Jeffrey D. Hicks

Intelligent agent-based software architecture for combat performance under overwhelming information inflow and uncertainty

In the highly dynamic and information rich environment of military submarine operations, the Commanding Officer must make split second decisions that could ultimately result in the destruction or survival of Ownship or the accomplishment of its designated mission. Despite extensive training and expertise, the fog of war often shadows real world encounters. Pieces of the tactical puzzle may be scattered, broken, and/or even missing. It is the Commanding Officers job to meld the fog into a clear tactical picture and intuitively decide upon the optimal course of action to obtain his mission goals and objectives. A mathematically derived optimal solution could be used as a decision making aid for the Commanding Officer to enhance Ownships performance. A highly innovative hybrid Bayesian/differential game modeling approach is being developed to tackle this problem. The resulting theory and technology will facilitate tactical operations by addressing such practical problems as tradeoff evaluation for course of action and maintaining tactical advantage while avoiding counter detection.

Cellular inference network (CIN) for learning and control of multi-agent cooperation

The coordination and learning process in a multiagent system (MAS) can be modeled as a control and adaptation process of a hybrid dynamic system. We study an inference network organized in cellular structure for supporting multiple levels of abstraction and reasoning in learning and control of MAS. Specifically, we study how the cellular inference structure can serve as a representation of hybrid dynamic systems model and be applied to the knowledge gathering and refinement processes of the MAS. The study shows that it is a promising model for distributed reasoning in the presence of uncertainties in the domains of knowledge management, deconfliction, and integration.