Command and Control

The concept of Effects-based operations (EBO) has been introduced to address new and emergent threats. For successful EBO, the question isn't "Did the weapon hit the target?"; but rather, "Did the weapon, attack, or campaign yield the desired effects?" By creating plans based on the assessment of the current situation, the planner can make decisions to shape the battle space that enhance the effectiveness of operations while limiting enemy options. The proposed Multi-Objective Bayesian Situation Assessment for Interactive Planning (MSBSA) system will support multiple aspects of EBO operations including fusion of sensor reports, situation assessment, and planning/replanning. The proposed system will efficiently create utility-optimal strategies that maximize the chance of achieving desired effects with minimum costs, continuously adjusting to changes in the environment. The MSBSA system for EBO planning uses Bayesian networks as a mean of seamlessly integrating sources of planning uncertainty into the final plans. Bayesian networks will be dynamically constructed from smaller Bayesian network fragments which we term Multi-Entity Bayesian Networks (MEBNs). A stochastic learning algorithm called popMCMC will use situation assessments from these MEBNs to find an optimal set of plans. System users can select any of the resulting plans based on the current objective priorities.