Mapping and Generating Adaptive Ontology of Decision Experiences
Yuan Zhou, Siamak Khatibi · 2020
Decision-making is shared by many disciplines. In computer science decision-making systems aim to substitute or support people for making decisions. The systems generally need to acquire as many as possible data to provide possible options for any decision-making. The possible options are usually obtained by modeling situations data. However, situation data is becoming tremendous along with daily life changes and it is becoming more and more difficult to model and restore those situation data. However as human, when the situation data is lacking, we still can make appropriate decisions based on our "decision experiences". To learn how decisions are made adaptively by humans, this paper propose a method to characterize a decision-making process for a finite number of people only based on individual's actions without modeling any situation data. Then the characterization problem is formulated as a one-dimensional decision-making process and experimented as a number guessing game. The experimental results show the feasibility of the proposed method in mapping and generation of an adaptive ontology structure of decision experiences for experimental participants.