Apply different fuzzy integrals in unit selection problem of real time strategy game

Li, Y. J.; Ng, Peter H. F.; Wang, H. B.; Shiu, Simon; Li, Y.

doi:10.1109/fuzzy.2011.6007426

Cited by 9 publications

(12 citation statements)

References 23 publications

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“…Shown are the best fitnesses obtained at each generation by using different fuzzy integrals: max-based fuzzy integral, mean-based fuzzy integral, and orderbased fuzzy integral. As can be seen, order-based fuzzy integral c 2015 Information Processing Society of Japan showed the best performance among those three methods, which also confirms the findings obtained by Li et al [16]. Thus, only order-based fuzzy integral and the corresponding fuzzy measure are used in our evaluation below.…”

Section: Learning Fuzzy Measure By Genetic Algorithmsupporting

confidence: 75%

“…Li et al applied different fuzzy integrals to solve the unit selection problem in RTS games [16]. The key to winning in most RTS games is to build up a strong army with appropriate unit types which can gain massive destroy power against the enemy army.…”

Section: Fuzzy Integral For Unit Selection Problem In Rts Gamementioning

confidence: 99%

“…In this paper, we used a genetic algorithm approach to learn the aforementioned fuzzy measure, as done in Ref. [16].…”

Section: Learning Fuzzy Measure By Genetic Algorithmmentioning

confidence: 99%

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Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

Nguyen

Thawonmas

2015

Journal of Information Processing

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This paper presents an approach that integrates fuzzy integral and fast heuristic search for improving the quality of unit micromanagement in the popular RTS game StarCraft. Unit micromanagement, i.e., detailed control of units in combat, is one of the most challenging problems posed by RTS games and is often tackled with search algorithms such as Minimax or Alpha-Beta. Due to vast state and action spaces, the game tree is often very large, and search algorithms must rely on evaluation methods from a certain limited depth rather than exploring deeper into the tree. We therefore attempt to apply fuzzy integral and aim for an evaluation method with high accuracy in the search. To achieve this aim, we propose a new function that allows fuzzy integral to cope with not only non-additive properties but also unit properties in RTS games. Experimental results are reported at the end of this paper, showing that our approach outperforms an existing approach in terms of win rates in this domain.

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Section: Learning Fuzzy Measure By Genetic Algorithmsupporting

confidence: 75%

Section: Fuzzy Integral For Unit Selection Problem In Rts Gamementioning

confidence: 99%

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Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

Nguyen

Thawonmas

2015

Journal of Information Processing

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“…Kurtosis is related to the second-and fourth-order moments: 2) Fuzzy integral: The above features are integrated to verify if the candidate is at a respiration point. The verification problem is a type of multi-criteria decision-making problem and fuzzy integral techniques have been used to solve such problems [19,20]. Fuzzy integral techniques can provide a nonlinear function to integrate evidence from different kinds of features.…”

Section: D) Kurtosis Of Periodsmentioning

confidence: 99%

A Vision-Based Infant Respiratory Frequency Detection System

Fang

Hsieh²,

Chen

2015

2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Sudden infant death syndrome (SIDS) is the major cause of death for infants aged one week to twelve months. The SIDS rate has declined owing to the awareness of caregivers and parents, but the rate is still high even in developed countries because of the difficulty in rescuing the infant immediately. Respiration, which can reflect various physiological conditions, is a basic but vital function for infants. Therefore, this study presents a respiration monitoring system with a video camera positioned in front of an infant to non-invasively detect the infant's respiratory frequency. The proposed system can continuously monitor the infant to detect unusual occurrences in the infant's respiration, to alert caregivers to attend to the infant immediately and reduce potential injuries from SIDS and other respiratory-related disease.The proposed system contains four major stages, including motion detection, candidate point extraction, respiration point selection, and respiratory frequency calculation. During motion detection the system captures images from video and decides whether to conduct the following stages. If no obvious motion is detected in the input frames, then SIDS may have occurred in the infant, and the system extracts candidate points by some spatial characteristics. Based on these points, the system then selects respiration points using a fuzzy integral technique with four temporal characteristics, including entropy, period, skewness, and kurtosis. Finally, the infant's respiratory frequency is calculated. Experimental data are obtained from ten infants, in 48 sequences with a total length of 150 minutes. The experimental results show that the proposed system is robust and efficient.Keywords-Home healthcare, vision-based infant monitoring system, vision-based infant respiratory frequency detection system, fuzzy integral.

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“…The core of this game is to build up an army with appropriate unit combination which can gains massive destroy power in a short time. Specially, feature interactions are proofed existing among different unit types [1,2], which offer a large variety of fundamental AI research problems such as decision making under uncertainty [3,4] and adversarial real-time planning [5].In this paper, we concentrate on how to evaluate different unit combinations rapidly and a combination of ELM and FI with real game data is introduced .…”

Section: Introductionmentioning

confidence: 99%

Rapid Game Strategy Evaluation Using Fuzzy Extreme Learning Machine

Shiu

2013

Lecture Notes in Computer Science

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Interactions among game units can be conveniently described by fuzzy measures and integrals. Focusing on Warcraft, there are several good results of unit selection strategy evaluation for a genetic algorithm that search in plan space. However, this kind of evaluators are suffered from high complexity in fuzzy measure determination. In this paper, we novelly combine Extreme Learning Machine(ELM) and Fuzzy Integral(FI) to achieve a fast evaluation of game strategy. Experimental comparison demonstrates the effectiveness of the proposed method in both time and accuracy.

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Apply different fuzzy integrals in unit selection problem of real time strategy game

Cited by 9 publications

References 23 publications

Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

Heuristic Search Exploiting Non-additive and Unit Properties for RTS-game Unit Micromanagement

A Vision-Based Infant Respiratory Frequency Detection System

Rapid Game Strategy Evaluation Using Fuzzy Extreme Learning Machine

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