Agent Based Evidence Marshaling: Discovery-Based Enhancement Tools for C2 Systems

摘要

Developers introduce new technologies at rates that defy prediction. This phenomenon applies to both new and existing sources of information, as well. As the recent attacks on America demonstrate, the result is an ever- increasing glut of information competing for our attention in ways that are unprecedented in history, potentially bringing even the most sophisticated command and control (C2) tools and practices to their knees. Conventional methods for organizing and focusing information for C2 purposes do support the current situation; for example, the scenario is one of the major methods in view within the NATO Guide to Best practice in C2 Assessment for this purpose. Scenarios can be of immense value in evaluating information and relationships of that information to various C2-related environmental constraints. The methods by which we construct and interact with scenarios must be subject to constant review, however. This paper offers novel methods for scenario development and interaction, based on modeling techniques that embrace multidisciplinary thinking the agent-based model. In fact, a meaningful method for better understanding how life and the massive information it routinely processes may actually be manifested in straight-forward uses of agent-based models. This paper describes and agent-based model called the Agent Based Evidence Marshaling (ABEM) model, and discusses ways to enhance scenarios that support Best Practices in Command and Control. ABEM brings to convergence centuries-old studies of semiotics and inference with recently introduced models for discovery and insight within an agent-based modeling environment-scenario development is one of ABEM's primary objectives.