• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.4
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• pp.287-295
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• 2022

As low altitude long-range artillery threat has been strengthened, the development of anti-artillery interception system to protect assets against its attacks will be kicked off. We view the defense of long-range artillery attacks as a typical dynamic weapon target assignment (DWTA) problem. DWTA is a sequential decision process in which decision making under future uncertain attacks affects the subsequent decision processes and its results. These are typical characteristics of Markov decision process (MDP) model. We formulate the problem as a MDP model to examine the assignment policy for the defender. The proximity of the capital of South Korea to North Korea border limits the computation time for its solution to a few second. Within the allowed time interval, it is impossible to compute the exact optimal solution. We apply approximate dynamic programming (ADP) approach to check if ADP approach solve the MDP model within processing time limit. We employ Shoot-Shoot-Look policy as a baseline strategy and compare it with ADP approach for three scenarios. Simulation results show that ADP approach provide better solution than the baseline strategy.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.83-102
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• 2008

Ubiquitous computing requires timely supply of contextual information in order to upgrade decision quality. In this sense, this study is aimed at proposing a multi-dynamic decision support system for highly ill-structured problems. Especially, it is very important for decision makers in the ubiquitous computing to coordinate conflicts among local goals and global goal harmoniously. The proposed Multi-Dynamic Decision Support System (MDDSS) is basically composed of both central structure and distributed structure, in which central structure supports multi objects decision making and distributed structure supports individual decision making. Its hybrid architecture consists of decision processor, multi-agent controller and intelligent knowledge management processor. Decision processor provides decision support using contexts which come from individual agents. Multi-agent controller coordinates tension among multi agents to resolve conflicts among them. Meanwhile, intelligent knowledge management processor manages knowledge to support decision making such as rules, knowledge, cases and so on. To prove the validity of the proposed MDDSS, we applied it to an u-fulfillment problem system in which many kinds of decision makers exist trying to satisfy their own objectives, and timely adjustment of action strategy is required. Therefore, the u-fulfillment problem is a highly ill-structured problem. We proved its effectiveness with the aid of multi-agent simulation comprising 60 customers and 10 vehicles under three experimental modes.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.327-331
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• 2009

In fuzzy logic, aggregating uncertainties is generally achieved by means of operators such as t-norms and t-conorms. However, existing aggregation operators have some disadvantages as follows : First, they are situation-independent. Thus, they may not be properly applied to dynamic aggregation process. Second, they do not give an intuitional sense to decision making process. To solve these problems, we propose a new $\varepsilon$-AMDA (Aggregation based on the fuzzy Multidimensional Decision Analysis) algorithm to reflect degrees of strength for option i (i = 1, 2, ..., n) in the decision making process. The $\varepsilon$-AMDA algorithm makes adaptive aggregation results between min (the most weakness for an option) and max (the most strength for an option) according to the values of the parameter representing degrees of strength for an option. In this respect, it may be applied to dynamic aggregation process. In addition, it provides a mechanism of the fuzzy multidimensional decision analysis for decision making, and gives an intuitional sense to decision making process. Thus, the proposed method aids the decision maker to get a suitable decision according to the degrees of strength for options (or alternatives).

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.378-378
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• 2017

본 연구는 도시에서 발생하는 홍수에 대응하기 위해서 홍수취약요인을 구성하고 홍수 위험성을 기반으로 취약지역과 정도를 도출하는 동적의사결정 모형 구성을 목표로 한다. 취약 요인은 인명피해에 초점을 맞추었으며 발생 가능한 홍수의 규모에 따른 취약 요인의 대응역량 등을 반영하여 동적의사결정 모형을 구성하고자 한다. 홍수위험성 산정에는 예상되는 홍수 시나리오를 반영한 SWMM 모델링 결과를 이용하였으며, 취약요인은 델파이기법으로 구성하였다. 구성한 모형은 빈번하게 내수침수가 발생한 지역인 도림천 유역을 대상으로 적용성을 검토하였다. 수립된 모형은 홍수 위험성의 정도에 대하여 발생 가능한 인명피해 지역을 공간적으로 파악할 수 있도록 하며 인명피해 예상 수치를 제공할 수 있다.

• Journal of KIISE
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• v.43 no.7
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• pp.801-811
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• 2016

Self-adaptive systems (SASs) can change their goals and behaviors to achieve its ultimate goal in a dynamic execution environment. Existing approaches have designed, at the design time, utility functions to evaluate and predict the goal satisfaction, and set policies that are crucial to achieve each goal. The systems can be adapted to various runtime environments by utilizing the pre-defined utility functions and policies. These approaches, however, may or may not guarantee the proper adaptability, because system designers cannot assume and predict all system environment perfectly at the design time. To cope with this problem, this paper proposes a new method of dynamic decision making, which takes the following steps: firstly we design a Dynamic Decision Network (DDN) with environmental data and goal model that reflect system contexts; secondly, the goal satisfaction is evaluated and predicted with the designed DDN and real-time environmental information. We furthermore propose a dynamic reflection method that changes the model by using newly generated data in real-time. The proposed method was actually applied to ROBOCODE, and verified its effectiveness by comparing to conventional static decision making.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.37-44
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• 2014

In this paper, we propose a NPC decision making model based on Prospect Theory which tries to model real-life choice, rather than optimal decision. For this purpose, we analyse the problems of reference point setting, diminishing sensitivity and loss aversion which are known as limitations of the utility theory and then apply these characteristics into the decision making in game. Dynamic Terrain Analysis is utilized to evaluate the proposed model and experimental result shows the method have effects on inducing diverse personality and emergent behavior on NPC.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1227-1234
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• 2006

최근 의료 서비스의 질적 향상을 위해 지식 기반의 의사결정지원 시스템 (Decision Support System)의 도입이 지속적으로 이루어지고 있으며, 이의 대표적 예로 임상실행지침(CPG : Clinical Practice Guideline) 중심의 진료 시스템이 있다. 임상실행지침은 환자가 병원에서 거치는 프로세스를 표현한 것으로, 질환에 대한 환자의 표준화된 진료 프로세스 지식이다. 본 연구에서는 임상실행지침, 의료 지식, 환자의 실시간 데이터를 연결시켜 환자가 병원에서 받아야할 진료 과정을 동적으로 생성하는 의사결정지원 시스템을 제시한다. 본 시스템은 임상실행지침과 의료지식을 바탕으로 추상화된 진료 프로세스 템플릿을 생성하고, 이 템플릿의 인스턴스에 해당하는 환자의 실시간 데이터를 반영하여 이후의 진료 프로세스를 동적으로 생성한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.723-726
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• 2007

국내의 응급의료체계는 119구급대와 1339 응급의료 정보센터간의 이원화된 체계로 구축되어 있다. 하지만 두 기관간의 긴밀한 연계체계의 부족으로 응급의료기관이나 의료기관으로의 이송된 자 중 이송의료 기관 선정이 부적절한 경우가 상대적으로 높게 된다. 또한 각각의 환경에 맞게 구축된 병원 정보시스템의 이질성으로 응급환자의 효율적인 이송체계 수립이 어려운 실정이다. 멀티 에이전트의 자율적이며 독립적은 성향은 이질적인 병원 정보 시스템에서 효과적으로 상호운용할 수 있는 가능성을 높여주며 점점 복잡해지는 응급 의료 상황에 대하여 동적으로 행동할 수 있게 해준다. 따라서 본 논문에서는 응급환자의 정보를 통해 실시간으로 응급환자에게 가장 적절한 후보 병원을 결정하여 동적으로 응급환자의 병원 이송체계를 수립할 수 있는 의사결정 지원 시스템을 제안한다.

• Proceedings of the Korean Society of Rural Planning Conference
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• 1997.03a
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• pp.30-32
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• 1997

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.43-61
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• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.