Abstract
The ability to perform a detailed decision-making approach based on large quantities of parameters and data is at the core of the majority of sciences. Traditionally, all possible scenarios should be considered, and their outcomes assessed via a logical and systematic manner to obtain accurate and applicable methods for knowledge discovery. However, such approach is typically associated with high computational complexity. Moreover, it is non-trivial for researchers to develop and train models with deep and complex model structures with potentially large number of parameters. However, there are compelling evidence from psychology and cognitive research that intuition plays an important role in the process of intelligence extraction and the decision-making process. More specifically, by using intuitive models, a system is able to take subsets from networks and pass them through a process to determine relationship that can be used to predict future decision without a deep understanding of a scenario and its corresponding parameters. When an artificial agent manifests human intuition properties, then we can describe this as artificial intuition. In this article, we discuss some requirements of artificial intuition and present a model of artificial intuition that utilises semantic networks to improve a decision system.
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Johnny, O., Trovati, M., Ray, J. (2020). Towards a Computational Model of Artificial Intuition and Decision Making. In: Barolli, L., Nishino, H., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2019. Advances in Intelligent Systems and Computing, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-29035-1_45
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