Biological Systems Modeling Approach to Bio-intelligent Decision Making: Knowledge Engineering Perspective

Background and Purpose: Knowledge engineering (KE) encompasses knowledge modeling to design a knowledge-based system (KBS) which in turn entails computational intelligence (CI) and physical intelligence (PI). KE includes knowledge management systems (KMS), executive control network (ECN), decision-making (DM), lateral thinking (LT), problem-solving, creativity, etc. The main purpose is to assess the human’s measurable potential, analyze big data, and then design a decision-making model required for KE. Big data analysis was based on CI-PI coupling. Method: Data were yielded from ۱۶۱ participants administered in the eight different sectors in Iran. The researcher used the mixed-methods sampling strategy in which a non-probability quantitative sampling was used. The researcher gathered data using (non)-cognitive, physical, and hematology tests. Analyses were performed using SPSS software. The method of analysis was statistical, interpretive, and meta-inferential. The interaction between the most important regions in the brain was modeled to simulate the DM process. Findings: Participants’ decision-making tilted toward linear/automatic rather than nonlinear/control ones since they liked to retrieve previous information rather than being confronted with a new decision. Lateral thinking showed a low mean of ۲۳.۶۸%. The inclination of the participants in linear/automatic decisions might be influenced by people (parameter). Considering their prospects, their decisions were based on Risk/Loss Aversion, and this might influence the participants to go with the tide. The executive function index was mediocre which calls forth reverse engineering of biological intelligence. KE and AI are suggested to copy and act in the world as a brain does to be suitable for knowledge modeling, closed-loop system of DM, and AI-powered knowledge management systems.

کلیدواژه ها:

Knowledge engineering ، decision-making modeling ، knowledge modeling