• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제10권3호
• /
• pp.170-177
• /
• 2010

Poker is a game of imperfect information where competing players must deal with multiple risk factors stemming from unknown information while making the best decision to win, and this makes it an interesting test-bed for artificial intelligence research. This paper introduces a new learning AI algorithm with embedded opponent modeling that can be used for these types of situations and we use this AI and apply it to a poker program. The new AI will be based on several graphs with each of its nodes representing inputs, and the algorithm will learn the optimal decision to make by updating the weight of the edges connecting these nodes and returning a probability for each action the graphs represent.

• 한국지능시스템학회논문지
• /
• 제19권3호
• /
• pp.350-354
• /
• 2009

Y. R$\acute{e}$ball$\acute{e}$[Fuzzy Sets and Systems, vol.157, pp.3025-2039, 2006] discussed the representation of necessity measure through the Choquet integral criterian. He also considered a decision maker who ranks necessity measures related with Choquet integral representation. Our motivation of this paper is that a decision maker have an "ambiguity" necessity measure to present preferences. In this paper, we discuss the representation of interval-valued necessity measures through the Choquet integral criterian.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.123-128
• /
• 1996

In this paper, a fuzzy controller is proposed for the operation of stoker-type refuse incinerator with many kinds of uncertain factors. To build the exact mathematical model is very difficult because of the variation of physical/chemical properties of refuse as a fuel and the complexity of the combusiton process. The fuzzy controller consists of fuzzy sensor, fuzzy decision maker and tracking part. The rules based on the professional operators empirical knowledge are made for the control of the boiler evaporation rate, emission gas and refuse throughput. For the performance measure of the proposed fuzzy controller, the model of the incinerator is constructed and the simulation results are given.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
• /
• pp.327-332
• /
• 1996

Rough Set theory suggested by Pawlak has a property that it can describe the degree of relation between condition and decision attributes of data which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a occupancy degree which is a measure can represent a degree of relational quantity between condition and decision attributes of data table. We also propose a method that can find an optimal fuzzy rule table and membership functions of input and output variables from data without linguistic information and examine the validity of the method by modeling data generated by fuzzy rule.

• 한국지능시스템학회논문지
• /
• 제11권3호
• /
• pp.231-240
• /
• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Journal of Electrical Engineering and Technology
• /
• 제13권2호
• /
• pp.961-968
• /
• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• 한국지능시스템학회논문지
• /
• 제10권6호
• /
• pp.575-583
• /
• 2000

본 논문에서 임의의 데이터가 입력되면 기준 영상 중에서 가장 유사도가 큰 영상을 찾아 국부 승리자로 선택하고, 그 국부 승리자 중에서 전체 승리자를 선택하여 최종 출력값을 얻는 계층적 FGNN(Fuzzy Genetic Neural Network)을 제안하고, 이에 하이브리드 퍼지 소속함수와 유전자 알고리즘을 적용하였다. 하이브리드 퍼지 소속함수는 입력 값을 0~1 사이의 값으로 함으로써 시스템의 속도를 빠르게 하고 유전자 알고리즘을 입력값을 일정한 오차 이내로 하여 최적의 영상을 얻도록 하였다. 위의 계층적 FGNN 알고리즘을 회로 설계 및 검증하였다. 또한 제안한 FGNN을 이용하여 영상에 포함된 잡음을 제거하고, 이와 유사한 구조를 가진 FDNN(Fuzzy Decision Neural Network) 성능보다 FGNN의 성능이 우수함을 여러 가지 영상을 통하여 확인하였다. 또한 모의 실험 결과 영상에 대한 평균자승오차(MSE : Mean Square Error)를 비교하였으며, 그 결과 하이브리드 퍼지 함수와 유전자 알고리즘을 적용한 FGNN이 메디안 필터, OC, CO, FDNN 등에 비해 우수함을 확인하였다. FGNN 알고리즘을 Top-Down 방식으로 VHDL(VHSIC Hardware description Language)을 이용하여 코딩(Coding)하고, Synopsys 툴을 이용하여 하드웨어를 설계하였다. 이 알고리즘의 하드웨어는 총 5개의 블록으로 가지고 있고 각각의 블록은 파이프라인 형태로 구성하고, 이는 Synopsys 툴을 이용하여 동작 및 성능을 검증하였다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1998년도 춘계학술대회 학술발표 논문집
• /
• pp.127-130
• /
• 1998

In this paper, we propose a method of cooperative control(T-cell modeling) and selection of group behavior strategy(B-cell modeling) based on immune system in distributed autonomous robotic system(DARS). Immune system is living body's self-protection and self-maintenance system. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
• /
• pp.1350-1353
• /
• 1993

A fuzzy control system typically requires“tuning,”or adjuctment of the parameters defining its linguistic variables. Automating this process amounts to applying a second“metacontrol”layer to drive the controller and plant to desired performance levels. Current methods of automated tuning rely on a single crisp numeric functional to evaluate control system performance. A generalization of Box's complex algorithm allows more realistic tuning based on lexicographic aggregation of multiple ordinal scales of performance, such as effectiveness and efficiency. The method is presented and illustrated using a simple inverted pendulum control system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
• /
• pp.11-14
• /
• 1995

A Fuzzy Classifier which deals with very confusing objects is proposed. Naturally this classifier heavily relies on the nulti-feature decision-making procedure. For a simple example, this classifier is applied to the recognition of confusing handwritten numerals 4,6 and 9 The characteristic variables used in this paper are the existence of a loop and the relative location of the starting or ending points(SEP). Thus each sample of handwritten numerals 4, 6 and 9 is classified in one of the 6 groups which are divided according to the sample structure. Each group has its own classifying rules. Also the method of rule-generation using genetic algorithms in each group is proposed.