Dr. Dawn A. Lott Profile

Dr. Dawn A. Lott

at Delaware State Univ

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Publications (4)

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Proceedings Article | 12 April 2021 Presentation + Paper

Decisions, graphs, and artificial reasoning for uncertainty of information

Adrienne Raglin, Somiya Metu, Dawn Lott

Proceedings Volume 11746, 117461J (2021) https://doi.org/10.1117/12.2585517

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A decision is a conclusion reached after considering information, while decision making is the process of making choices after assessing alternatives with the information gathered. Most decisions made lead to an action or a choice. However, this process is often interactive and iterative in nature. Interaction can be from people, agents, or both. The interaction can take the form of additional data, different criteria, and changes to the goals for the decision. A single decision may not be the final decision but made at a single point that flows into another. Thus, iterations can initiate this flow of decisions at various points and in a cycle. In the decision cycle the decision may be repeated, as fine tuning of the system or tasks occur. Then there is the ongoing challenge that decisions nor the decision making process is simple nor is it perfect. Many techniques strive to capture the uncertainty that this imperfection creates. In this work we are referencing continued research being conducted under Artificial Reasoning for Uncertainty of Information (UoI). UoI is a reason based approach that builds on the concept of imperfect information. The objective of the UoI research is to represent and present the reasons, causes for specified uncertainty for a task, specifically the decisions for the task. As the UoI research expands to ideally allow greater adaptability, the area of graphs is being explored. Graphs are a decision making tool where connections between inputs (information, criteria, goals, etc…) and outputs (alternatives, choices, …) can be shown and analyzed. Utilizing graphs is also helpful as new capabilities are integrated into the UoI concept. This paper will explore how graphs are and can be used to incorporate new features and new capabilities particularly for selected warfighter functions.

Proceedings Article | 12 April 2021 Poster + Presentation + Paper

SAGE supporting operations research inspired algorithms

Somiya Metu, Adrienne Raglin, Dawn Lott

Proceedings Volume 11746, 117462T (2021) https://doi.org/10.1117/12.2585827

KEYWORDS: Computer simulations

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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.

Proceedings Article | 12 April 2021 Presentation + Paper

Decision making with uncertainty using the LRM method: MATLAB versus Java

Dawn Lott, Adrienne Raglin, Somiya Metu

Proceedings Volume 11746, 117460B (2021) https://doi.org/10.1117/12.2585826

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Challenges arise when sources of data are needed to provide information to teams of humans in multi-domain battle. Some critical challenges include the dependence and inter-dependence of heterogeneous devices and the uncertainty of information (UoI) obtained from these devices. UoI significantly affects the decision-making process and humans rely on underlying reasons for uncertainty in making decisions that rely on devices and data from these devices. The LRM Method is an excellent tool utilized to assist a decision-maker in support of military relevant operations. Previously, the LRM algorithm optimized the UoI and determined the decomposition of the UoI into its various elements via MATLAB. In recent work, the LRM algorithm is performed via Java. Although both algorithms compute the UoI to an error of within a negligible tolerance, there are small disparities in the decomposition of the UoI. Decomposing the UoI into its various parts is the main benefit of utilizing the LRM Method. These differences can have a significant impact on the decision-maker and the decision-making process.

Proceedings Article | 23 April 2020 Presentation + Paper

On the use of operations research for decision making with uncertainty for IoT devices in battlefield situations: simulations and outcomes

Dawn Lott, Adrienne Raglin, Somiya Metu

Proceedings Volume 11426, 1142609 (2020) https://doi.org/10.1117/12.2557870

KEYWORDS: Optimization (mathematics), Taxonomy, Visualization, Information visualization, Computer simulations, Analytical research, Internet, Device simulation, Data modeling, Performance modeling, Data transmission

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As the dependence on IoT devices which support teams of humans and agents increases, it becomes increasingly difficult to provide information to teams in a multi-domain battle. Numerous challenges are present and there are several sources of data needed and utilized in these operations, including internet of things (IoT). Some of the critical challenges include the dependence and inter-dependence of IoT devices and the uncertainty of information (UoI) obtained from these devices. Uncertainty of information significantly affects the decision-making process and humans rely on underlying reasons for uncertainty in making decisions that rely on devices and data from these devices. In this paper, the novel method called the LRM (Lott, Raglin, and Metu) Method is utilized in the construction of various scenarios for decision making involving IoT devices. The LRM method incorporates several sources of uncertainty and their relationship to taxonomies deemed important to humans in support of military relevant conditions. Each scenario provides supporting information that the uncertainty of information significantly affects the decision making process when tasks are performed.

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