Dr. Justine P. Rawal Profile

Dr. Justine P. Rawal

at DEVCOM Army Research Laboratory

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Publications (7)

Proceedings Article | 14 June 2024 Presentation + Paper

A generalized framework for multicriteria decision making under uncertainty

Kofi Nyarko, Peter Taiwo, Kelechi Nwachukwu, Justine Rawal, John Richardson, Adrienne Raglin

Proceedings Volume 13051, 130510L (2024) https://doi.org/10.1117/12.3015949

KEYWORDS: Decision making, Data processing, Visualization, Emergency preparedness, Statistical analysis, Uncertainty analysis

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In the face of increasing complexity and uncertainty in decision-making across various domains, there is a pressing need for frameworks that can adeptly navigate the multifaceted nature of such challenges. This paper introduces a novel, generalized framework designed to enhance multi-criteria decision-making under uncertain conditions. By integrating advanced data processing techniques with dynamic decision plan generation and adaptive mechanisms, the framework offers a robust solution to making informed decisions that are both flexible and responsive to changing information landscapes.

Central to the framework is its methodological rigor, which encompasses a comprehensive approach to data processing, including feature extraction and anomaly detection. This ensures decisions are grounded in accurate and up-to-date information. The framework employs a systematic algorithm for generating initial decision plans, which are then dynamically expanded to include co-requisite actions, ensuring comprehensive coverage of necessary interventions. A distinctive feature of the framework is its adaptive feedback mechanism, which refines decision plans based on the outcomes of implemented actions and new data, facilitating continuous improvement and relevance of decision strategies.

Furthermore, the paper details the application of advanced visualization techniques, such as Sankey diagrams, to elucidate the flow of information and the interdependencies between actions within the decision-making process. This not only enhances the transparency and interpretability of decisions but also fosters stakeholder engagement and consensus.

The versatility of the proposed framework is demonstrated through its applicability to a variety of domains, from emergency response planning to business strategy development, showcasing its potential to significantly improve decision-making practices. Through a meticulous examination of the framework's methodology and its application across diverse scenarios, this paper contributes to the advancement of decision science, oYering a scalable and adaptable solution to the challenges of multi-criteria decision-making under uncertainty.

Proceedings Article | 6 June 2022 Poster + Presentation + Paper

Enhanced tactical inferencing (ETI): an initial assessment

Adrienne Raglin, John Richardson, Jeffrey Basilotta, Mark Mittrick, Mathew Maness, Justine Caylor, Somiya Metu

Proceedings Volume 12113, 1211326 (2022) https://doi.org/10.1117/12.2622321

KEYWORDS: Intelligence systems, Data processing, Data modeling, Knowledge management, Analytical research, Taxonomy, Systems modeling, Visualization, Reconnaissance, Control systems

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Proceedings Article | 6 June 2022 Presentation + Paper

Enhanced tactical inferencing (ETI): a decision recommendation framework

Adrienne Raglin, John Richardson, Mark Mittrick, Somiya Metu, Justine Caylor

Proceedings Volume 12113, 121130Z (2022) https://doi.org/10.1117/12.2622319

KEYWORDS: Intelligence systems, Data modeling, Taxonomy, Situational awareness sensors, Telecommunications, Control systems, Analytical research, Visualization, Tactical intelligence, System integration

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Proceedings Article | 6 June 2022 Presentation + Paper

Metareasoning for multi-criteria decision making using complex information sources

Justine Caylor, Jeffrey Herrmann, Chou Hung, Adrienne Raglin, John Richardson

Proceedings Volume 12113, 121130Y (2022) https://doi.org/10.1117/12.2619418

KEYWORDS: Evolutionary algorithms, Visualization, Sensors, Data modeling, Probability theory, Machine learning, Decision support systems, Uncertainty analysis, Artificial intelligence

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Proceedings Article | 10 May 2019 Paper

Classification of military occupational specialty codes for agent learning in human-agent teams

Justine Caylor, Sean Barton, Erin Zaroukian, Derrik Asher

Proceedings Volume 11006, 110060W (2019) https://doi.org/10.1117/12.2518473

KEYWORDS: Machine learning, Analytical research, Statistical analysis, Principal component analysis, Visualization

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Proceedings Article | 3 May 2017 Presentation + Paper

Human-Assisted Machine Information Exploitation: a crowdsourced investigation of information-based problem solving

Sue Kase, Michelle Vanni, Justine Caylor, Jeff Hoye

Proceedings Volume 10207, 1020705 (2017) https://doi.org/10.1117/12.2263704

KEYWORDS: Decision support systems, Analytical research, Data processing, Scientific research, Stars, Standards development

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Proceedings Article | 3 May 2017 Presentation + Paper

Determining the perceived value of information when combining supporting and conflicting data

Timothy Hanratty, Eric Heilman, John Richardson, Mark Mittrick, Justine Caylor

Proceedings Volume 10207, 102070N (2017) https://doi.org/10.1117/12.2264820

KEYWORDS: Algorithm development, Information fusion, Information theory, Content addressable memory, Current controlled current source, Analytical research, Military intelligence, Fuzzy logic

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Showing 5 of 7 publications

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