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Mapping and Generating Adaptive Ontology of Decision Experiences

Published: 20 April 2020 Publication History

Abstract

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.

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  • (2024)A New AI Approach by Acquisition of Characteristics in Human Decision-Making ProcessApplied Sciences10.3390/app1413546914:13(5469)Online publication date: 24-Jun-2024

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    ICISS '20: Proceedings of the 3rd International Conference on Information Science and Systems
    March 2020
    238 pages
    ISBN:9781450377256
    DOI:10.1145/3388176
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    • University of Salford: University of Salford
    • Cardiff University: Cardiff University
    • Kingston University: Kingston University

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    Published: 20 April 2020

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    Author Tags

    1. Decision
    2. Decision-making
    3. Ontology
    4. Representation of uncertainty

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    • (2024)A New AI Approach by Acquisition of Characteristics in Human Decision-Making ProcessApplied Sciences10.3390/app1413546914:13(5469)Online publication date: 24-Jun-2024

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