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This paper describes an approach to the use of citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient. [ABSTRACT FROM AUTHOR]

TSP is a problem finding out the shortest distance out of possible courses where one starts a certain city and turns back to a starting city, visiting every city only once among N cities. This paper proposes the new method using both population initialization and sequential transformation method at the same time and then proves the improvement of capability by comparing them with existing methods. Keywords: Genetic Algorithm, GA, TSP, Initialization. [ABSTRACT FROM AUTHOR]

The neuron based nonlinear PID control method for plant with uncertainties is proposed in this paper. In this control system, the neuron based nonlinear PID controller is constructed by selecting the neuron inputs, and the control action of the nonlinear PID controller are determined by modifying the neuron weights on-line. The neuron based nonlinear PID controller produces the control signal in model-free way. With an example of the basis weight control of a paper machine, the experiments are made. The simulation results show that the neuron model-free controller has good control performance, fast transient response and strong robustness. [ABSTRACT FROM AUTHOR]

This paper proposes the canonical representation of multiplication operation on trapezoidal fuzzy numbers using the L-1-R-1 Inverse Function Arithmetic Representation method. Finally, the canonical representation proposed in this paper is applied to solve a fuzzy multiple criteria decision making problem of selection of plant location. [ABSTRACT FROM AUTHOR]

This paper presents an economical inspired intelligent approach for modeling learners in learning systems. Decision making in complex systems like e-learning systems requires processing of large amounts of heterogeneous data and information from dispread sources. Moreover, most of the decision parameters are incomplete and uncertain. Lacking of a complete model of learner is the prominent problem of current learning systems. In this paper, a market based method for describing Learner's preferences to the learning system is provided. The proposed approach strives for applying a Dempster-Shafer decision making over a society of self motivated agents. It tries to present a final learner agent with a high degree of similarity to the user for the purpose that it can act as a model of learner through the system. An implicit learning is also implemented by the idea of Stocks in real markets which can improve decision making efficiently. Keywords: Intelligent agent, personalizing, user modeling, Marker based systems. [ABSTRACT FROM AUTHOR]

The goal of this paper is to provide a visual structure to view the temporal and thematic content of English news documents. This paper combines both information extraction techniques and visualization techniques to visualize the documents content. Topical information present in the documents are extracted by applying Booksteins model and by calculating the various measures associated with the model. Temporal information is extracted as date information from the documents. Later the extracted information is converted into a signal and both discrete and continuous wavelet transfor-mations are applied over the signal. The content of the document is visualized at varying levels of detail by multi resolution analysis techniques available in wavelets. This gives a wavelet based visualization model which enables us to view the spread of topics across a document, the portions of the document that are most involved in topic description, and the contribution of documents in the corpus from the temporal perspective. [ABSTRACT FROM AUTHOR]

This paper presents an efficient approach to achieve fast and accurate face detection in still gray level images. The structure of eye region is used as a robust cue to find possible eye pairs. Candidates of eye pair at different scales are discovered by finding regions which roughly match with the binary eye pair template. To obtain real ones, all the eye pair candidates are then verified by using SVM. Faces are finally located according to the eyes position. The proposed method is robust to deal with illumination changes, moderate rotations, glasses wearing and partial face occlusions. The proposed method is evaluated on the BioID face database. Comparative experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, a robust image segmentation method is proposed. The relationship between pixel intensities and distance between pixels are introduced to the traditional neighbourhood potential function To perform an unsupervised segmentation, the Bayes Information Criterion (BIC) is used to determine the class number, the K-means is employed to initialise the classification and calculate the mean values and variances of the classes. The segmentation is transformed to maximize a posteriori(MAP) procedure. Then, the Iterative Conditional Model (ICM) is employed to solve the MAP problem. In the experiments, the proposed method is compared with other segmentation techniques, for noisy image segmentation applying on synthetic and real images. The experiment results shows that the proposed algorithm is the better choice. [ABSTRACT FROM AUTHOR]

Multi-document summarization has become a key technology in natural language processing. This paper proposes a strategy for Chinese multi-document summarization based on clustering and sentence extraction. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization. [ABSTRACT FROM AUTHOR]

In this paper, we present a real-time face detection method based on hybrid neural networks. We propose a modified version of fuzzy min-max (FMM) neural network for feature analysis and face classification. A relevance factor between features and pattern classes is defined to analyze the saliency of features. The measure can be utilized for the feature selection to construct an adaptive skin-color filter. The feature extraction module employs a convolutional neural network (CNN) with a Gabor transform layer to extract successively larger features in a hierarchical set of layers. In this paper we first describe the behavior of the proposed FMM model, and then introduce the feature analysis technique for skin-color filter and pattern classifier. [ABSTRACT FROM AUTHOR]

Kernel Matching Pursuit Machine (KMPM) is a relatively new learning algorithm utilizing Mercer kernels to produce non-linear version of conventional supervised and unsupervised learning algorithm. But the commonly used Mercer kernels can't expand a set of complete bases in the feature space (subspace of the square and integrable space). Hence the decision-function found by the machine can't approximate arbitrary objective function in feature space as precise as possible. Multiresolution analysis (MRA) shows promise for both nonstationary signal approximation and pattern recognition, so we combine KMPM with multiresolution analysis technique to improve the performance of the machine, and put forward a MRA shift-invariant kernel, which is a Mercer admissive kernel by theoretical analysis. An MRA kernel matching pursuit machine (MKMPM) is constructed in this paper by Shannon MRA shift-invariant kernel. It is shown that MKMPM is much more effective in the problems of regression and pattern recognition by a large number of comparable experiments. [ABSTRACT FROM AUTHOR]

To complete missing values, a solution is to use attribute correlations within data. However, it is difficult to identify such relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation method in this paper. This approach aims at making optimal statistical parameters: mean, distribution function after missing-data are imputed. We refer this approach to parameteroptimization method (POP algorithm, a random regression imputation). We experimentally evaluate our approach, and demonstrate that our POP algorithm is much better than deterministic regression imputation in efficiency of generating an inference on the above two parameters. The results also show our algorithm is computationally efficient, robust and stable for the missing data imputation. [ABSTRACT FROM AUTHOR]

In recent years, the support vector machine (SVM) has been extensively applied to deal with various data classification problems. However, it has also been observed that, for some datasets, the classification accuracy delivered by the SVM is very sensitive to how the cost parameter and the kernel parameters are set. As a result, the user may need to conduct extensive cross validation in order to figure out the optimal parameter setting. How to expedite the model selection process of the SVM has attracted a high degree of attention in the machine learning research community in recent years. This paper proposes an advanced data reduction algorithm aimed at expediting the model selection process of the SVM. Experimental results reveal that the proposed mechanism is able to deliver a speedup of over 70 times without causing meaningful side effects and compares favorably with the alternative approaches. [ABSTRACT FROM AUTHOR]

Customer relationship management (CRM) is one of the biggest concerns in a telecom company. Application of data mining can support telecom CRM effectively, and a systematical architecture of data mining has been needed to support every aspects of telecom CRM roundly. To solve this problem, the systematical application architecture of data mining in telecom CRM is established and components of five modules in the architecture are specified in this paper. Data mining algorithms based-on swarm intelligence improved by our own have been adopted in these modules. SIMiner, a self-development data mining software system based on swarm intelligence, is applied in this architecture. Finally, an application example is given to illuminate that telecom companies can make marketing strategies roundly and effectively with the support of the application architecture. [ABSTRACT FROM AUTHOR]

In this paper, we argue that the insertion of domain knowledge into ensemble of diverse evolutionary checkers can produce improved strategies and reduce evolution time by restricting search space. The evolutionary approach for game is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages, so that it is not sometimes efficient to evolve simple heuristic that is found easily by humans because it is based purely on a bottom-up style of construction. In this paper, we have proposed the systematic insertion of opening knowledge and an endgame database into the framework of evolutionary checkers. Also, common knowledge, the combination of diverse strategies is better than the single best one, is inserted into the middle stage and is implemented using crowding algorithm and a strategy combination scheme. Experimental results show that the proposed method is promising for generating better strategies. [ABSTRACT FROM AUTHOR]

Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine break down, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralized manufacturing environment to accommodate changes dynamically. In this paper, A new class of Time Petri Nets(TPN), Buffer-nets, for defining a Scheduling Holon is proposed, which enhances the modeling techniques for manufacturing systems with features that are considered difficult to model. The proposed novel GA algorithm performs the population alternation according to the features of the evolution of the populations in natural. Simulation results show that the proposed GA is more efficient than standard GAs. The proposed HPGA synthesizes the merits in both PSO and GA. The simulation results of the example show that the methods to scheduling holon are effective for fulfilling the scheduling problem. [ABSTRACT FROM AUTHOR]

The Traveling Salesman Problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-hard, and is an often-used benchmark for new optimization techniques.This paper will to bring up a three-tier multi-agent approach for solving the TSP. This proposed approach supports the distributed solving to the TSP. It divides into three-tier (layer), the first tier is ant colony optimization agent and its function is generating the new solution continuously; the second-tier is genetic algorithm agent, its function is optimizing the current solutions group; and the third tier is fast local searching agent and its function is optimizing the best solution from the beginning of the trial. Ultimately, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of the processing time in the practical production, no idle flow shop scheduling problem with fuzzy processing time is introduced. The objective is to find a sequence that minimizes the mean makespan and the spread of the makespan by using a method for ranking fuzzy numbers. The particle swarm optimization (PSO) is a population-based optimization technique that has been applied to a wide range of problems, but there is little reported in respect of application to scheduling problems because of its unsuitability for them. In the paper, PSO is redefined and modified by introducing genetic operations such as crossover and mutation to update the particles, which is called GPSO and successfully employed to solve the formulated problem. Several benchmarks with fuzzy processing time are used to test GPSO. Through the comparative simulation results with genetic algorithm, the feasibility and effectiveness of the proposed method are demonstrated. [ABSTRACT FROM AUTHOR]

There usually exist diverse variations in face images taken under uncontrolled conditions. Most previous work on face recognition focuses on particular variations and usually assume the absence of others. Such work is called controlled face recognition. Instead of the ‘divide and conquer' strategy adopted by controlled face recognition, this paper presents one of the first attempts directly aiming at uncontrolled face recognition. The solution is based on Individual Stable Neural Network (ISNN) proposed in this paper. ISNN can map a face image into the so-called Individual Stable Space (ISS), the feature space that only expresses personal characteristics, which is the only useful information for recognition. There are no restrictions for the face images fed into ISNN. Moreover, unlike many other robust face recognition methods, ISNN does not require any extra information (such as view angle) other than the personal identities during training. These advantages of ISNN make it a very practical approach for uncontrolled face recognition. In the experiments, ISNN is tested on two large face databases with vast variations and achieves the best performance compared with several popular face recognition techniques. [ABSTRACT FROM AUTHOR]

Protein kinases, a family of enzymes, have been viewed as an important signaling intermediary by living organisms for regulating critical biological processes such as memory, hormone response and cell growth. The unbalanced kinases are known to cause cancer and other diseases. With the increasing efforts to collect, store and disseminate information about the entire kinase family, it not only leads to valuable data set to understand cell regulation but also poses a big challenge to extract valuable knowledge about metabolic pathway from the data. Data mining techniques that have been widely used to find frequent patterns in large datasets can be extended and adapted to kinase data as well. This paper proposes a framework for mining frequent itemsets from the collected kinase dataset. An experiment using AMPK regulation data demonstrates that our approaches are useful and efficient in analyzing kinase regulation data. [ABSTRACT FROM AUTHOR]

There still exist two key problems required to be solved in the classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems. [ABSTRACT FROM AUTHOR]

This paper provides a framework based on temporal defeasible logic to reason about deliberative rule-based cognitive agents. Compared to previous works in this area our framework has the advantage that it can reason about temporal rules. We show that for rule-based cognitive agents deliberation is more than just deriving conclusions in terms of their mental components. Our paper is an extension of [5,6] in the area of cognitive agent programming. [ABSTRACT FROM AUTHOR]

Most techniques for probabilistic reasoning focus on reasoning about conditional probability constraints. However, human experts are accustomed to representing uncertain knowledge in the form of expectation rather than probability distribution directly in many cases. It is necessary to provide a logic for encoding hybrid probabilistic knowledge bases that contain expectation knowledge as well as the purely probabilistic knowledge in the form of conditional probability. This paper constructs a nonmonotonic logic for reasoning about hybrid probabilistic knowledge bases. We extend the propositional logic for reasoning about expectation to encoding hybrid probabilistic knowledge by introducing the conditional expectation constraint formula. Then we provide an approach to nonmonotonic reasoning about hybrid probabilistic knowledge bases. Finally,we compare this logic with related works. [ABSTRACT FROM AUTHOR]

In this paper, a new GA-based constructive algorithm is proposed for wavelet neural networks. Wavelets will be added to the WNNs from low resolution level to high resolution level. At each resolution, the translation parameters of a new wavelet is trained using GA, and output weights is obtained using least square techniques. The proposed algorithm is suitable to the high dimensional problems. [ABSTRACT FROM AUTHOR]

Enterprise data warehousing technology aims at providing integrated, consolidated and historical data for users to analyze businesses and make decisions. In order to obtain the correct results, the high data quality is required. In this paper, we analyze the quality problems of enterprise data warehouse and present an object-oriented framework for data quality management. In this framework, an object-oriented data quality model (OODQM) is built. The data quality requirements, the participators, the data quality checking object, and the possible data quality problems, form the core components of OODQM. The method we provide is a goal-driven method. Once the data quality goal is built, we manage data quality by the interaction of those components of OODQM. [ABSTRACT FROM AUTHOR]

In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration. We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images. [ABSTRACT FROM AUTHOR]

Most of the image-based traffic monitoring system (ITMS) adopts auto iris lens to control the amount of incoming light to camera. Auto iris mechanism measures total light energy in camera's field of view (FOV) and controls iris opening mechanically and inversely proportional to the light energy perceived. Thus, under counterlight, it causes the reduction of incoming light to produce dark scene where brighter one is desirable. To overcome this difficulty, some camera provides a function to define a region of interest (ROI) in FOV and measures light energy only in ROI. Thus, if we leave out counterlight area from ROI, the iris may properly be controlled. However, in ITMS, it frequently happens that large vehicle with white or black roof passes under camera, covers most of the FOV, and results in undesirable iris change. In this paper, we suggest a new iris control mechanism, called user-controlled iris (UCI), in which iris control depends only on background brightness. Since UCI is not sensitive to counterlight or foreground object's brightness, it can maintain the optimal environment for vehicle detection for ITMS. [ABSTRACT FROM AUTHOR]

Owing to the highly complicated nature and the escalating cost involved in construction claims, it is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to predict the outcome of construction claims in Hong Kong. The advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the conventional PSO algorithm, it attains a higher accuracy in a much shorter time. [ABSTRACT FROM AUTHOR]

Traditional statistical approaches for identifying multi-word terms have to handle a large amount of noisy data and are extremely time consuming. This paper introduces a multi-word term extraction system for extracting multi-word terms from a set of documents based on the co-related text-segments existing in these documents. The system uses a short predefined stoplist as an initial input to segment a set of documents into text-segments, calculates the segment-weights of all text-segments, and then applies the short text-segments to segment the longer text-segments based on the weight values recursively until all text-segments cannot be further divided. The resultant text-segments can thus be identified as terms based on a specified threshold. The initial experimental result on a set of traditional Chinese documents shows that this system can achieve a minimum of 76.39% of recall rate and a minimum of 91.05% of precision rate on retrieving multiple occurrences terms, which include 18.30% of new identified terms. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation(WSD) is one of the traditionally most difficult problems in natural language processing and has broad theoretical and practical implications. One of the main difficulties for WSD systems is the lack of relevant knowledge-commonly known as the knowledge acquisition bottleneck problem. We present in this paper a novel method that utilizes dynamic Web data obtained through Web search engines to effectively enrich the semantic knowledge for WSD systems. We demonstrated through a word sense disambiguation system the large quantity and good quality of the extracted knowledge. [ABSTRACT FROM AUTHOR]

This paper presents a biologically-inspired method of perceiving the TROIs(: Texture Region Of Interest) from various texture images. Our approach is motivated by a computational model of neuron cells found in the primary visual cortex. An unsupervised learning schemes of SOM(: Self-Organizing Map) is used for the block-based image clustering, plus 2D spatial filters referring to the response properties of neuron cells is used for extracting the spatial features from an original image and segmenting any TROI from the clustered image. To evaluate the effectiveness of the proposed method, various texture images were built, and the quality of the extracted TROI was measured according to the discrepancies. Our experimental results demonstrated a very successful performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a noble classification algorithm for content-based audio signal retrieval. The algorithm uses the Gradient-Based Fuzzy C-Means with a Mercer Kernel (GBFCM(MK)) to perform clustering of Gaussian Probability Density Function (GPDF) data of a Gaussian Mixture Model (GMM). The GBFCM(MK) algorithm incorporates a kernel method into the GBFCM to implicitly perform nonlinear mapping of the input data into a high-dimensional feature space. Experiments and results for several audio data sets have shown that the GBFCM(MK)-based classification algorithm has accuracy improvements of 3.14%-7.49% over classification algorithms employing the traditional k-means and the Fuzzy C-Mean (FCM), respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a multiple SVMs enabled sales forecasting support system (SFSS). The SFSS has a two-stage system architecture. In the first stage, agglomerative hierarchical clustering(AHC) is used to partition the goods into several patterns based on similarity measure. In the second stage, multiple SVMs that best fit partitioned patterns are constructed by finding the appropriate kernel function and the optimal free parameters of SVMs. The experiment shows that this integrated system achieves significant improvement in forecasting performance compared with single SVMs models. [ABSTRACT FROM AUTHOR]

This paper proposes a color-shape based method (CSBM) based on color, area, and perimeter intercepted lengths of segmented objects in an image. It characterizes the shape of an object by the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center. The experimental results show that CSBM provides a better performance than fuzzy color histogram (FCH) and conventional color histogram (CCH). Besides, it is insensitive to translation, rotation, distortion, scaling, and hue variations, but impressionable to contrast and noise variations. Keywords: Content-based image retrieval, Perimeter intercepted length, Fuzzy c-mean histogram (FCH), Conventional color histogram (CCH). [ABSTRACT FROM AUTHOR]

Based on energy distribution analysis of FRIT coefficients, a novel feature extraction method of low computation complexity in FRIT domain was proposed for texture classification in this paper. A ‘one-against-one' multi-class SVM with RBF kernel was adopted as classifier. Experiments carried out on abundant texture databases with varying sizes demonstrated its validity. [ABSTRACT FROM AUTHOR]

A design of the fingerprint capture system based on chip FPS200 is introduced. The capture system uses the MCU interface with DSP. In this paper, a new method based on the intrinsic directional features of fingerprint is proposed to evaluate the quality of images acquired by FPS200 sensor. Using directional map, the available area of image and clearness is checked to calculate the overall image quality score that can be used to quantitatively determine the quality of the fingerprint image. If the quality of image is unsatisfied, prompt is presented: whether the finger is wet or dry, then DSP adjusts the parameters of FPS200 sensor automatically until image of good quality has been acquired. Experimental results show that the performance of AFIS has indeed improved. Keywords: FPS200 sensor, directional map, prominent direction, image quality evaluation. [ABSTRACT FROM AUTHOR]

A new methodology on the algorithm of sequential minimal optimization (SMO) for power system load was presented. In order to solve the problem that support vector machines (SVM) can not deal with large scale data, this paper introduces the modified algorithm of SMO to increase operational speed by use of a single threshold value. Adopting the actual data from the distribution network of a certain domestic city, and the load is forecasted by use of support vector regression (SVR) which is based on the modified SMO algorithm and proper kernel function. The forecasted results are compared with those SVR employing quadratic programming (QP) optimization algorithm and BP artificial neural method, and it is shown that the presented forecasting method is more accurate and efficient. Keywords: Support vector machine; Sequential minimal optimization; kennel function; load forecasting. [ABSTRACT FROM AUTHOR]

A natural question in Information Bottleneck method is how many "groups" are appropriate. The dependency on prior knowledge restricts the applications of many Information Bottleneck algorithms. In this paper we aim to remove this dependency by formulating the parameter choosing as a model selection problem, and solve it using the minimum message length principle. Empirical results in the documentation clustering scenario indicates that the proposed method works well for the determination of the optimal parameter value for information bottleneck method. [ABSTRACT FROM AUTHOR]

Many existing plagiarism detection systems fail in detecting plagiarism when there are an abundant garbage in the copied programs. This is because they do not use the structural information efficiently. In this paper, we propose a novel plagiarism detection system which uses parse tree kernels. By incorporating parse tree kernels into the system, it efficiently handles the structural information within source programs. A comparison with existing systems such as SID and JPlag shows that the proposed system can detect plagiarism more accurately due to its ability of handling structural information. [ABSTRACT FROM AUTHOR]

In analysis of the market, detecting not only differences in consumer groups or changes but also their causal factors observed in consumer behavior is expected because it enables the marketer to take marketing actions. Although rule-discovery approaches can efficiently identify differences in groups or changes, it is still difficult to explain the causes of them. In this paper we propose an algorithm to detect causal differences in two bayesian networks by search and probability inference. We perform some experimental studies to analyze consumer behavior in purchasing personal computer. [ABSTRACT FROM AUTHOR]

In computer games, the level design and balance of character attributes are the key features of interesting games. Level designers adjust the attributes of the game characters and opponent behavior to create appropriate levels of difficult, and avoid player frustration. Generally, opponent behavior is defined by a static script, however, this results in repetitive levels and environments, making in difficult to maintain the player's interest. Accordingly, this paper proposes a dynamic scripting method that can sustain the degree of interest intended by the level designer by adjusting the opponent behaviors while playing the game. The player's countermeasure pattern for dynamic level design is modeled using a Gaussian Mixture Model (GMM). The proposed method is applied to a shooter game, and the experimental results maintain the degree of interest intended by the level designer. [ABSTRACT FROM AUTHOR]

This paper proposes SV-kNNC, a new algorithm for k-Nearest Neighbor (kNN). This algorithm consists of three steps. First, Support Vector Machines (SVMs) are applied to select some important training data. Then, k-mean clustering is used to assign the weight to each training instance. Finally, unseen examples are classified by kNN. Fourteen datasets from the UCI repository were used to evaluate the performance of this algorithm. SV-kNNC is compared with conventional kNN and kNN with two instance reduction techniques: CNN and ENN. The results show that our algorithm provides the best performance, both predictive accuracy and classification time. [ABSTRACT FROM AUTHOR]

In this paper, we incorporate the insurance concept in buying and selling model for agents to trade in the open multi-agent marketplace. During buying, agents purchase insurance as a method to search for potential sellers and select their partners based on the information provided by insurance agents. During selling, agents purchase insurance as a method to protect themselves against potential risk. The insurance concept greatly simplifies the trading procedure in the open marketplace. The novelty of this proposal is that it ensures a dynamic trading environment while agents continue to seek maximum utility and being fully protected by insurance. Our experimental results confirm the feasibility of our approach. [ABSTRACT FROM AUTHOR]

An application of Immune Genetic Algorithm (IGA) suitable for the optimal design to intelligent AC contactors is presented in this paper. Besides the ability of stochastic global searching of Simple Genetic Algorithm (SGA), the IGA draws into the mechanisms exist in biological immune system such as immune memory, immune regulation, antibody diversity and others. The simulation results show that IGA overcomes the disadvantages of premature convergence of SGA, and improve the global searching efficiency and capability. This algorithm has been successfully used in the optimal design to the intelligent AC contactors. Keywords: Evolutionary computing, optimal design, contactor. [ABSTRACT FROM AUTHOR]

There are two main objectives for this paper : (1) we will propose a more general class of consecutive-component systems which generalizes both the typical consecutive-k-out-of-n:F systems and two-dimensional consecutive-k- out-of-n:F systems, (2) we will propose an immune algorithm to investigate the optimal maintenance policy for the proposed consecutive-component systems. Numerical results are reported and compared with those of implicit enumeration. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive inventory control model for a supply chain consisting of one supplier and multiple retailers with nonstationary customer demands. The objective of the adaptive inventory control model is to minimize inventory related cost. The inventory control parameter is safety lead time. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a statistical distribution is not a prerequisite in this model. Instead, using a reinforcement learning technique called action-reward based learning, the control parameter is designed to adaptively change as customer demand pattern changes. A simulation based experiment was performed to compare the performance of the adaptive inventory control model. [ABSTRACT FROM AUTHOR]

The Semiring Constraint Satisfaction Problem (SCSP) framework is a popular approach for the representation of partial constraint satisfaction problems. In this framework preferences (semiring values) can be associated with tuples of values of the variable domains. Bistarelli et al. [1] define an abstract solution to a SCSP which consists of the best set of solution tuples for the variables in the problem. Sometimes this abstract solution may not be good enough, and in this case we want to change the constraints so that we solve a problem that is slightly different from the original problem but has an acceptable solution. In [2] we propose a relaxation of a SCSP where we define a measure of distance (a semiring value from a second semiring) between the original SCSP and a relaxed SCSP. In this paper we show how the two semirings can be combined into a single semiring. This combined semiring structure will allow us to use existing tools for SCSPs to solve Combined Semiring Relaxations of SCSPs. At this stage our work is preliminary and needs further investigation to develop into a useful algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we describe our approach to achieve accurate localization using RFID for Cellular Phone Robot (CPR). We solely rely on RFID mechanism and complement coordinate errors accumulated during the wheel-based CPR navigation. We especially focus on how to distribute RFID tags (tag pattern) and how many to place (tag granularity) on the floor to accomplish efficient navigations. We define the error in navigation and use it to compare the effectiveness of various RFID floor settings through a simulation. Identified tag patterns and granularities would be useful to many USN applications where the adoption of RFID technology is appropriate. [ABSTRACT FROM AUTHOR]

To enhance the performance of evolutionary algorithms, different parallel computation models have been proposed, and they have been implemented on parallel computers to speed up the computation. Instead of using expensive parallel computing facilities, in this paper we propose to implement parallel evolutionary computation models on easily available networked PCs, and present a multi-agent framework to support parallelism. To evaluate the proposed approach, different kinds of experiments have been conducted to assess the developed system and the preliminary results show the efficiency of our approach. [ABSTRACT FROM AUTHOR]

In this paper we present LP-TPOP, a domain-independent temporal partial-order planning algorithm that handles CBI-style action model. By the utilization of ground actions variable binding constraints can be eliminated from partial plans, then all the constraints needed to be handled are temporal constraints, which are maintained in a revised simple temporal constraint network (r-STN) structure. The exact scheduling of plan returned by planning phase is calculated by a linear programming module. By exploiting incremental algorithms the efficiency of LP-TPOP can be further improved. LP-TPOP is proved to be sound and complete. [ABSTRACT FROM AUTHOR]