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Mapping and Generating Adaptive Ontology of Decision Experiences
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.ORCID iD: 0000-0003-4327-117x
2020 (English)In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2020, p. 138-143Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2020 ACM.

Place, publisher, year, edition, pages
Association for Computing Machinery , 2020. p. 138-143
Keywords [en]
Decision, Decision-making, Ontology, Representation of uncertainty, Behavioral research, Mapping, One dimensional, Daily lives, Decision making process, Decision-making systems, Finite number, Making decision, Decision making
National Category
Computer Sciences Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:bth-20558DOI: 10.1145/3388176.3388200Scopus ID: 2-s2.0-85092161672ISBN: 9781450377256 (print)OAI: oai:DiVA.org:bth-20558DiVA, id: diva2:1477154
Conference
3rd International Conference on Information Science and System, ICISS 2020, Virtual, Online, United Kingdom, 19 March 2020 through 22 March 2020
Available from: 2020-10-16 Created: 2020-10-16 Last updated: 2020-10-16Bibliographically approved

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Zhou, YuanKhatibi, Siamak

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