Abstract:
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.
