• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.06a
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• pp.377-381
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• 2001

An ATMS(Assumption-based Truth Maintenance System) has been widely used for maintaining the truth of information by detecting and solving contradictions in nile-based systems. But the ATMS can not correctly maintain the truth of the information in case that the generated information is satisfied within a time interval or includes data about temporal relations of events in time varying situations, because it has no mechanism manipulating temporal data. In this paper, The extended ATMS method is proposed, which can maintain the truth of the information in the inference system using information changing over time or temporal relations of events. In order to maintain contexts generated by relations of events, the label representation method is modified, the disjunction, conjunction simplification method in the label-propagation procedure and nogood handling method of the conventional ATMS are modified, too.

• Journal of Information Technology Applications and Management
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• v.10 no.4
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• pp.119-133
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• 2003

맞춤형 금융상품 설계시스템(Financial Product Factory System)이란 온라인으로 접근하는 고객의 요구사항을 고려하여 고객에게 가장 적합한 금융상품을 실시간으로 설계하여 제공하는 시스템이다. 최근 들어 인터넷 뱅킹 고객의 수가 급증함에 따라 맞춤형 금융상품 설계시스템의 필요성이 대두되고 있으나, 이러한 시스템의 정의나 성격, 필요 기능, 구축 방안에 대한 연구가 되어 있지 않은 실정이다. 본 연구에서는 맞춤형 금융상품 설계시스템의 정의를 내리고, 이 시스템이 갖추어야 할 요구사항을 시스템과 서비스 측면에서 분석한 후, 이 요구사항을 반영하는 시스템의 아키텍처를 제안ㆍ구현한다

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.81-83
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• 1998

ATMS(Assumption-based Truth Maintenance System)는 추론기관의 추론 과정을 기억하고 각 추론 상태의 진위를 관리해주는 기능을 수행한다. ATMS는 JTMS나 LTMS와는 다르게 각 노드의 레이블과 Nogood들을 관리함으로써, 추론기관의 추론에 모순(Contradiction)이 발생하였을 때 이를 효과적으로 처리해준다. 기존의 ATMS는 모순에 영향을 주는 가정(Assumption)을 제거(Retract)함으로써 모순에 영향을 주는 원인을 제거하는 방식을 취하고 있다. 그러나, 본 논문에서는 이와 같은 방식으로 문제가 해결되지 못하는 새로운 종류의 모순을 설명하고 이를 처리하기 위해서는 ATMS가 추론기관과 연동하여 모순을 처리하는 방식에 대해서 서술하고자한다.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.401-410
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• 2009

In order to derive hidden information (concept subsumption, concept satisfiability and realization) of OWL ontology, a number of OWL reasoners have been introduced. The most of these ontology reasoners were implemented using the tableau algorithm. However most reasoners simply report this information without providing a justification for any arbitrary entailment and unsatisfiable concept derived from OWL ontologies. The purpose of this paper is to investigate an optimized method for non-deterministic rule of the tableau algorithm and finding axioms to cause inconsistency in ontology. In this paper, therefore, we propose an optimized method for non-deterministic rule and finding axiom to cause inconsistency using NATMS. In the first place, we introduce Dependency Directed Backtracking to deal non-deterministic rule, a tableau-based decision procedure to find unsatisfiable axiom Furthermore we propose an improved method adapting NATMS.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.191-194
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• 2003

Product factory is a real-time financial product design system for the Internet customers. Recently, as the number of the Internet customers increases, the importance of the product factory becomes more emphasized. However, there is not much research performed regarding its definition, properties, requirements, nor implementation. In this research, we make a clear definition of product factory, and analyze the requirements of the system from the perspectives of functions and services, and we propose an architecture that reflects the analyzed requirements. In additions, we implemented a prototypical system based on the proposed architecture to prove the usefulness of this research.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.805-812
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• 1989

An efficient diagnostic approach for real-time operation aiding expert system in chemical process plants is discussed. The approach is based on the hybrid of the simplified symptom tree(SST) and the fault consequence digraph(FCD), representation of propagation patterns of fault states. The SST generates fault hypothesis efficiently and the FCD resolve the real fault accurately. Frame based knowledge representation and object-oriented programming make diagnostic system general and efficient. Truth maintenance system enables robust pattern matching and provides enhanced explain facilities. A prototype expert system for supports operation of naphtha furnaces process, called OASYS, has been built and tested to demonstrate this methodology. Utilization of diversified process symbolic data, produced using dynamic normal standards, overcomes the problem of qualitative Boolean reasoning and enhance the applicability.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.161-175
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• 2001

In this paper, a detection method of contradiction between input informations is proposed when the inference is processed in rule-based systems. The proposed method is accomplished by improving the label representation and the label management scheme in a conventional ATMS(Assumption-based Truth Maintenance System). The Proposed method also can represent and process input informations having uncertainty values.

• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.197-211
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• 2014

An investigation of tunnel linings is performed at two tunnels in the US using complimentary noncontact techniques: air-coupled ground penetrating radar (GPR), and a vehicle-mounted scanning system (SPACETEC) that combines laser, visual, and infrared thermography scanning methods. This paper shows that a combination of such techniques can maximize inspection coverage in a comprehensive and efficient manner. Since ground-truth is typically not available in public tunnel field evaluations, the noncontact techniques used are compared with two reliable in-depth contact nondestructive testing methods: ground-coupled GPR and ultrasonic tomography. The noncontact techniques are used to identify and locate the reinforcement mesh, structural steel ribs, internal layer interfaces, shallow delamination, and tile debonding. It is shown that this combination of methods can be used synergistically to provide tunnel owners with a comprehensive and efficient approach for monitoring tunnel lining conditions.

• Journal of KIISE
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• v.45 no.2
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• pp.113-125
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• 2018

Recently, with the advent of the Big Data era, we have gained the capability of acquiring vast amounts of knowledge from various fields. The collected knowledge is expressed by well-formed formula and in particular, OWL, a standard language of ontology, is a typical form of well-formed formula. The symbolic reasoning is actively being studied using large amounts of ontology data for extracting intrinsic information. However, most studies of this reasoning support the restricted rule expression based on Description Logic and they have limited applicability to the real world. Moreover, knowledge management for inaccurate information is required, since knowledge inferred from the wrong information will also generate more incorrect information based on the dependencies between the inference rules. Therefore, this paper suggests that the SWAT, knowledge management system should be combined with the SWRL (Semantic Web Rule Language) reasoning based on ATMS (Assumption-based Truth Maintenance System). Moreover, this system was constructed by combining with SWRL reasoning and ATMS for managing large ontology data based on the distributed In-memory framework. Based on this, the ATMS monitoring system allows users to easily detect and correct wrong knowledge. We used the LUBM (Lehigh University Benchmark) dataset for evaluating the suggested method which is managing the knowledge through the retraction of the wrong SWRL inference data on large data.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.