• The Journal of Korea Robotics Society
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• v.18 no.2
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• pp.225-232
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• 2023

With the recent increase of multiple robots cooperating in smart manufacturing logistics environments, it has become very important how to predict the safety and efficiency of the individual tasks and dynamically assign them to the best one of available robots. In this paper, we propose a novel task policy learner based on deep relational reinforcement learning for predicting the safety and efficiency of tasks in a multi-robot manufacturing logistics environment. To reduce learning complexity, the proposed system divides the entire safety/efficiency prediction process into two distinct steps: the policy parameter estimation and the rule-based policy inference. It also makes full use of domain-specific knowledge for policy rule learning. Through experiments conducted with virtual dynamic manufacturing logistics environments using NVIDIA's Isaac simulator, we show the effectiveness and superiority of the proposed system.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1434-1445
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• 2000

An expert system executes the inference, based on the knowledge of specific domain. the reliability on the results of inference depends upon both the consistency and accuracy of knowledge. This is the reason why expert system requires the facilities which can practice an access to the various kinds of knowledge and maintain the consistency and accuracy of knowledge an maintain the consistency and accuracy of knowledge. This paper is to implement an expert system permitting an access of declarative and procedural knowledge in the knowledge base and in the data base. This paper is also to implement a knowledge acquisition system which adds the knowledge a only if its accuracy and consistency are maintained, after verifying the potential errors such as contradiction, redundancy, circulation, non-reachable rule and non-lined rule. In consequence, the expert system realizes a good access to the various sorts of knowledge and increases the reliability on the results of inference. The knowledge acquisition system contributes tro strengthening man-machine interface that enables users to add the knowledge easily to the knowledge base.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.142-148
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• 1993

Recectly, development of expert system utilizing the domain specific knowledge focuses upon the machining operations. This paper describes an expert system for selecting the optimum grinding wheel based on the Analytic Hierarchy Process and Fuzzy Logic. Knowledge-base, in this system, for selecting of grinding wheel is designed to appling the knowhow and experience knowledge of skilled hands. In this paper, firstly determination method of fuzzy membership function utilizing the qualitative knowledge, and then selection of the optimum wheel from among the available components according to Saaty's priority rule are described. Lastly,some implementation results are suggested.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.11
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• pp.126-133
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• 2007

Tuberculosis (TB) is a chronic or acute infectious disease which damages more people than any other infectious diseases according to WHO estimates. In this paper, a new automatic detection system of tuberculosis bacilli by using their color information is proposed. Through the deep investigation of color and intensity compositions of tuberculosis images, new pre-processing and segmentation algorithms are suggested. Specific features of bacilli are extracted from the processed images and number counting is done by using domain-specific knowledge rules.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.511-515
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• 2001

Many intelligent agent systems are known to incorporate BDI architecture for cognitive reasoning. Since this architecture contains all the knowledge of world model and reasoning rule, it is very complex and difficult to handle. This paper describes a methodology to design and implement BDI architecture, BDIAXml based on XML for multi-agent systems. This XML-based BDI architecture is smaller than any other BDI architecture because it separates knowledge for reasoning from domain knowledge and enables knowledge sharing using XML technology. Knowledge for BDI mental state and reasoning is composed of specific XML files and these XML files are stored into a specific knowledge server. Most systems using BDIAxml architecture can access knowledge from this server. We apply this BDIAXml system to domain of Hospital Information System and show that this architecture performs more efficiently than other BDI architecture system in terms of knowledge sharing, system size, and ease of use.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• The Transactions of the Korea Information Processing Society
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• v.3 no.7
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• pp.1781-1791
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• 1996

A number of research issues such as knowledge acquisition, inferencing techniques, and knowledge refinement methodologies have been involved in the development of expert systems. Since each issue is considered very com- plicated, there has been little effort to take all the issues into account collectively at once. However, knowledge acquisition and inferencing are closely reated because the knowledge is extracted by human experts from the inferencing process for solving a specific task or problem. Knowledge refinement is also accomplished by hand-ling problems caused during the inferencing process of the system due to incompleteness and inconsistency of the knowledge base. From this perspecitive, we present a method by which software platform is established in which those issues are integrated in the development of expert systems, especially in the domain where the domain models and concepts are hard to be constructed because of inherent fuzziness of the domain. We apply a machine learning technique,technique, conceptual clustering,to build a knowledge base and rual models by which an efficient inferencing,incermental knp\owledge acquisition and refinment are possible.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.479-486
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• 2005

The ontology was proposed to construct the logical basis of semantic web. Ontology represents domain knowledge in the formal form and it enables that machine understand domain knowledge and provide appropriate intelligent service for user request. However, the construction and the maintenance of ontology requires large amount of cost and human efforts. This paper proposes an automatic ontology construction method for defining relation between concepts in the documents. The Proposed method works as following steps. First we find concept pairs which compose association rule based on the concepts in domain specific documents. Next, we find pattern that describes the relation between concepts by clustering the context between two concepts composing association rule. Last, find generalized pattern name by clustering the clustered patterns. To verify the proposed method, we extract relation between concepts and evaluate the result using documents set provide by TREC(Text Retrieval Conference). The result shows that proposed method cant provide useful information that describes relation between concepts.