SAGE supporting operations research inspired algorithms.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING FOR MULTI-DOMAIN OPERATIONS APPLICATIONS III(2021)

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摘要
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.
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关键词
Sentry Agents, Uncertainty of Information, decision-making, simulation, operations research
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