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Proceedings Paper

Agent based simulation of decision making with uncertainty
Author(s): Adrienne Raglin; Somiya Metu
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Paper Abstract

As humans and agents or machines are utilized to accomplish missions in multi domain battles there is an increase in artificial intelligence (AI) and machine learning (ML) to support the interaction. However, the techniques and algorithms within AI/ML are not without challenges. One key challenge is how uncertainty of the results influences decision making. Added to this challenge is where uncertainty is introduced and how it impacts the decision making tasks. Uncertainty can come from limitations in the data that is used to develop or train the AI/ML model to lack of confidence in the behavior or suggestions that the agents generate. Uncertainty can come from underlying motivations or objectives tied to the mission to interpretations of the operational area. In this paper we will define and scope uncertainty, linking it to selected components of decision making. We will discuss the generation of a measure of uncertainty that we are including in simulations along with the supporting information that will impact the decisions. Following this, the selected parameters values for several simulations for multi domain battle scenarios are presented. Analysis and evaluation of the results from these simulations will be shown. Supporting data will be mentioned to frame the results and the plans for future investigations.

Paper Details

Date Published: 10 May 2019
PDF: 9 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060M (10 May 2019); doi: 10.1117/12.2519030
Show Author Affiliations
Adrienne Raglin, U.S. Army Research Lab. (United States)
Somiya Metu, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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