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- W3036700482 abstract "Abstract Artificial intelligence (AI) is being applied successfully in a number of domains. Advances in low-cost, high-performance computing platforms have made AI approaches sufficiently scalable so that they can be applied in high-volume, commercial applications. The true promise of AI in modeling human intelligence, however, remains elusive. Current approaches can simulate a small subset of the many processes that make up human cognition, and yet it would be a huge benefit to be able to integrate expert human decision-making in AI applications. In this chapter, 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 collected using subject matter experts. In addition, we present a novel approach for validating our model by measuring the degree of consistency across subjects. While this is not a general solution, it provides a workable approach for AI applications dealing with well defined, limited domains of knowledge. In the future, we hope to extend our model to human-machine teams and shared context." @default.
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- W3036700482 date "2020-01-01" @default.
- W3036700482 modified "2023-09-23" @default.
- W3036700482 title "Integrating expert human decision-making in artificial intelligence applications" @default.
- W3036700482 doi "https://doi.org/10.1016/b978-0-12-820543-3.00013-4" @default.
- W3036700482 hasPublicationYear "2020" @default.
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