• Proceedings of the Korea Society of Design Studies Conference
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• 2002.05b
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• pp.234-235
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• 2002

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.91-98
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• 2010

Conventional path-finding has focused on finding short collision-free paths. However, as computer games become more sophisticated, it is required to take tactical information like ambush points or lines of enemy sight into account. One way to make this information have an effect on path-finding is to represent a heuristic function of a search algorithm as a weighted sum of tactics. In this paper we consider the problem of learning heuristic to optimize path-finding based on given tactical information. What is meant by learning is to produce a good weight vector for a heuristic function. Training examples for learning are given by a game level-designer and will be compared with search results in every search level to update weights. This paper proposes a learning algorithm integrated with search for tactical path-finding. The perceptron-like method for updating weights is described and a simulation tool for implementing these is presented. A level-designer can mark desired paths according to characters' properties in the heuristic learning tool and then it uses them as training examples to learn weights and shows traces of paths changing along with weight learning.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.84-92
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• 1993

Calibration of Engine models is a painstaking process but very important for successful application to automotive industry problems. A combined heuristic and machine learning approach has therefore been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of amachine learning program called GID3*for automatic acquisition of heuristic rules for ordering target parameters.

• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.116-121
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• 2014

This paper discusses computer-generated heuristics for motion planning. Planning with many degrees of freedom is a challenging task, because the complexity of most planning algorithms grows exponentially with the number of dimensions of the problem. A well-designed heuristic may greatly improve the performance of a planning algorithm in terms of the computation time. However, in recent years, with increasingly challenging high-dimensional planning problems, the design of good heuristics has itself become a complicated task. In this paper, we present an approach to algorithmically develop a heuristic for motion planning, which increases the efficiency of a planner in similar environments. To implement the idea, we generalize modern motion planning algorithms to an extent, where a heuristic is represented as a set of random variables. Distributions of the variables are then analyzed with computer learning methods. The analysis results are then utilized to generate a heuristic. During the experiments, the proposed approach is applied to several planning tasks with different algorithms and is shown to improve performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.210-212
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• 2017

A design which would let its users optimize their learning experiences accordingly to the diversification of learning platforms is needed. Especially with the expansion of mobile learning, a heuristic evaluation was used to evaluate the usability in order to provide learners with various learning methods and experiences according to the unique characteristics a mobile possesses. Therefore, we could conclude that systematic factors like information display, intuitiveness and conciseness do have a significant effect on users' attitudes.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Research in Mathematical Education
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• v.25 no.2
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• pp.135-158
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• 2022

This study aimed to building models to understand the relationships between reasoning resources and content knowledge. We applied Support Vector Machine and linear models to the data including fifth graders' scores in the Cornel Critical Thinking Test and the Iowa Assessments, demographic information, and learning science approach (a student-centered approach to learning called the Science Writing Heuristic [SWH] or traditional). The SWH model showing the relationships between critical thinking domains and academic achievement at grade 5 was developed, and its validity was tested across different learning environments. We also evaluated the stability of the model by applying the SWH models to the data of the grade levels. The findings can help mathematics educators understand how critical thinking and achievement relate to each other. Furthermore, the findings suggested that reasoning in mathematics classrooms can promote performance on standardized tests.

• Journal of the Korean Society of Earth Science Education
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• v.10 no.3
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• pp.278-289
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• 2017

The purpose of this study is to investigate the effect of science writing heuristic class on 'seasonal change' on elementary school students' science learning motivation and scientific attitude. In order to carry out this study, 49 students from 6 grade of elementary school in P metropolitan city were selected and 24 students in one class were selected as experimental group and 25 students in the other class were selected as the comparative group. The experimental group was taught science writing heuristic in 10 times classes related to the 'seasonal change' and the general classes related to the 'seasonal change' were conducted in the comparative group. Based on the results of this study, the conclusions are as follows. First, the science writing heuristic class about 'seasonal change' showed a significant effect on elementary school students' science learning motivation for science class. This is analyzed as a result of the students being more familiar with their science class through science writing heuristic class. Second, the science writing heuristic class about 'seasonal changes' showed a significant effect on elementary school students' scientific attitude. This is because the science writing heuristic class offered elementary school students an opportunity to combine their experiences in daily life with the contents of the class. Third, students' participation, satisfaction, and interest in the science writing heuristic class applied in this study were high. Therefore, it seems to be effective if the science writing heuristic class is used for science class for elementary school students.

• Korean Management Science Review
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• v.13 no.3
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• pp.173-186
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• 1996

This paper presents an approach for classifying scheduling problems and selecting a heuristic rule to yield best solution in terms of certain performance measure(s). Classification parameters are employed from previous studies on job shop scheduling and project scheduling. Neural network is used for learning and estimating the performance of heuristic rules. In addition, multi-criteria decision making techniques are employed to combine the preferences for each performance measure and heuristic rule for the problems with multi-objectives.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.