Snap: A time critical decision-making framework for MOUT simulations

摘要

Deliberative reasoning based on the rational analysis of various alternatives often requires too much information and may be too slow in time critical situations. In these situations, humans rely mainly on their intuitions rather than some structured decision-making processes. An important and challenging problem in Military Operations on Urban Terrain (MOUT) simulations is how to generate realistic tactical behaviors for the non-player characters (also known as bots), as these bots often need to make quick decisions in time-critical and uncertain situations. In this paper, we describe our work on Snap, a time critical decision-making framework for the bots in MOUT simulations. The novel features of Snap include case-based reasoning (CBR) and thin slicing. CBR is used to make quick decisions by comparing the current situation with past experience cases. Thin slicing is used to model human's ability to quickly form up situation awareness under uncertain and complex situations using key cues from partial information. To assess the effectiveness of Snap, we have integrated it into Twilight City, a virtual environment for MOUT simulations. Experimental results show that Snap is very effective in generating quick decisions during time critical situations for MOUT simulations.