Command agents with human-like decision making strategies - Raza, Masood (Department of Engineering Systems and Management, 2010)
Human behaviour representation in military simulations is not sufficiently realistic, specially the decision making by synthetic military commanders. The decision making process lacks realistic representation of variability, flexibility, and adaptability exhibited by a single entity across various episodes. It is hypothesized that a widely accepted naturalistic decision model, suitable for military or other domains with high stakes, time stress, dynamic and uncertain environments, based on an equally tested cognitive architecture can address some of these deficiencies. And therefore, we have developed a computer implementation of Recognition Primed Decision Making (RPD) model using Soar cognitive architecture and it is referred to as RPD-Soar agent in this report. Due to the ability of the RPD-Soar agent to mentally simulate applicable courses of action it is possible for the agent to handle new situations very effectively using its prior knowledge. The proposed implementation is evaluated using prototypical scenarios arising in command decision making in tactical situations. These experiments are aimed at testing the RPD-Soar agent in recognising a situation in a changing context, changing its decision making strategy with experience, behavioural variability within and across individuals, and learning. The results clearly demonstrate the ability of the model to improve realism in representing human decision making behaviour by exhibiting the ability to recognise a situation in a changing context, handle new situations effectively, flexibility in the decision making process, variability within and across individuals, and adaptability. The observed variability in the implemented model is due to the ability of the agent to select a course of action from reasonable but some times sub-optimal choices available. RPD-Soar agent adapts by using ‘chunking’ process which is a form of explanation based learning provided by Soar architecture. The agent adapts to enhance its experience and thus improve its efficiency to represent expertise.