Agent based simulation of decision making with uncertainty

Adrienne Raglin; Somiya Metu

doi:10.1117/12.2519030

10 May 2019 Agent based simulation of decision making with uncertainty

Adrienne Raglin, Somiya Metu

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060M (2019) https://doi.org/10.1117/12.2519030
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Adrienne Raglin and Somiya Metu "Agent based simulation of decision making with uncertainty", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060M (10 May 2019); https://doi.org/10.1117/12.2519030

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