• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.120-123
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• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.127-132
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• 2008

This study proposes a Natural Language Query Framework (NLQF) for the semantic web. It supports an intelligent inference at a semantic level. Most of previous researches focused on the knowledge representation on the semantic web. However, to revitalize the intelligent e-business on the semantic web, there is a need for semantic level inference to the web information. To satisfy the need, we will review the knowledge/resource representation on the semantic web such as RDF, Ontology and Conceptual Graph (CG), and then discuss about the natural language (NL) inference. The result of this research could support a natural interface for the semantic web. Furthermore, we expect that the NLQF can be used in the semantic web-based business communications.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.69-74
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• 2009

In this paper, we propose a method of ontology-based control of autonomous robots. Advancing one step further from using ontology as a hierarchical storage of information, the proposed method shows how to control robots through ontology inference. That is, the information on obstacles detected by robots is represented as an ontology, and robots' action planning and control are performed according to robots' surroundings through ontology inference. We make a differentially driven robot and illustrate the effectiveness of the proposed method via the experiment of the robot's navigation in real environment.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.2
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• pp.67-72
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• 2005

This paper presents a method to select contingency ranking considering voltage security problems in power systems. Direct method which needs not the detailed knowledge of the post contingency voltage at each bus is used. Based on system operator's experience and knowledge, the membership functions for the MVAR mismatch and allowable voltage violation are justified describing linguistic representation with heuristic rules. Rule base is used for the computation of severity index for each contingency by fuzzy inference. Contingency ranking harmful to the system is formed by the index for security evaluation. Compared with 1P-1Q iteration, this algorithm using direct method and fuzzy inference shows higher computation speed and almost the same accuracy. The proposed method is applied to model system and KEPCO pratical system which consists of 311 buses and 609 lines to show its effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.554-560
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• 2006

In this paper, we propose the topological map building method about unknown environment using the ultrasonic sensors. An ultrasonic sensor inherently has the range error due to the specular reflection. To decrease this error, we estimate the obstacle states(position and velocity) using the local minimum sensor values and Jacobian. Estimated states are used to avoid the obstacles and build the topological map similar to the type that human being memorizes an environment. When a mobile robot is faced with three problems(comer way, cross way and dead end), it senses the movable directions using FIS(Fuzzy Inference System). Among these directions, it can select the target direction using binary decision tree(Turn Side Selector). Proposed algorithm has been verified with three simulations and three implementations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.60-66
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• 1997

This paper presents a fuzzy inference-based reinforcement learning algorithm of dynamci recurrent neural networks, which is very similar to the psychological learning method of higher animals. By useing the fuzzy inference technique the linguistic and concetional expressions have an effect on the controller's action indirectly, which is shown in human's behavior. The intervlas of fuzzy membership functions are found optimally by genetic algorithms. And using recurrent neural networks composed of dynamic neurons as action-generation networks, past state as well as current state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying it to the inverted pendulum control problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.140-145
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• 2008

In this paper, we constructed the advanced cargo monitoring system for liquid cargo tankers which embedded the Decision Support System (DSS) based on the International Ship Management Code (ISM Code). To make this system, we first organized a base of expert's knowledge concerning liquid tanker operations that largely affect ocean accidents. We can find out the knowledge via inference method which simply imitates the fuzzy inference method. Based on this expert's knowledge, we constructed the DSS that provides a code of conduct for operating cargo tanks safely. The proposed monitoring system could eliminate human error when confronting dangerous situations, so the system will help sailors to operate cargo tanks safely.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.87-93
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• 2008

In this paper, we propose a hierarchical fuzzy based nutrition evaluation system that can analyze the individuals' nutrition status through the inference results generated by each layer. Moreover, a method to minimize the uncertainty of inference in the evaluated nutrition status is discussed. To show the effect of the uncertainty in fuzzy inference, we compared the results of nutrition evaluation with/without the certainty factor of rules on 132 people over the age of 65. From the experimental results, we can see that the evaluation method with the modified certainty factor provides better reliability than that of the general evaluation method without the certainty factor.

• 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.