• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.137-140
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• 2023

자연어처리는 인공지능 발전과 함께 주목받는 분야로 컴퓨터가 인간의 언어를 이해하게 하는 기술이다. 그러나 많은 인공지능 모델은 블랙박스처럼 동작하여 그 원리를 해석하거나 이해하기 힘들다는 문제점이 있다. 이 문제를 해결하기 위해 설명 가능한 인공지능의 중요성이 강조되고 있으며, 활발히 연구되고 있다. 연구 초기에는 모델의 예측에 큰 영향을 끼치는 단어나 절을 근거로 추출했지만 문제 해결을 위한 단서 수준에 그쳤으며, 이후 문장 단위의 근거로 확장된 연구가 수행되었다. 하지만 문서 내에 서로 떨어져 있는 근거 문장 사이에 누락된 문맥 정보로 인하여 이해에 어려움을 줄 수 있다. 따라서 본 논문에서는 사람에게 보다 이해하기 쉬운 근거를 제공하기 위한 생성형 기반의 근거 추론 연구를 수행하고자 한다. 높은 수준의 자연어 이해 능력이 필요한 문서 요약 데이터셋을 활용하여 근거를 생성하고자 하며, 실험을 통해 일부 기계독해 데이터 샘플에서 예측에 대한 적절한 근거를 제공하는 것을 확인했다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10a
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• pp.231-233
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• 2000

인터넷에 산재해 있는 분산 이질의 대규모 정보들을 통합, 관리하기 위한 기본 모델로서 최근 정보교환 표준으로 인정받고 있는 XML을 사용할 수 있다. HMS(Hallym Mediator System)는 XML을 기본 데이터 모델로 하는 미디에이터 시스템으로서 뷰(View) DTD의 정의, 추론 기능을 이용하여 웹 상의 분산, 이질 정보의 통합 기능을 제공한다. 뷰 DTD는 관리자에 의하여 정의되어, 추론 및 보완 과정을 거쳐 생성되며, 웹 상의 통합된 가상 정보 구조를 표현한다. 웹 상의 사용자는 이와 같이 생성된 뷰 DTD를 근거로 분산이질 정보에 대한 구조정보 및 내용정보에 대한 질의를 수행할 수 있다. HMS에서는 DTD 구동형 비주얼 사용자 인터페이스를 제공하여, 관리자와 일반 사용자에게 직관적이고 간편한 웹 정보 브라우징 및 질의검색 환경을 제공한다.

• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.440-442
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• 1999

본 연구에서는 전자연동장치의 확장성과 신뢰성 제고를 위해 전자연동장치의 실시간 연동전략으로 활용될 수 있는 연동 지식베이스를 자동 생성, 구축할 수 있는 지능형 연동지식베이스 자동 구축 소프트웨어(IIKBAGS)를 개발한다. IIKBAGS의 추론부는 주어진 역 모델의 동적탐색하에서 휴리스틱 규칙들의 우선순위에 따라 모든 진로를 탐색함은 물론 각 진로들에 대해 진로상 신호설비들간의 연쇄관계를 확인하여 연동패턴들을 자동생성하는 연동지식 자동생성기능을 가진다. 지식베이스는 전자연동장치상의 실시간 전문가 시스템이 직접적으로 활용할 수 있는 구조로 설계됨으로써 연동도표 입력과정에서 발생할 수 있는 오류를 배제, 연동장치의 정확성과 신뢰성을 높인다.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1034-1041
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• 2014

This paper propose a fuzzy inference model for obstacle avoidance for a mobile robot with an active camera, which is intelligently searching the goal location in unknown environments using command fusion, based on situational command using an vision sensor. Instead of using "physical sensor fusion" method which generates the trajectory of a robot based upon the environment model and sensory data. In this paper, "command fusion" method is used to govern the robot motions. The navigation strategy is based on the combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance. We describe experimental results obtained with the proposed method that demonstrate successful navigation using real vision data.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.732-748
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• 2009

The technology of Semantic Web realizes the base technology for context-awareness that creates new services by dynamically and flexibly combining various resources (people, concepts, etc). According to the realization of ubiquitous computing technology, many researchers are currently working for the embodiment of web service. However, most studies of them bring about the only predefined results those are limited to the initial description by service designer. In this paper, we propose a new service supporting model to provide an automatic method for plan related tasks which achieve goal state from initial state. The inputs on an planner are intial and goal descriptions which are mapped to the current situation and to the user request respectively. The idea of the method is to infer context from world model by DL-based ontology reasoning using OWL domain ontology. The context guide services to be loaded into planner. Then, the planner searches and plans at least one service to satisfy the goal state from initial state. This is STRIPS-style backward planner, and combine OWL-S services based on AI planning theory that enabling reduced search scope of huge web-service space. Also, when feasible service do not find using pattern matching, we give user alternative services through DL-based semantic searching. The experimental result demonstrates a new possibility for realizing dynamic service modeler, compared to OWLS-XPlan, which has been known as an effective application for service composition.

• Cartoon and Animation Studies
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• s.26
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• pp.31-56
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• 2012

This is a scientific study of subjectivity on recognition types and preferences of animation audience. Therefore, Q-methodology was used as a methodology of this study. As a result, it was possible to find the various characteristics according to the preferences of audience on animation genres. The features of each type were classified into 3 types. First was an artistic and literary value-oriented type that explored storytelling and descriptive value. Second was a pictorial and aesthetic value-oriented type that explored visual beauty such as characters and Mise en scene. Third was a visual and entertainment value-oriented type that explored entertainment factors and attractions. Thus, according to the classification of preference types, it was inferred that digital image generation audience with an active preference that can create and consume images have developed a tendency that independently consumed their preferences based on thoroughly personal or subjective preferences rather than classified and accepted their preferred culture as their symbolic expressions of hierarchical position. In this aspect, Q-methodological consideration that is useful to analyze the subjectivity of audience could be a significant work. Nevertheless, this study alone could not reveal various generative contexts of specific preferences. It seems to have more significant results by applying various qualitative or quantitative methodologies together with this study and analyzing recognition types and preferences of audience.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.