TY - GEN
T1 - Towards a computational model of artificial intuition and decision making
AU - Johnny, Olayinka
AU - Trovati, Marcello
AU - Ray, Jeffrey
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2019/8/15
Y1 - 2019/8/15
N2 - 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.
AB - 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.
KW - Intelligent Networks
KW - Collaborative Systems
KW - Big Data and Cloud Computing
KW - Data Intelligent Systems
KW - Data Mining
KW - Knowledge Management
KW - Fuzzy Systems
KW - INCoS-2019
KW - INCoS
UR - https://www.scopus.com/pages/publications/85071496941
UR - https://www.scopus.com/inward/citedby.url?scp=85071496941&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29035-1_45
DO - 10.1007/978-3-030-29035-1_45
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85071496941
SN - 9783030290344
T3 - Advances in Intelligent Systems and Computing
SP - 463
EP - 472
BT - Advances in Intelligent Networking and Collaborative Systems - The 11th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2019
A2 - Barolli, Leonard
A2 - Nishino, Hiroaki
A2 - Miwa, Hiroyoshi
PB - Springer Verlag
T2 - 11th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2019
Y2 - 5 September 2019 through 7 September 2019
ER -