Applying advanced user models and input technologies to augment military simulation-based training
SpringSim '10 Proceedings of the 2010 Spring Simulation Multiconference
Generating situation awareness for time critical decision making
Transactions on computational science XII
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Generating realistic behaviors for the non-player characters (also known as bots) is an important task for Military Operations on Urban Terrain (MOUT) simulations. Most of the current bots reasoning models are based on complete rational analysis of all alternatives. However, such models may not be adequate in time critical and uncertain situations. In such situations, due to time constraints and incomplete information, humans rely mainly on experience rather than some structured analysis of the given situation to make decisions. In our previous work, we have developed SNAP, a time critical decision-making framework which aims to imitate human decision-making processes for MOUT simulations. A major limitation that we found about SNAP is its difficulty in dealing with dynamic changes in MOUT simulations. In this paper, we propose to address this problem by incorporating a novel feature of expectations into SNAP. The effectiveness of this new feature is assessed with Twilight City, a virtual environment for MOUT simulations. Experimental results show that our expectation model works well in dealing with dynamic changes in MOUT. Copyright © 2009 John Wiley & Sons, Ltd. Generating realistic behaviors for the non-player characters (also known as bots) is an important task for Military Operations on Urban Terrain (MOUT) simulations. In such situations, due to time constraints and incomplete information, humans rely mainly on experience rather than some structured analysis of the given situation to make decisions. To address this issue, we had incorporated expectations in SNAP, a time critical decision-making framework which aims to imitate human decision-making processes for MOUT simulations.