration of the daily air battle plan. The Dynamic Course Of Action Decision-aid is a tool that generates predictions and presents them in the form of probabilistic maps, indicating the likelihood of TST occurrence in a given geographical area and the Strike and ISR coverage in those areas. These maps are overlaid on a situational display to provide operators information regarding "gaps" in TST coverage. This paper addresses the problem of properly anticipating TSTs, develops prediction models that assist in the decision making process, and defines the DCOAD architecture. DCOAD's architecture consists of three major components; the graphical user interface, the shared data services, and the estimator framework. These fit into the paradigm of the model-view-controller architecture used in most graphical applications.
A key component of the Joint Air Operations (JAO) environment is the dynamic control of resources in the presence of uncertainty. This control involves the allocation of resources (e.g., different aircraft types) to prosecute targets and collect information while accounting for uncertain future events and partial, imperfect observations. The objective is to maximize the reward associated with the effective prosecution of targets, which is contingent on information collection, while minimizing loss of resources. In this paper, we extend an earlier formulation of an optimal dynamic resource allocation problem to explicitly include the dynamics of information collection and to identify the complexities involved. We then describe a simulation-based approach that was developed to solve the dynamic JAO control problem in the presence of partial and imperfect information.
A key component of a Joint Air Operation (JAO) environment is the planning and dynamic control of missions in the presence of uncertainties. This involves the assignment of resources (e.g., different aircraft types) to targets while taking into account and anticipating the effect of random future events and, subsequently, dynamic control in response to various controllable and uncontrollable events as missions are executed in a hostile and rapidly changing setting. The objective is to maximize the reward associated with targets while minimizing loss of resources. In this paper, we first formulate the problem of optimal mission assignment and identify the complexities involved due to combinatorial and stochastic characteristiscs. We then describe a discrete event simulation tool developed to model the JAO environment and all of its dynamics and stochastic elements and to provide a testbed for several methods we are developing to solve the problem of agile mission control. We describe some of these methods, including approximate dynamic programming using rollout algorithms and optimal resource allocation schemes, and present some numerical results.