• Journal of Korea Game Society
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• v.15 no.4
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• pp.157-168
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• 2015

The difficulty of the game is closely related to the fun of the game. However, it is not easy to determine the appropriate level of difficulty of the game. In most cases, human playtesting is required. But even so, it is still hard to quantitatively evaluate difficulty of the game. Thus, if we perform quantitative evaluation of the difficulty automatically it will be very helpful in game developments. In this paper, we use a path finding algorithm to evaluate difficulty of exploration in a game level. Exploration is a basic attribute in common video games and it represents the overall difficulty of the game level. We also optimize the proposed evaluation algorithm by using previous exploration histories when available area in an game level is dynamically expanded and the new search is required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.939-955
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• 2010

With the progress of IT and mobile positioning technologies, various types of location-based services (LBS) have been proposed and implemented. Finding a shortest path between two nodes is one of the most fundamental tasks in many LBS related applications. So far, there have been many research efforts on the shortest path finding problem. For instance, $A^*$ algorithm estimates neighboring nodes using a heuristic function and selects minimum cost node as the closest one to the destination. Pruning method, which is known to outperform the A* algorithm, improves its routing performance by avoiding unnecessary exploration in the search space. For pruning, shortest paths for all node pairs in a map need to be pre-computed, from which a shortest path container is generated for each edge. The container for an edge consists of all the destination nodes whose shortest path passes through the edge and possibly some unnecessary nodes. These containers are used during routing to prune unnecessary node visits. However, this method shows poor performance as the number of unnecessary nodes included in the container increases. In this paper, we focus on this problem and propose a new border line-based pruning scheme for path routing which can reduce the number of unnecessary node visits significantly. Through extensive experiments on randomly-generated, various complexity of maps, we empirically find out optimal number of border lines for clipping containers and compare its performance with other methods.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.179-182
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• 2008

It needs to consider of turns during a path-finding on real road network. Because a car is delayed by waiting a traffic signal and decreasing speed when drived in a turn road such as cross road and slip road. If a straightness of a path is increased, a real cost of traveling should be able to decrease. An older method, the algorithm with Turn Heuristic, considered of this case. The algorithm, that differently gave weights to left, right and U-turns, improved a straightness of a path, but increased a cost of exploring. In this paper, we propose a improved Turn Heuristic Algorithm. Proposed algorithm uses Dynamic Turn Heuristic. It is able to more decrease a cost of exploring than older method by using the Turn Heuristic in a part of path-finding.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.565-569
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• 2007

To find optimal path is killer application in the telematics system. The shortest path of conventional system, however, isn't always optimal path. That is, the path with minimum travelling time could be defined as optimal path in the road networks. There are techniques and algorithms for finding optimal path. Hierarchical path algorithm categorizes road networks into major layer and minor layer so that the performance of operational time increases. The path searched is accurate as much as optimal path. At above 2 system, a method to allocate minor roads to major road region influences the performance extremely. This paper proposes methods to determine search space for selecting major roads in the hierarchical path algorithm. In addition, methods which apply the proposed methods to hierarchical route algorithm is presented.

• Journal of Korea Game Society
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• v.13 no.1
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• pp.31-40
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• 2013

A* algorithm is a well known pathfinding algorithm. However, there may be a limit to use A* algorithm in real-time in a map where many interactions occur between objects or many obstacles exist. Therefore, it may be necessary to find a naturally looking path quickly instead of finding a shortest path in games. In this paper, we propose a new heuristic function to exploit path information in a map. We also show that the pathfinding algorithm based on the proposed heuristic function can find a good path much faster than A* algorithm on several grid maps.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.5
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• pp.574-584
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• 2017

In this paper, the optimizing path finding control method is studied for a Legged-robot. It's named TITAN-VIII. It has a lot of advantages over the wheeled robot in the ability to walk freely on an irregular ground. However, the moving speed on the ground of the Legged-robot is slower than the Wheeled-robot's. Consequently, the purpose of the method is presented in this paper to minimize its time when it walks to a goal. It find the path, our approach is based on an algorithm which is called Dijkstra's algorithm. In the rest of paper, the various posture of the robot is discussed to keep the robot always in the statically stable. Based on above works, the math formulas are presented to determine the joint angles of the robot. After that an algorithm is designed to find and keep robot on the desired trajectory. Experimental results of the proposed method are demonstrated in the last of paper.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.434-439
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• 1994

Recently neural networks leave been proposed as new computational tools for solving constrained optimization problems because of its computational power. In this paper, the shortest path finding algorithm is proposed by rising a Hopfield type neural network. In order to design a Hopfield type neural network, an energy function must be defined at first. To obtain this energy function, the concept of a vector-represented network is introduced to describe the connected path. Through computer simulations, it will be shown that the proposed algorithm works very well in many cases. The local minima problem of a Hopfield type neural network is discussed.

• The KIPS Transactions:PartA
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• v.13A no.6 s.103
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• pp.541-544
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• 2006

In a tree with each edge associated with a length and a weight (positive, negative, or zero are possible) we develop an O(nlognloglogn) time algorithm for finding a path such that its sum of weights is maximized and its sum of lengths does not exceed a given value. The previously best-known result is O($nlog^2n$), where n is the number of nodes in the tree.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.170-171
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• 2015

Resource procurement and material handling are considered as a significant part of construction project especially in large or tall building construction site. There are multiple variables that must be considered in a construction site during finishing work such as movement of materials, equipments, and workers. Therefore, it is difficult for construction workers to find the material handling path solely by intuition. The aim of this study is to propose a real-time path finding model suitable for complicated logistics flow in the field. The model explores the optimal transport path of finishing material with its basis on optimization algorithm, and it determines the direction of the Smart Sign. The proposed model is expected to be utilized for planning of efficient finishing material handling.