Can a Composite Agent Be Used to Implement a Recognition-Primed Decision Model
John A. Sokolowski · 2002
Legacy constructive military simulation systems such as the Corps Battle Simulation (CBS) and the Joint Theater Level Simulation (JTLS) have simple models of military commanders and their decision-making process. It would be useful to the military community to advance the robustness and the realism of these decision models. To improve human decisions in a simulation we need better models of human decision-making. The Recognition-Primed Decision (RPD) model was developed to explain the mental process that decision makers, especially experienced ones such as senior military commanders, go through to arrive at a decision. Several efforts are ongoing to try to produce a computational model of RPD. These efforts are centered on rule-based, neural network, and fuzzy logic approaches. This paper analyzes a different approach, using a composite agent, to implement the RPD model. A composite agent uses multi-agent system simulation technology to implement various cognitive processes of a single entity or agent. It is this author's contention that a composite agent's decision-making method closely matches that described by the RPD model. This close match is expected to produce a better implementation of the RPD model.