• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.19-29
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• 2007

This paper introduces a robot knowledge framework which is represented with multiple classes, levels and layers to implement robot intelligence at real environment for mobile robot. Our root knowledge framework consists of four classes of knowledge (KClass), axioms, rules, a hierarchy of three knowledge levels (KLevel) and three ontology layers (OLayer). Four KClasses including perception, model, activity and context class. One type of rules are used in a way of unidirectional reasoning. And, the other types of rules are used in a way of bi-directional reasoning. The robot knowledge framework enable a robot to integrate robot knowledge from levels of its own sensor data and primitive behaviors to levels of symbolic data and contextual information regardless of class of knowledge. With the integrated knowledge, a robot can have any queries not only through unidirectional reasoning between two adjacent layers but also through bidirectional reasoning among several layers even with uncertain and partial information. To verify our robot knowledge framework, several experiments are successfully performed for object recognition and navigation.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.241-245
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• 1989

A knowledge based expert system is a computer program that emulates the reasoning process of a human expert in a specific problem domain. This paper presents an expert system to diagnose the various faults in power system. The developed expert system is represented considering two points; the possibility of solution and the fast processing speed. As uncertainties exist in the facts and rules which comprise the knowledge base of the expert system, Certainty Factor, which is based on the confirmation theory is used for the inexact reasoning. Also, as the diagnosis problem requires the inductive reasoning process in nature, the solution is imperfect and not unique in general. So the expert system is designed to generate all the possible hypothesis in order of the possibility and also it can explain the propagation procedure of the faults for each solution using the built in backtracking mechanism. In realization of the expert system, the processing speed is greatly dependent upon the problem representation, reasoning scheme and search strategy. So, in this paper the fault diagnosis problem itself is analysed from the view point of Artificial Intelligence and as a result, the expert system has the following basic features. 1) The certainty factor is adopted in the inference engine for inexact reasoning. 2) Problem apace is represented using the problem reduction technique. 3) Bidirectional reasoning scheme is used. 4) Best first search strategy is adopted for rapid processing. The expert system was developed us ing PROLOG language.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.165-170
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• 2011

It is expected that the cooperation between rule-based reasoning and case-based reasoning gives us an efficient approach for flexible reasoning. In this paper, we present an integrated model of rule-base reasoning and case-base reasoning using the MVL automata model. In addition, we introduce how to handle the uncertainty in the integrated model.

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.61-69
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• 2001

We present a probabilistic reasoning method for inferring knowledge about mathematical truth before an automated theorem prover completes a proof. We use a Bayesian analysis to update beleif in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.1
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• pp.81-89
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• 1992

After much research on qualitative reasoning, the problem of ambiguities still hampers the practicality of this important AI tool. In this paper, the sources of ambiguities are examined in depth with a systems engineering point of view and possible directions to disambiguation are suggested. This includes some modeling strategies and an architecture of temporal inference for building unambiguous qualitative models of practical complexity. It is argued that knowledge of multiple levels in abstraction hierarchy must be reflected in the modeling to resolve ambiguities by introducing the designer's decisions. The inference engine must be able to integrate two different types of temporal knowledge representation to determine the partial ordering of future events. As an independent quantity management system that supports the suggested modeling approach, LIQUIDS(Linear Quantity-Information Deriving System) is described. The inference scheme can be conjoined with ordinary rule-based reasoning systems and hence generalized into many different domains.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.5
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• pp.93-102
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• 1997

Conventional expert systems have difficulties in the inference on time-varing situations because they don't have the structure for processing time related informations and rule representation method to describe time explicitely. Some expert systems capable of temporal reasoning are not applicable to the domain in which state changes happen by unpredictble events that cannot be represented by periodic changes of data. In this paper, an event based temporal reasoning method is proposed. It is capable of processing te unpredictable events, representing the knowledge related to event and time, and infering by that knowledge as well as infering by periodically time-varing data. The NEO/temporal, an temporal inference engine, is implemented by applying the proposed temporal reasoning situation assessment and decision supporting system is implemented to show the benefits of the proposed temporal information processing model.

• Journal of Information Processing Systems
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• v.10 no.4
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• pp.581-588
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• 2014

To facilitate the implementation of a wide variety of context-aware applications based on mobile devices, general-purpose context-aware framework that applications can use by calling is needed. The context-aware framework is a middleware that performs the sensing, reasoning, and retrieving based on the knowledge base. The knowledge base must systematically represent the information required on the behavior of the context-aware framework, such as context information and reasoning information. It must also provide functions for storage and retrieval. To date, previous research on the representation of the context information have been carried out, but studies on the unified representation of the knowledge base has seen little progress. This study defines the knowledge base as the unified context information, and proposes the UniOWL, which can do a good job of representing it. UniOWL is based on OWL and represents the information that is necessary for the operation of the context-aware framework. Therefore, UniOWL greatly facilitates the implementation of the knowledge base on a context-aware framework.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.91-100
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• 2020

Unlike the existing Visual Question Answering(VQA) problems, the new Visual Commonsense Reasoning(VCR) problems require deep common sense reasoning for answering questions: recognizing specific relationship between two objects in the image, presenting the rationale of the answer. In this paper, we propose a novel deep neural network model, KG_VCR, for VCR problems. In addition to make use of visual relations and contextual information between objects extracted from input data (images, natural language questions, and response lists), the KG_VCR also utilizes commonsense knowledge embedding extracted from an external knowledge base called ConceptNet. Specifically the proposed model employs a Graph Convolutional Neural Network(GCN) module to obtain commonsense knowledge embedding from the retrieved ConceptNet knowledge graph. By conducting a series of experiments with the VCR benchmark dataset, we show that the proposed KG_VCR model outperforms both the state of the art(SOTA) VQA model and the R2C VCR model.

• The KIPS Transactions:PartB
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• v.15B no.2
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• pp.147-158
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• 2008

Fuzzy Cognitive Map (FCM) and Bayesian Belief Network (BBN) are two major frameworks for modeling, representing and reasoning about causal knowledge. Despite their extensive use in causal knowledge engineering, there is no reported work which compares their respective roles. This paper aims to fill the gap by providing a qualitative comparison of the two frameworks through a systematic analysis based on some inherent features of the frameworks. We proposed a set of comparison criteria which covers the entire process of causal knowledge engineering, including modeling, representation, and reasoning. These criteria are usability, expressiveness, reasoning capability, formality, and soundness. The results of comparison have revealed some important facts about the characteristics of FCM and BBN, which will help to determine how FCM and BBN should be used, with respect to each other, in causal knowledge engineering.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.411-419
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• 2009

In order to derive hidden Information (concept subsumption, concept satisfiability and realization) of OWL ontologies, a number of OWL reasoners have been introduced. Most of the reasoners were implemented to be based on tableau algorithm. However this approach has certain limitation. This paper presents architecture for Medusa. The Medusa is an extended DL-reasoner for SWRL(Semantic Web Rule Language) reasoning under well-founded semantics with ontologies specified in Description Logic. Description logic based ontology reasoners theoretically explore knowledge representation and its reasoning in concept languages. However these logics are not equipped with rule-based reasoning mechanisms for assertional knowledge base; specifically, rule and facts in logic programming, or interaction of rules and facts with terminology. In order to deal with the enriched reasoning, The Medusa provides combining DL-knowledge base and rule based reasoner. The described prototype uses $Prot{\acute{e}}g{\acute{e}}$ API[1] for controlling communication with the ontology reasoner.