Journal of Intelligence and Information Systems,
Vol. 1, No. 2, December 1995
Nonlinear Models and Linear Models in Expert-Modeling A Lens Model Analysis
Vol. 1, No. 2, Page: 1 ~ 16
The field of human judgment and decision making provides useful methodologies for examining the human decision making process and substantive results. One of the methodologies is a lens model analysis which can examine valid nonlinearity in the human decision making process. Using the method, valid nonlinearity in human decision behavior can be successfully detected. Two linear(statistical) models of human experts and two nonlinear models of human experts are compared in terms of predictive accuracy (predictive validity). The results indicate that nonlinear models can capture factors(valid nonlinearity) that contribute to the expert's predictive accuracy, but not factors (inconsistency) that detract from their predictive accuracy. Then, it is argued that nonlinear models cab be more accurate than linear models, or as accurate as human experts, especially when human experts employ valid nonlinear strategies in decision making.
Fuzzy Traffic Control Expert System
Jeong-Ae Jin, and Yong-Gi Kim
Vol. 1, No. 2, Page: 17 ~ 32
Keywords : Fuzzy Information Retrieval Technique, Traffic signal control system, expert system
An Improved Fuzzy Cognitive Map with Fuzzy Causal Relationships and Fuzzy Partially Causal Realtionships
Hyunsoo Kim, and Kun-Chang Lee
Vol. 1, No. 2, Page: 33 ~ 55
Rule Extraction from Neural Networks : Enhancing the Explanation Capability
Sang Chan Park, S. Monica Lam, and Amit Gupta
Vol. 1, No. 2, Page: 57 ~ 71
Keywords : rule extraction, neural network, inductive learning, expert systems, knowledge based decision support systems
This paper presents a rule extraction algorithm RE to acquire explicit rules from trained neural networks. The validity of extracted rules has been confirmed using 6 different data sets. Based on experimental results, we conclude that extracted rules from RE predict more accurately and robustly than neural networks themselves and rules obtained from an inductive learning algorithm do. Rule extraction algorithm for neural networks are important for incorporating knowledge obtained from trained networks into knowledge based systems. In lieu of this, the proposed RE algorithm contributes to the trend toward developing hybrid and versatile knowledge-based system including expert systems and knowledge-based decision support systems.
Intelligent Control Based on Evolution Algorithms
Mal Rey Lee, and Ki Tae Kim
Vol. 1, No. 2, Page: 73 ~ 83
In this paper, we propose a generating method for the optimal rules of the fuzzy rule base using evolution algorithms. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and knowledge. can be intelligent control. The a, pp.oach presented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which is the defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method in non-linear systems.
Predicting Korea Composite Stock Price Index Movement Using Artificial Neural Network
Jongyeob Park, and Ingoo Han
Vol. 1, No. 2, Page: 103 ~ 121
Keywords : Korea composite stock price index (KOSPI), Artifical neural network, moving-period simulation, buying-and-selling simulation
This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.
Knowledge Acquisition on Scheduling Heuristics Selection Using Dempster-Shafer Theory(DST)
Jaemin Han, and Insoo Hwang
Vol. 1, No. 2, Page: 123 ~ 137
Keywords : knowledge acqusition, job shop scheduling, Dempster-Shafer Theory (DST), multi-objectives scheduling, statistical reasoning
Most of solution methods in scheduling attempt to generate good solutions by either developing algorithms or heuristic rules. However, scheduling problems in the real world require considering more factors such as multiple objectives, different combinations of heuristic rules due to problem characteristics. In this respect, the traditional mathematical a, pp.oach showed limited performance so that new a, pp.oaches need to be developed. Expert system is one of them. When an expert system is developed for scheduling one of the most difficult processes faced could be knowledge acquisition on scheduling heuristics. In this paper we propose a method for the acquisition of knowledge on the selection of scheduling heuristics using Dempster-Shafer Theory(DST). We also show the examples in the multi-objectives environment.

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