Can a Composite Agent be Used to Implement a Recognition-Primed Decision Model.pdfVIP

Can a Composite Agent Be Used to Implement a Recognition-Primed Decision Model? John A. Sokolowski Virginia Modeling, Analysis Simulation Center Old Dominion University 7000 College Dr. Suffolk, VA 23435 757-686-6215 jsokolow@odu.edu Keywords: Decision making, multi-agent system simulation, naturalistic decision theory ABSTRACT: 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. 1. Introduction 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. These models do not provide a realistic decision representation or decision result. Consequently, the military’s ability to train, to perform analysis, and to conduct war