• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.249-254
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• 2009

The today's decision making tasks in globalized business and manufacturing become more complex, and ill-defined, and typically multiaspect or multi-discipline due to many influencing factors. The requirement of obtaining fast and reliable decision solutions further complicates the task. Intelligent decision support system (DSS) currently exhibit wide spread applications in business and manufacturing because of its ability to treat ill-structuredness and vagueness associated with complex decision making problems. For multi-dimensional decision problems, generally an optimum single DSS can be developed. However, with an increasing number of influencing dimensions, increasing number of their factors and relationships, complexity of such a system exponentially grows. As a result, software development and maintenance of an optimum DSS becomes cumbersome and is often practically unfeasible for real situations. This paper presents a technically feasible approximation of an optimum DSS through decreasing its complexity by a modular structure. It consists of multiple DSSs, each of which contains the homogenous knowledge's, decision making tools and possibly expertise's pertaining to a certain decision making dimension. Simple, efficient and practical integration mechanism is introduced for integrating the individual DSSs within the proposed overall DSS architecture.

• 인터넷정보학회논문지
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• 제25권4호
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• 정보처리학회논문지D
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• 제18D권5호
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• pp.313-316
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• 2011

다차원 데이터 집합에서 서로 지배되지 않는 데이터로 구성된 부분 집합을 스카이라인이라고 한다. 스카이라인 계산은 다차원 데이터를 대상으로 한 의사결정에 유용한 연산이다. 그러나 스카이라인이 지나치게 큰 경우 이를 의사결정에 활용하기 어려울 수 있다. 본 연구에서는 사용자가 제시하는 원점의 이동, 원점으로부터의 각도와 거리 정보를 반영하여 스카이라인의 일부를 효율적으로 구하는 방법을 모색하였다. 제안한 알고리즘은 스카이라인에 속하지 않는 데이터를 신속하게 제거해가며, 사용자의 요구를 점진적으로 반영할 수 있다는 특징을 갖는다. 알고리즘의 효율성은 실험을 통해 검증하였다.

• 경영정보학연구
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• 제25권1호
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• pp.47-73
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• 2023

본 연구는 인공지능 기법 중 다중레이블 속성선택 방법을 적용하여 복잡한 경영환경에서 의사결정의 효과성을 증대시키는 방안을 설명한다. 인공지능 기반의 의사결정 시스템은 의사결정자의 선택과 판단을 돕거나, 대신하는 중요한 역할을 한다. 더욱이 최근 인공지능을 중심으로 한 비즈니스 의사결정은 기업의 성장 동력으로 평가받는데, 이를 위해서는 효과적인 의사결정 방법이 수반되어야 한다. 이에 본 연구는 의미 있는 속성값을 선별하는 CFS-BR(이진연관성 접근 기반의 상관관계 속성선택 모델)을 제안하여, 효과적인 의사결정을 지원하는 것을 돕는다. 예시데이터와 실증데이터의 분석 결과, CFS-BR은 유의미한 속성을 최상우선선별 알고리즘 기반으로 최상의 조합을 선별하므로 효율적 의사결정을 지원할 수 있고, 기존의 다중 레이블 속성선택 방법과 비교하였을 때 정확도가 높은 것으로 보아 효과적인 의사결정을 증대시키는 데 유용하다.

• 한국환경과학회지
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• 제32권8호
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• pp.585-593
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• 2023

This study promotes the understanding of landscape technicians by, assessing the professional qualification system that aligns with the needs of the 21st-century environment, distinct from the industrialization era, It, provides basic theoretical insights into the multi-dimensional connections between the motivation for a certificate and the career decision-making self-efficacy of individuals with a demand for the certificate in the structural aspect. The collected data underwent a comprehensive analysis involving frequency assessments, confirmatory factor analysis, descriptive statistics, reliability tests, and correlation analyses. The study found differences according to particpants' diverse sociodemographic characteristics including gender, place of residence, educational background, and occupation. The motivation for obtaining a certificate had significant positive effects on their career-decision-making self-efficacy, within the context of structural relations. The study findings on the relations between motivation for obtaining a certificate and career decision-making self-efficacy demonstrate that the direction and intensity of efforts to obtain a certificate can increase the career decision-making self-efficacy of people hoping to become landscape technicians.

• Advances in robotics research
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• 제1권2호
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• pp.171-183
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• 2014

Distributed planning and decision making can be beneficial from the robustness, adaptability and fault tolerance in multi-robot systems. Distributed mechanisms have not been employed in three dimensional transportation systems namely aerial and underwater environments. This paper presents a distributed cooperation mechanism on multi robot transportation problem in three dimensional environments. The cooperation mechanism is based on artificial capital market, a newly introduced market based negotiation protocol. In the proposed mechanism contributing in transportation task is defined as asset. Each robot is considered as an investor who decides if he is going to invest on some assets. The decision is made based on environmental constraint including fuel limitation and distances those are modeled as capital and cost. Simulations show effectiveness of the algorithm in terms of robustness, speed and adaptability.

• 품질경영학회지
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• 제42권2호
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• pp.131-143
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• 2014

Purpose: This paper discusses the intrinsic assumption of one-dimensional relationship between the upper and lower levels in AHP(Analytic Hierarchy Process) for the Preliminary Feasibility Study of National R&D Program. This assumptions has not been questioned in academia and industry so far. Methods: This discussion is induced by understanding the Theory of Attractive Quality (Kano et al. 1984) and explains the limitation of AHP in the preliminary feasibility study of national R&D program. Results: In this paper, we propose a new questioning method based on two dimensional perspective, which is named as 2D-AHP (two dimensional AHP), to overcome the limitation. The main idea stems from the observation that the relationship between the upper and lower levels in AHP can vary depending on the subject of R&D. Conclusion: The two dimensional perspective pointed out in this paper should be more deeply studied in the field of MCDM(multi-criteria decision making) since it can be applied to the more general problems in human decision making.

• 대한교통학회지
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• 제32권5호
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• pp.544-553
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• 2014

활동기반 모형은 개개인의 다양한 일상 활동을 교통계획의 의사결정단위로 파악하고, 인간의 활동으로 인해 파생된 통행을 분석한다. 즉, 동일한 활동패턴 집단의 유형의 사회경제적, 상황적 특성에 따라 어떤 활동을 수행할 것인지를 결정하고 활동수행주체의 활동시간, 공간의 이동, 교통수단을 선택하는 행위에 대한 예측을 수행한다. 통행은 개인과 다른 사람들이 참여하여 이루어지는 복합적인 의사결정 과정으로 간주되기 때문에 이를 통한 활동기반모형은 교통수요 예측에 있어 보다 효율적이면서 현실에 부합한 수요추정을 수행 할 수 있다. 이러한 과정에서 개인의 하루 활동유형을 선택하는 과정은 매우 중요하며, 이에 따라 활동기반 모형의 통행이 발생된다고 할 수 있다. 본 연구에서는 개인의 활동유형 선택에 영향을 주는 요인을 파악하고, 개개인의 특성에 따라 활동유형 선택행태를 분석하였다. 먼저 통행사슬 유형과 활동목적을 검토하여 활동유형을 분류하고, 활동유형선택에 영향을 미치는 개개인의 사회경제적 특성변수를 도출하였다. 다음으로 활동유형별 다항 로지스틱 회귀분석 모형을 구축하여, 활동수행주체의 복합적 특성에 따라 선호되는 활동패턴에 대한 분석을 수행하였다. 결론적으로 활동유형의 선택은 활동수행주체의 복합적인 특성에 의존하며, 장래 활동기반 교통수요 예측에 있어 개인의 복합적인 특성이 활동기반 모형의 활동스케줄 결정 과정에서 고려되어야 함을 시사하고 있다.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.496-500
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• 2015

International construction projects are inherently more risky than domestic projects with multi-dimensional uncertainties that require complementary risk management at both the country and project levels. However, despite a growing need for systematic country evaluations, most studies have focused on project-level decisions and lack country-based approaches for firms in the construction industry. Accordingly, this study suggests data-driven approaches for evaluating countries using two quantitative models. The first is a two-stage country segmentation model that not only screens negative countries based on country attractiveness (macro-segmentation) but also identifies promising countries based on the level of past project performance in a given country (micro-segmentation). The second is a multi-criteria country segmentation model that combines a firm's business objective with the country evaluation process based on Kraljic's matrix and fuzzy preference relations (FPR). These models utilize not only secondary data from internationally reputable institutions but also performance data on Korean firms from 1990 to 2014 to evaluate 29 countries. The proposed approaches enable firms to enhance their decision-making capacity for evaluating and selecting countries at the early stage of corporate strategy development.

• 한국측량학회지
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• 제30권3호
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• pp.287-294
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• 2012

가축전염병 중 하나인 구제역의 경우 정보의 초동 대응 미흡 등으로 전국적으로 확산되는 피해를 초래하였다. 이를 해결하기 위해 국가에서는 가축이력에 대한 체계적인 관리를 마련하기 위하여 데이터를 구축하였으며, 2002년도부터 추진되어 현재 웹 기반의 가축전염병 발생 통계 시스템(AIMS)이 운영하고 있다. 이는 사용자가 원하는 기간에 해당 축종의 형태에 따른 질병을 선택하면 지역별로 가축전염병 발생 통계 현황이 텍스트 기반으로 제공하고 있다. 하지만 이 같은 경우 시각적으로 공간적인 위치정보를 즉각적으로 파악할 수 없기 때문에 정보를 효과적으로 전달하기 어렵고, 의사결정을 내리고자 할 때 사용자가 원하는 정보를 다차원적으로 지원하지 못하는 한계가 있다. 이 연구에서는 오픈소스 기반의 SOLAP(Spatial On-Line Analytical Processing) 기술을 적용하여 여러 형태의 데이터를 다각적인 방법으로 분석하고, 최종적으로 나온 결과를 공간정보와 통합하여 지도상에 시각적으로 전달되도록 표현하였다.