Contingent attention management in multitasked environments

Hesham Fouad; Ranjeev Mittu; Derek Brock

doi:10.1117/12.2524979

10 May 2019 Contingent attention management in multitasked environments

Hesham Fouad, Ranjeev Mittu, Derek Brock

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 1100620 (2019) https://doi.org/10.1117/12.2524979
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Artificial Intelligence (AI) technology is being applied successfully in a number of domains. Advances in low cost, high performance computing platforms have made AI approaches sufficiently scalable to be applied in high volume, commercial applications. The true promise of AI in modeling human intelligence remains elusive. Current approaches can simulate a small subset of the many processes that make up human cognition, and yet it would be of huge benefit to be able to integrate expert human decision making in AI applications. In this paper, we present a pragmatic approach that can be used to capture expert human decision making within a limited domain of expertise. We propose an approach that automates the Analytic Hierarchy Process in order to capture a model of expert decision making from observational data. While this is not a general solution, it provides a workable approach for AI applications dealing with well defined, limited domains of knowledge.

Citation Download Citation

Hesham Fouad, Ranjeev Mittu, and Derek Brock "Contingent attention management in multitasked environments", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100620 (10 May 2019); https://doi.org/10.1117/12.2524979

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