SAGE supporting operations research inspired algorithms

Somiya Metu; Adrienne Raglin; Dawn A. Lott

doi:10.1117/12.2585827

12 April 2021 SAGE supporting operations research inspired algorithms

Somiya Metu, Adrienne Raglin, Dawn A. Lott

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462T (2021) https://doi.org/10.1117/12.2585827
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Conference Poster

Abstract

SAGE (Sentry Agents), is a dynamic multi-agent-based framework for system automation. It is used for creation of virtual agents with defined behaviors and states. The SAGE framework provides the flexibility for agents to exist independently and also to interact and collaborate with each other for task execution. As part of the capabilities within SAGE, simulations can be generated. For this work, SAGE has been used to create scenarios for simulation of decision tasks with uncertainty. Decision-making is a challenging task. The complexities of decision-making increases when the information that supports and forms decision comes with any uncertainty. Uncertainty can be represented in different ways however for this simulation, the Uncertainty of Information (UoI) concept will be utilized. This allows understanding of the various sources of uncertainty and their impact on decision making. One of the UoI algorithms implemented is the LRM version which is based on operations research. This version computes an UoI value that is used as an input to the decision tasks within the simulation. In this paper, we detail the construction of those scenarios and its integration into SAGE.

Conference Presentation

Citation Download Citation

Somiya Metu, Adrienne Raglin, and Dawn A. Lott "SAGE supporting operations research inspired algorithms", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462T (12 April 2021); https://doi.org/10.1117/12.2585827

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