Automatic player position detection in basketball games

This paper presents us with a framework for the automatic player position detection (APPD) in the game of basketball. Court players are detected in the images broadcasted via television stations. In them, at any point of time, the view is from only one camera. This makes the detection process much more difficult. The player detection is based on the mixture of non-oriented pictorial structures. The detection of body parts is performed by the Support Vector Machine (SVM) algorithm. The results of these detections are combined together with constraints on their locations, which specify the position of one body part with respect to the parent body part. In order to train the whole model, we used a latent form of SVM called the latent SVM (LSVM). Such approach generated the statistical accuracy of about 82 %, which represents one of the best results in basketball player detection framework. Beside players, the algorithm detected a certain number of false positive objects. These are mostly people from the audience and the referees as well. This paper contains a simple and robust solution to remove them all, based on the play court boundaries and the histogram comparison. Separating players in different teams is done by k-means clustering. The inputs to this algorithm are saturation histograms calculated on the jerseys. A spatial transformation is determined by the detected play court boundaries and the actual court measures. Using this transformation, points representing the location of detected players in TV images are mapped to the actual location of players on the court, which was the main goal of our research. The proposed solution is sound and efficient. In addition, it is backed up by the experimental results obtained using the model of the actual footage of basketball games.     

Adaptive fuzzy rule-based classification systems

This paper proposes an adaptive method to construct a fuzzy rule-based classification system with high performance for pattern classification problems. The proposed method consists of two procedures: an error correction-based learning procedure, and an additional learning procedure. The error correction-based learning procedure adjusts the grade of certainty of each fuzzy rule by its classification performance. That is, when a pattern is misclassified by a particular fuzzy rule, the grade of certainty of that rule is decreased. On the contrary, when a pattern is correctly classified, the grade of certainty is increased. Because the error correction-based learning procedure is not meaningful after all the given patterns are correctly classified, we cannot adjust a classification boundary in such a case. To acquire a more intuitively acceptable boundary, we propose an additional learning procedure. We also propose a method for selecting significant fuzzy rules by pruning unnecessary fuzzy rules, which consists of the error correction-based learning procedure and the concept of forgetting. We can construct a compact fuzzy rule-based classification system with high performance     

Bootstrapping polarity classifiers with rule-based classification

In this article, we examine the effectiveness of bootstrapping supervised machine-learning polarity classifiers with the help of a domain-independent rule-based classifier that relies on a lexical resource, i.e., a polarity lexicon and a set of linguistic rules. The benefit of this method is that though no labeled training data are required, it allows a classifier to capture in-domain knowledge by training a supervised classifier with in-domain features, such as bag of words, on instances labeled by a rule-based classifier. Thus, this approach can be considered as a simple and effective method for domain adaptation. Among the list of components of this approach, we investigate how important the quality of the rule-based classifier is and what features are useful for the supervised classifier. In particular, the former addresses the issue in how far linguistic modeling is relevant for this task. We not only examine how this method performs under more difficult settings in which classes are not balanced and mixed reviews are included in the data set but also compare how this linguistically-driven method relates to state-of-the-art statistical domain adaptation.     

Designing sound-based computer games

Designing interactive entertainment for visually impaired users poses several challenges. This article points out some key issues faced when developing sound-based computer games. The three games described here were developed for the Swedish Library of Talking Books and Braille (TPB), to be published on the TPB Internet site (TPB 2002). These games, Towers of Hanoi, Memory and Tag, can be played without the aid of graphics, although they do also feature animations of a style designed for partially sighted players. The games are fairly simple Macromedia Flash? applications, suitable for web publishing, and while not so complex as some other titles, they still emphasise some crucial design issues of creating sound-based interactive media.     

Effect of rule weights in fuzzy rule-based classification systems

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF-THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF-THEN rule that has the maximum compatibility grade with the new pattern. When we use fuzzy IF-THEN rules with certainty grades, the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy IF-THEN rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy IF-THEN rules with certainty grades     

R1: A rule-based configurer of computer systems

R1 is a program that configures VAX-11/780 computer systems. Given a customer's order, it determines what, if any, modifications have to be made to the order for reasons of system functionality and produces a number of diagrams showing how the various components on the order are to be associated. The program is currently being used on a regular basis by Digital Equipment Corporation's manufacturing organization. R1 is implemented as a production system. It uses Match as its principal problem solving method; it has sufficient knowledge of the configuration domain and of the peculiarities of the various configuration constraints that at each step in the configuration process, it simply recognizes what to do. Consequently, little search is required in order for it to configure a computer system.     

On frame rate and player performance in first person shooter games

The rate at which frames are rendered in a computer game directly impacts player performance, influencing both the game playability and enjoyability. However, despite the importance of frame rate and the wide-spread popularity of computer games, to the best of our knowledge, there is little quantitative understanding of the effects of frame rate on player performance in computer games. This paper provides a unique classification of actions in First Person Shooter (FPS) games based on interaction requirements that allow qualitative assessment of the impact of frame rates on player performance. This qualitative assessment is supported by quantitative analysis from two large user studies that measure the effects of frame rate on the fundamental player actions in a FPS game. Nearly 100 users participated in the two user study experiments, providing performance and perception data over a range of frame rates commonly studied for video streaming and inclusive of frame rates found in many computer game platforms. In general, the analysis shows that actions that require precise, rapid response, such as shooting, are greatly impacted by degradations in frame rates, while actions with lower precision and response requirements, such as moving, are more tolerant of low frame rates. These insights into the effects of frame rates on player performance can guide players in their choice for game settings and new hardware purchases, and inform system designers in their development of new hardware.     

Missing data imputation for fuzzy rule-based classification systems

Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.     

Online behavior change detection in computer games

Player Modelling has been receiving much attention from the game community in the recent years. The ability to build accurate models of player behavior can be useful in many aspects of a game. One important aspect is the tracking of a player’s behavior along time, informing every time a change is perceived. This way, the game Artificial Intelligence can adapt itself to better respond to this new behavior. In order to build models of player behavior, researchers frequently resort to Machine Learning techniques. Such methods work on previously recorded game metrics representing player’s interactions with the game environment. However, if the player changes styles over time, the constructed models get out of date. In order to address this drawback, this work proposes the use of and incremental learning technique to track a player’s behavior during his/her interaction with the game environment. Our approach attempts to automatically detect the moments in time when the player changes behavior. We apply a change detection technique from the area of Data Stream Mining that is based on incremental clustering and novelty detection. We also propose three modifications to the original technique, in order to formalize change detection, improve detection rate and reduce detection delay. Simulations were performed considering data produced by the Unreal Tournament game, showing the applicability of the method to online tracking of a player’s behavior and informing whenever behavior changes occur.     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.     

An ensemble method for fuzzy rule-based classification systems

Fuzzy rule-based classification systems are very useful tools in the field of machine learning as they are able to build linguistic comprehensible models. However, these systems suffer from exponential rule explosion when the number of variables increases, degrading, therefore, the accuracy of these systems as well as their interpretability. In this article, we propose to improve the comprehensibility through a supervised learning method by automatic generation of fuzzy classification rules, designated SIFCO–PAF. Our method reduces the complexity by decreasing the number of rules and of antecedent conditions, making it thus adapted to the representation and the prediction of rather high-dimensional pattern classification problems. We perform, firstly, an ensemble methodology by combining a set of simple classification models. Subsequently, each model uses a subset of the initial attributes: In this case, we propose to regroup the attributes using linear correlation search among the training set elements. Secondly, we implement an optimal fuzzy partition thanks to supervised discretization followed by an automatic membership functions construction. The SIFCO–PAF method, analyzed experimentally on various data sets, guarantees an important reduction in the number of rules and of antecedents without deteriorating the classification rates, on the contrary accuracy is even improved.     

基于个人超出值的区间值合作博弈新的求解模型

从个人超出值的视角研究特征函数为区间值的合作博弈和联盟为模糊集的无限模糊联盟区间值合作博弈.首先,利用区间值距离公式定义个人超出值;然后,建立最小化所有局中人个人超出值的最优化模型,进一步得到两类区间值合作博弈的显式解析解,并证明该解的性质;最后,通过数值实例验证所提出区间值合作博弈求解模型的实用性与有效性,为区间值合作博弈提供一种新的求解思路.     

Supervised methods for regrouping attributes in fuzzy rule-based classification systems

This paper focuses on ensemble methods for Fuzzy Rule-Based Classification Systems (FRBCS) where the decisions of different classifiers are combined in order to form the final classification model. The proposed methods reduce the FRBCS complexity and the generated rules number. We are interested in particular in ensemble methods which cluster the attributes into subgroups of attributes and treat each subgroup separately. Our work is an extension of a previous ensemble method called SIFRA. This method uses frequent itemsets mining concept in order to deduce the groups of related attributes by analyzing their simultaneous appearances in the databases. The drawback of this method is that it forms the groups of attributes by searching for dependencies between the attributes independently from the class information. Besides, since we deal with supervised learning problems, it would be very interesting to consider the class attribute when forming the attributes subgroups. In this paper, we proposed two new supervised attributes regrouping methods which take into account not only the dependencies between the attributes but also the information about the class labels. The results obtained with various benchmark datasets show a good accuracy of the built classification model.     

Al in computer games: Survey and perspectives

Computer games have traditionally implemented empirical solutions to many Al problems and are now turning to more traditional Al algorithms. After introducing the role of Al in gameplay, we review the main techniques used in current computer games such as Finite-State Transition Networks, rule-based systems and search algorithms. We describe the implementation of Al in several commercial computer games, as well as academic research in Al targeting computer games applications. We conclude this review by discussing future trends and proposing research directions.     

Ender's game redux [computer games]

In this article the author explains the context of his brother's e-mail, who is a US Army surgeon in Iraq. The e-mail concisely summarizes the convergence of entertainment technology and military training. He also describes the influence of game technology in his training.     

Aggression,competition and computer games: computer and human opponents

Violence and aggression in computer games has been a concern of social commentators and an interest of media researchers for more than 10 years. Violent content has been at the top of the agenda even though aggression and hostility have been identified as a part of competitive gaming situations. The role of the opponent in this process has been largely overlooked. We examined the difference in frustration and aggression in game play after users encountered the computer as opponent and a proximate person as opponent using the same CD-ROM version of Monopoly. We found that users experienced higher levels of aggressive feelings after playing the computer than after playing a stranger face-to-face. It appears that aggression related to computer gaming may be reduced through the humanization of computer opponents.     

Applying inexpensive AI techniques to computer games

Groo (Generic Robot, Object-Oriented) and tt14m (Trash-Talking 14-year-old Moron) are two systems that use simple and computationally inexpensive artificial intelligence mechanisms to produce engaging character behavior for computer games, while remaining within the performance constraints of modern game development. Groo engages in intelligent tactical behavior in a first-person-shooter death-match game using a fairly simple and static behavior network, while tt14m uses simple text processing to attempt engagement in the social aspects of the game "Counter-Strike".     

A systematic literature review of empirical evidence on computer games and serious games

This paper examines the literature on computer games and serious games in regard to the potential positive impacts of gaming on users aged 14 years or above, especially with respect to learning, skill enhancement and engagement. Search terms identified 129 papers reporting empirical evidence about the impacts and outcomes of computer games and serious games with respect to learning and engagement and a multidimensional approach to categorizing games was developed. The findings revealed that playing computer games is linked to a range of perceptual, cognitive, behavioural, affective and motivational impacts and outcomes. The most frequently occurring outcomes and impacts were knowledge acquisition/content understanding and affective and motivational outcomes. The range of indicators and measures used in the included papers are discussed, together with methodological limitations and recommendations for further work in this area.