Type of Research:
Government funded (Joint Warfighting Center, U.S. Joint Forces Command & Defense Modeling and Simulation Office) academic research.
Summary:
The author (Sokolowski) is associated with the Virginia Modeling, Analysis & Simulation Center at Old Dominion University. In this paper he describes the methods he used to create a multiple agent system, employing Recognition-Primed Decision Making (RPD), entitled RPDAgent that was designed to simulate strategic decisions made by a senior military commander. The author first examined von Neumann’s classical decision theory before deciding upon RPD (a method by Klein5 to describe Naturalistic Decision Making (NDM)).
The ‘test bed’ that Sokolowski created was a hypothetical amphibious landing. Sokolowski then presented the scenario to thirty senior U. S. and Coalition military officers and recorded their responses to four strategic decision making points:
- Which of four beaches to attack?
- When to attack?
- Should the timing of the attack be changed due to enemy troop movements?
- Should the attack be called off after encountering high casualties and strong enemy resistance?
RPDAgent evaluated 20 variables that were narrowly defined such as Beach Topography: Sand Type: Coarse or Fine (see also Representing Knowledge and Experience in RPDAgent. Sokolowski (2003)). RPDAgent, which also employed stochastic methods, was run 200 times and its responses to the four above decisions were recorded. The RPDAgent decisions were remarkably similar those of the 30 senior military officers (showing a standard deviation of between 0 and 2.4185).
Sokolowski then conducted a version of the famous Turing Test (see Computing Machinery and Intelligence) by showing the results of the RPDAgent simulation runs combined with the responses of the original 30 senior military officers to five generals (three four-star and two three-star generals) and recorded their responses.
The results (shown below) would qualify for “passing” the Turing Test.