• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1333-1341
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• 2009

Tactical path-finding in computer games is path-finding where a path is selected by considering not only basic elements such as the shortest distance or the minimum time spend but also tactical information of surroundings when deciding character's moving trajectory. One way to include tactical information in path-finding is to represent a heuristic function as a sum of tactical quality multiplied by a weighting factor which is.. determined based on the degree of its importance. The choice of weighting factors for tactics is very important because it controls search performance and the characteristic of paths found. In this paper. we propose a method for improving a heuristic function by adjusting weights based on the difference between paths on examples given by a level designer and paths found during the search process based on the CUITent weighting factors. The proposed method includes the search algorithm modified to detect search errors and learn heuristics and the perceptron-like weight updating formular. Through simulations it is demonstrated how different paths found by tactical path-finding are from those by traditional path-finding. We analyze the factors that affect the performance of learning and show the example applied to the real game environments.

• 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 Korea Information Processing Society Conference
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• 2010.11a
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• pp.486-489
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• 2010

전술적 경로찾기에서는 거리나 시간 요소 외에 여러 가지 전술적 요소를 포함한 비용 함수를 사용하여 경로를 탐색한다. 경로찾기에서 가장 많이 이용되는 A* 알고리즘의 경우, 현재 노드에서 목표까지의 추정값을 의미하는 휴리스틱 함수를 이용하는데 대표적인 허용가능 휴리스틱(admissible heuristic)인 유클리디안 거리(Euclidean distance)를 전술적 경로찾기에서 이용하는 경우, 탐색 성능이 저하되는 단점이 있다. 이는 거리이외에 전술적 요소까지 더해진 실제 비용에 비해 직선 거리만을 고려한 휴리스틱 값이 현저하게 작은데 기인한다. 그러므로 본 논문에서는 A*를 이용하는 경로찾기에서 탐색의 성능을 향상시킬 수 있는 두 가지 휴리스틱을 제안하고 이들의 허용성을 분석하고 방문 노드수 비교를 통해 탐색 성능을 비교한다.