Search

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected world. As a result, there is a massive flow of data in different sectors of human activity. One example is intelligent buildings, where thousands of components, devices, systems and suppliers interact. In this context, failures in control and monitoring systems are frequent. To analyze this situation, this paper presents as a case study the problem of fault-tolerant robust adaptive monitoring control with state prediction performance for a class of IoT temperature systems subject to uncertainties of precision states and external disturbances. The authors propose a new control strategy based on consensus game theory and prediction of future precision states to reduce tracking error and improve algorithm efficiency. The authors present the development of a new algorithm that improves the functioning of monitoring and control of parcel networks. This has the purpose of increasing the energy efficiency of the same and ensure the effectiveness of our adaptive temperature control algorithm, compared to existing results. With the simulation presented in this research, it is possible to conclude that a new fault tolerant error tracking algorithm ensures robust monitoring of the reference model. It was shown that the predicted temperature signal is limited by a small range close to the collected temperature data. A case study result is provided to demonstrate the effectiveness of the proposed fault-tolerant adaptive monitoring control algorithm. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find an adaptive control strategy which would enable us to estimate the parameters of the ARMAX model as accurately as possible, along with consuming less controlling energy, while keeping the output of the system below a specified level of variability. Using the self-tuning tracker, this paper establishes global convergence of a stochastic adaptive control algorithm for discrete linear system, and the adaptive controller may converge to the one-step-ahead optimal controller at the same time. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study of several well-known and thoroughly tested techniques for the segmentation of textured images, including two algorithms belonging to the adaptive Bayesian family of restoration and segmentation methods, and a novel approach based on the recently introduced concept of the frequency histogram of connected elements (FHCE). The paper first introduces the parameters that define a connected element and then details the sensitivity analysis of these parameters, showing that the grayscale intensity histogram of a digital image is a particular case of the FHCE. The application domain chosen for comparison purposes is the problem of medical images segmentation and, more specifically, as a particularly illustrative case the segmentation of digital angiograms is analyzed in detail. To get a comparative evaluation of FHCE performance, two well-established adaptive or contextual Bayesian segmentation algorithms have been applied to the segmentation of digital angiograms as well. The paper ends with a brief discussion of the comparative performances. [ABSTRACT FROM AUTHOR]

In this paper, we propose the DoubleFace algorithm to infer peers' reputation rankings, which explicitly includes two phases. First is the computation of peers' Recommendation Ability (RA) based on the following intuitive idea: Peer's RA will be reversely determined by how many peers it points to and how bad those peers have been rated, which comprise two subroutines: the badness propagation and the conversion of RA from badness. In the first subroutine, we thoroughly consider the effect of sybils' attack on badness propagation, which has been investigated in previous work rarely; in the second subroutine, we design two methods to convert the badness into peer's RA: the disproportional way and the logistic way. The second phase is the integration of RA into adjacent matrix used to represent P2P trust graph, to reflect peer's RA in trust propagation. The simulation results show that the disproportional and logistic DoubleFace can be robust against sybils' and front peers' attacks to reputation ranking and can achieve significant performance improvement in comparison with Eigentrust-like algorithms, in hostile P2P environments. Moreover, we also discuss the effect of hostile and hospitable P2P environments on the performance of DoubleFace. Specifically, disproportional DoubleFace can perform slightly better than logistic DoubleFace in hostile P2P environments, but, in hospitable P2P environments, disproportional DoubleFace performs anti-intuitively poorly, and logistic DoubleFace achieves the same performance as traditional Eigentrust-like algorithms. [ABSTRACT FROM AUTHOR]

This paper presents the application of data mining algorithms to the prediction of Web performance. Our domain-driven data mining uses historic HTTP transactions data reflecting Web performance as perceived by the end-users located in the Internet domain of Wroclaw University of Technology, Wroclaw, Poland. The predictive modeling features of two general data mining systems, Microsoft SQL Server and IBM Intelligent Miner, are compared. The neural networks, decision tree, time series, and transform regression models are evaluated. It is shown that the data mining algorithms return quite accurate prediction results. The best results are achieved using the IBM's transform regression algorithm. [ABSTRACT FROM AUTHOR]

Despite the existence of a number of variations of genetic algorithms for the traveling salesman problem in literature, no efforts have been made to the best of our knowledge to characterize their performance. This paper presents a detailed comparative study on some of the solution methods for the traveling salesman problem using genetic algorithms. All the operators of genetic algorithms have been given equal emphasis in the analysis. The complete simulation has been done using a number of C programs written by us for this purpose. A permanent network has been particularly considered for this task. The results shed insight on the best solution method for the problem. However, we cannot conclude how the behavior of those algorithms depends on network topology. [ABSTRACT FROM AUTHOR]

In this paper we first present a novel approach to determine the structural information content (graph entropy) of a network represented by an undirected and connected graph. Such entropic measures can be very important and useful to analyze and compare complex systems by means of networks. The novel graph entropy definition is based on local vertex functionals obtained by calculating j-spheres via the algorithm of Dijkstra. We state some lower and upper bounds of the defined graph entropy to estimate the structural information content for graph classes or explicitly given graphs. Second, we present a detailed example for calculating the graph entropies of a special graph class. [ABSTRACT FROM AUTHOR]

Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches are specially suited to learn from data by representing knowledge as density functions. In this paper, we choose Gaussian mixture models (GMMs) to represent densities, as they possess great flexibility to adequate to a wide class of problems. The classical estimation approach for GMMs corresponds to the iterative algorithm of expectation maximization (EM). This approach, however, does not scale properly to meet the high demanding processing requirements of large databases. In this paper we introduce an EM-based algorithm, that solves the scalability problem. Our approach is based on the concept of data condensation which, in addition to substantially diminishing the computational load, provides sound starting values that allow the algorithm to reach convergence faster. We also focus on the model selection problem. We test our algorithm using synthetic and real databases, and find several advantages, when compared to other standard existing procedures. [ABSTRACT FROM AUTHOR]

The use of traditional optimization approaches has reached a point of saturation in solving many complex optimization problems. However, the design of an efficient stochastic algorithm whose solution is close to optimal is still possible, albeit the complexity of such problems. These algorithms basically search through a space of potential solutions using randomness as a major factor in decision making. In any optimization process, the goal is to find the best solution. For small spaces, classical exhaustive search methods usually suffice. For larger spaces, special artificial intelligence techniques must be employed. Evolutionary algorithm models natural selection which generates new points in the search by applying operators to current points and statistically moving toward more optimal points in the search space. This paper describes the job sequencing (JS) problem and the evolutionary programming (EP) approach, and proposes an evolutionary algorithm (EA) that solves the JS problem. It also compares EA with the following methods: Shortest processing time (SPT), longest processing time (LPT) and greedy method (GM), and then demonstrates the effectiveness of EA in obtaining better results. [ABSTRACT FROM AUTHOR]

Web spam has become one of the most exciting challenges and threats to web search engines. The relationship between the search systems and those who try to manipulate them came up with the field of adversarial information retrieval. In this article, we set up several experiments to compare HostRank and TrustRank to show how effective it is for TrustRank to combat web spam, and we report a comparison on different link-based web spam detection algorithms. [ABSTRACT FROM PUBLISHER]

A method is proposed to analyze the effect of the algorithmic parameters and instance characteristics on the quality of a Pareto front produced by a multiobjective algorithm. This method is applied to a variable neighborhood tabu search that is used to solve a multiobjective microzone-based vehicle routing problem. Our method can accommodate many different performance indices for Pareto fronts and uses the Promethee multicriteria decision analysis method to select the best configuration based on the characteristics of the instance being solved. [ABSTRACT FROM AUTHOR]

This paper examines an Information Storage System (ISS), in which a vocabulary and set of experimental data are defined, on the basis of which both linear and linearized (meaning that they are treated in linear fashion) mathematical models are generated. The studies obtained lead to establish certain objective criteria that let us select the best characters to observe and establish a methodology, which allows us to select the mathematical functions that best represent a given system. The studies provide the theoretical basis for obtaining computational algorithms that allow the computational implementation of model selection. [ABSTRACT FROM AUTHOR]

The pervasive sensing technologies found in smart environments offer unprecedented opportunities for monitoring and assisting the individuals who live and work in these spaces. An aspect of daily life that is important for one's emotional and physical health is social interaction. In this paper we investigate the use of smart environment technologies to detect and analyze interactions in smart spaces. We introduce techniques for collecting and analyzing sensor information in smart environments to help in interpreting resident behavior patterns and determining when multiple residents are interacting. The effectiveness of our techniques is evaluated using two physical smart environment test beds. [ABSTRACT FROM AUTHOR]

While there has been a variety of research focusing on ontology integration based on simple techniques (e.g., element- or structure-level techniques), the hybrid approaches combining the simple techniques have not been explored. In this paper we describe a hybrid method to integrate multiple ontologies in several levels, such as the element level, internal structure, and relational structure. A semantic supporting environment (SSE) combining special domains (e.g., WordNet) and text corpus are defined in the proposed approach. An enriched ontology model (EOM) has been proposed to reduce the initial complexity of the process of ontology integration. Subsequently, the semantic network called OnConceptSNet is provided. The relations between the concepts in the OnConceptSNet are derived from the SSE. An enhanced algorithm (EA) has been proposed to enhance OnConceptSNet. [ABSTRACT FROM AUTHOR]

In recent years, some methods have been proposed to estimate the null values in relational database systems (Chang and Chen 2006; Chen and Chang 2008; Chen and Hsiao 2005; Chen and Huang 2003, 2008; Chen and Lee 2005; Cheng and Wang 2006). In this paper, we present a new method for estimating the null values in relational database systems having negative dependency relationships between attributes. First, we apply the automatic clustering algorithm we presented in Chang and Chen (2006) for clustering the tuples in the relational database. Based on the clustering results and multiple regression techniques (Kvanli et al. 1986), we present a new method for estimate null values in relational database systems having negative dependency relationships between attributes, where the “Benz secondhand car database” (Chen and Huang 2008) is used for the experiment. The experimental results show that the proposed method gets a higher average estimated accuracy rate than Chen and Huang's method (2003, 2008) for estimating null values in relational database systems having negative dependency relationships between attributes. [ABSTRACT FROM AUTHOR]

Swarm intelligence, as demonstrated by a natural biological swarm, such as an ant colony, has many powerful properties that are desirable for effective routing in communications networks. In this paper, we propose an intelligent routing algorithm that we are calling Fuzzy Logic Ant-based Routing (FLAR), which is inspired by ant colonies and enhanced by fuzzy logic techniques. Using a fuzzy system as an intelligent and expert mechanism allows multiple constraints to be considered in a simple and intuitive way. Simulation results and a comparison of the proposed method with two state-of-the-art routing algorithms show better performance and a higher fault tolerance for our approach, particularly in regard to link failures. [ABSTRACT FROM AUTHOR]

Fuzzy classification systems are important applications of the fuzzy set theory. In order to design a fuzzy classification system, it is an important task to construct the membership function of each attribute and generate fuzzy rules that are suitable for handling a specific classification problem. In this paper, we propose a new method to construct the membership function of each attribute and generate weighted fuzzy rules from training instances for handling fuzzy classification problems. The proposed method can construct membership functions and generate weighted fuzzy rules without any human experts' intervention. It can get a higher average classification accuracy rate and generate fewer fuzzy rules than the existing methods. [ABSTRACT FROM AUTHOR]

In this study, an efficient and reliable algorithm known as Jaya Algorithm-enabled Flower Pollination Algorithm (JA-FPA) is introduced for enhancing the Optimal Power Flow (OPF) and system security by a multi objective model. The multi-objective model is first derived based on entropy of individual objectives such as the Voltage Stability Index (VSI), Power Loss Index (PLI), Line Collapse Proximity Indexes (LCPI), severity and TCSC cost. Multi- objectives are achieved by optimizing the location as well as size of Flexible Alternating Current Transmission System (FACTS) devices in the test bus system. Consequently, the most popular and ubiquitous component of the FACTS equipment category, the Thyristor Controlled Series Capacitor (TCSC), is utilized in this study. The best solution with optimal sizing and allocation of TCSC devices for achieving the aforesaid objectives is chosen from the JA-FPA optimized outputs. The proposed model is performed on two benchmark test bus systems. The adopted model is distinguished with conventional models, and the results are obtained to analyze its effectiveness. In particular, the computational time of proposed model is is 1.74%, 2.52%, 1.13%, 0.63%, 50.15%, 2.36%, 3.83%, 6.00%, 2.79%, and 7.57% superior to existing GWO, WOA, DA, FPA, JA, PSO + GSA, PSO, Henry gas, GA, and TLBO approaches. [ABSTRACT FROM AUTHOR]

In this paper we describe the ILA-2 rule induction algorithm, which is the improved version of a novel inductive learning algorithm ILA . We first outline the basic algorithm ILA, and then present how the algorithm is improved using a new evaluation metric that handles uncertainty in the data. By using a new soft computing metric, users can reflect their preferences through a penalty factor to control the performance of the algorithm. Inductive learning algorithm has also a faster pass criteria feature which reduces the processing time without sacrificing much from the accuracy that is not available in basic ILA. We experimentally show that the performance of ILA-2 is comparable to that of well-known inductive learning algorithms, namely, CN2, OC1, ID3, and C4.5. [ABSTRACT FROM AUTHOR]

Relations among temporal intervals can be used to assist the automatic generation of multimedia presentations. In this paper, we analyze the domains of interval temporal relations. A set of algorithms is proposed to derive reasonable relations between intervals. Possible conflicts in the user specification are first detected and eliminated. Our mechanism then constructs partial order relations among temporal intervals before the presentation time chart is built. The algorithm is extended for objects in an arbitrary n-dimensional space. Thus, presentation layouts in 2-D space or Virtual Reality object representations in 3-D space can be constructed. We use our algorithms to design alpha reasoning system that generates the schedule and layout of multimedia presentations. Finally, our contribution is completed with the application of STIMEVIS, a structured methodology of systemic analysis in the computation model we present, adapted in the process of generating multimedia presentations of user specifications. [ABSTRACT FROM AUTHOR]

An intelligent security agent for a reliable cipher system using a PingPong cipher is proposed. The system contains a security agent for security managements and services. The agent includes an intelligent selection of many security features: encryption processing speeds, encryption methods, encryption algorithms, synchronization methods, network interfaces, user authentications, security-bit levels, the size of synchronization patterns, etc. Furthermore, encryption algorithms with a PingPong-256, -192 and -128 for data confidentiality and a synchronization generator and detector with a SYNPAT-128, -112, -96, -80, and -64 for system stabilization are designed and analyzed. [ABSTRACT FROM AUTHOR]

Spam, also known as unsolicited bulk e-mail (UBE), has recently become a serious threat that negatively impacts the usability of legitimate mails. In this article, an evidential spam-filtering framework is proposed. As a useful tool to handle uncertainty, the Dempster–Shafer theory of evidence (D–S theory) is integrated into the proposed approach. Five representative features from an e-mail header are analyzed. With a machine-learning algorithm, e-mail headers with known classifications are used to train the framework. When using the framework for a given e-mail header, its representative features are quantified. Although in classical probability theory, possibilities are forcedly assigned even when information is not adequate, in our approach, for every word in an e-mail subject, basic probability assignments (BPA) are assigned in a more flexible way, thus providing a more reasonable result. Finally, BPAs are combined and transformed into pignistic probabilities for decision-making. Empirical trials on real-world datasets show the efficiency of the proposed framework. [ABSTRACT FROM AUTHOR]

The vertex coloring problem is a well-known classical optimization problem in graph theory in which a color is assigned to each vertex of the graph in such a way that no two adjacent vertices have the same color. The minimum vertex coloring problem is known to be an NP-hard problem in an arbitrary graph, and a host of approximation solutions are available. In this article, a learning automata–based approximation algorithm is proposed to solve the minimum vertex coloring problem. The proposed algorithm iteratively finds the different possible colorings of the graph and compares it at each stage with the best coloring found so far. If the number of distinct colors in the chosen coloring is less than that of the best coloring, the chosen coloring is rewarded; otherwise, it is penalized. Convergence of the proposed algorithm to the optimal solution is proven. The proposed vertex coloring algorithm is compared with the well-known coloring techniques and the results show the superiority of the proposed algorithm over the others both in terms of the color set size and running time of algorithm. [ABSTRACT FROM PUBLISHER]

This article proposes a novel approach to the radial basis function network (RBFN) design. Its main idea is to apply the agent-based population learning algorithm to the task of initialization and training RBFNs. The approach allows for an effective network initialization and estimation of its output weights. The initialization involves two stages, where in the first one initial clusters are produced using the similarity-based procedure and next, in the second stage, prototypes (centroids) from the thus-obtained clusters are selected. The agent-based population learning algorithm is used to select prototypes. In the proposed implementation of the algorithm, both tasks—RBFN initialization and RBFN training—are carried out by a team of agents executing various local search procedures and cooperating with a view to determine the solution to the RBFN design problem at hand. The performance of the RBFN constructed using the proposed agent-based approach is analyzed and evaluated. The proposed approach is also compared with different RBFN initialization and training procedures in the literature. [ABSTRACT FROM AUTHOR]

The aim of this article is to propose and evaluate an agent-based population learning algorithm generating, through prototype selection, a representative training data set of the required size. The main feature of the proposed approach is selection of the representative feature vectors, called prototypes, from clusters of feature vectors constructed over the original training data set under the assumption that from each cluster a single prototype is obtained. Thus, the number of clusters produced from the original training data set has a direct influence on the size of the reduced data set. The process of selection is executed by a team of agents, which execute various local search procedures and cooperate to determine a solution to the instance reduction problem, aiming at obtaining a compact representation of the data set. Rules for agent cooperation during the clustering and selection processes are defined within the so-called working strategy used by the team of agents (A-Team) in question. The article proposes a set of procedures that are used by agents to produce clusters and select prototypes. The approach is validated experimentally using well-known benchmark data sets. In addition to the computational experiment used to validate the model, the article investigates the efficiency and performance of two different working strategies used by the proposed A-Team. Because the proposed approach is based on the population learning algorithm, which belongs to the class of the population-based methods, an evaluation of the influence of the population of solution size on the performance of the algorithm is also included. [ABSTRACT FROM PUBLISHER]

Single robot hose transport is a limit case of linked multicomponent robotic systems, where one robot moves the tip of a hose to a desired position. The interaction between the passive, flexible hose and the robot introduces highly nonlinear effects in the system's dynamics, requiring innovative control design approaches, such as reinforcement learning. This article improves previous approaches to this problem by introducing a novel reinforcement learning algorithm (TRQ-learning) and a new system state definition for the autonomous derivation of the hose–robot control algorithm. Computational experiments based on accurate geometrically exact dynamic splines hose dynamics simulations show the improvement obtained. [ABSTRACT FROM PUBLISHER]

Moving vehicle detection and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a novel Marr wavelet, kernel-based background modeling method and a background subtraction method based on binary discrete wavelet transforms (BDWT) are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. The background and current frame are transformed by BDWT, and moving vehicles are detected in the binary discrete wavelet transforms domain. For the shortages of RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value) color space-based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space and edge detection is proposed. The original data of the shadow according to the characteristics of the YCbCr space is chosen, and then, combined with edge detection, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after detection and obtaining the center of the object. An actual road test shows that the algorithm can effectively remove the influence of pedestrians and cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring. [ABSTRACT FROM AUTHOR]

Reinforcement learning is one of the more prominent machine-learning technologies due to its unsupervised learning structure and ability to continually learn, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from the other agents in the system so the speed of learning can be accelerated. In the proposed learning algorithm, an agent adapts to comply with its peers by learning carefully when it obtains a positive reinforcement feedback signal, but should learn more aggressively if a negative reward follows the action just taken. These two properties are applied to develop the proposed cooperative learning method. This research presents the novel use of the fastest policy hill-climbing methods of Win or Lose Fast (WoLF) with policy-sharing. Results from the multi-agent cooperative domain illustrate that the proposed algorithms perform better than Q-learning alone in a piano mover environment. It also demonstrates that agents can learn to accomplish a task together efficiently through repetitive trials. [ABSTRACT FROM AUTHOR]

The map building problem is the most fundemental issue in intelligent mobile robot reasearch. Several map construction methods have been proposed, but most of them require a predefined path before the construction of an environment map. In this study, a novel concurrent path construction algorithm for map building problem in a completely unknown indoor environment is presented. In the concurrent path construction task, the main idea is based on a lookahead strategy that estimates the next location where the robot will go, using its latest state and range readings. A local map is derived from the readings of 2D range-finders and line segments constituting the local map are also used to update a global map. A significant feature of this study is that it does not reqire a predefined path for navigation to construct an environment map. Moreover, optimum sensor readings are aimed to decrease the data processing time needed for a complete map construction by reducing the number of range readings from the surroundings. Simulation results on widely used and custom indoor architectures demonstrate that the proposed approach generates highly efficient paths and global maps for all handled problem instances. [ABSTRACT FROM AUTHOR]

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This article is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The second outlines some current research within the ACO community, reporting recent results obtained on different problems, while the third part focuses on a particular research line, named ANTS, providing some details on the algorithm and presenting results recently obtained on a prototypical strongly constrained problem: the set partitioning problem. [ABSTRACT FROM AUTHOR]

Today's knowledge and content Web-based technologies call for adaptive and intelligent algorithms for accessing the resources, especially to respond to Semantic Web, as well to performance challenges. This work presents the application of two machine learning techniques in an adaptive decision making framework, namely a fuzzy logic and neural networks, to deploy the adaptive and intelligent dispatching algorithm for resource requesting within a geographically distributed fully replicated Web sites. Our approach and algorithm can be used in content distribution and delivery networks where due to content replication each Web server can respond to the client request. Intelligent redirection mechanism redirects the request from the originating site's server to the "best" content source to deliver the content in the fastest manner. First, we introduce a broker-based system architecture. Next, we develop a new global adaptive intelligent request distribution algorithm. Our algorithm called GARDiB by a means of the fuzzy-neural techniques redirects each incoming request to local service with the least expected response time, which takes into account, the transfer time of the request and the response, as well as the time elapsed on the local service that responds to the request. Our simulations showed the effectiveness of the proposed adaptive distribution. [ABSTRACT FROM AUTHOR]

As a very effective method for universal purpose pattern recognition, support vector machine (SVM) was proposed for dichotomic classification problem, which exhibits a remarkable resistance to overfitting, a feature explained by the fact that it directly implements the principle of structural risk minimization. However, in real world, most of classification problems consist of multiple categories. In an attempt to extend the binary SVM classifier for multiclass classification, decision-tree-based multiclass SVM was proposed recently, in which the structure of decision tree plays an important role in minimizing the classification error. The present study aims at developing a systematic way for the design of decision tree for multiclass SVM. Kernel-induced distance function between datasets was discussed and then kernelized hierarchical clustering was developed and used in determining the structure of decision tree. Further, simulation results on satellite image interpretation show the superiority of the proposed classification strategy over the conventional multiclass SVM algorithms. [ABSTRACT FROM AUTHOR]

Data mining techniques can be used to discover useful information by exploring and analyzing data. The aim of this article is to propose a new fuzzy-data mining method to find a compact set consisting of fuzzy if-then classification rules with high classification capability using the genetic algorithm. Furthermore, for not reducing the usefulness of the proposed method for classification problems with high dimensional feature space, the curse dimensionality resulting from the grid partition is overcome in the proposed method by employing the principal component analysis to reduce the dimensions. Through computer simulations, it can be seen that the proposed method is comparable to the other fuzzy classification methods on the well-known iris data, the appendicitis data, and the cancer data. [ABSTRACT FROM AUTHOR]

We present a new information fusion algorithm for fusing fuzzy opinions in a heterogeneous group decision-making environment. The proposed information fusion algorithm has the following advantages: (1) It uses linguistic quantifiers based on the FN-IOWA operator to flexibly determine the weight w i of the opinion of each expert E i for aggregating the experts' fuzzy opinions. (2) The experts' opinions do not necessarily need to have a common intersection. (3) It does not need to use the Delphi method to adjust fuzzy numbers given by experts. [ABSTRACT FROM AUTHOR]

To increase the adaptivity of hierarchical reinforcement learning (HRL) and accelerate the learning process in environments with multiple sources of reward, we propose an emotion-based HRL algorithm inspired by neurobiology. In the algorithm, each reward source defines a subtask and each subtask is assigned an artificial emotion indication (AEI) that predicts the reward component associated with the subtask. The AEIs are simultaneously learned along with the top-level policy and used to interrupt subtask execution when the AEIs change significantly. The algorithm is tested in a simulated gridworld which has two sources of reward and is partially observable. Experimental results show that the inclusion of an artificial emotion mechanism that adaptively terminates subtasks makes reuse of the subtask policies efficient for multigoal environments. The use of the artificial emotion variables significantly accelerates the learning process by 60% and achieves higher long-term reward compared lo a human-designed policy and a restricted form of the MAXQ algorithm. [ABSTRACT FROM AUTHOR]

In this article, a novel fuzzy control algorithm is proposed. In e - e - u coordinate, the control surface of the fuzzy control table method, which directly provides a control value u by a control table without reasoning, is discontinuous; so is the control surface of the analytic expression method, such as u =〈α E + (1-α)Δ E >, which was first conceived by Rong and Wang (1982). By continuing the control surfaces of the fuzzy control table method and the analytic expression method, a new algorithm to improve the properties of the fuzzy controller is constructed. The algorithm has two merits: (a) static error is erased; (b) its program is very simple. Therefore, its operation speed is fast. If we use the algorithm, the excellent quality control and rapid response real-time system can be realized easily only by a cheap general digital chip computer. The article presents theoretic proof. Simulation results illustrate the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Temporal difference TD methods are used by reinforcement learning algorithms for predicting future rewards. This article analyzes theoretically and illustrates experimentally the effects of performing TD lambda prediction udpates backwards for a number of past experiences. More exactly, two related techniques described in the literature are examined, referred to as replayed TD and backwards TD. The former is essentially an online learning method which performs at each time step a regular TD 0 update, and then replays updates backwards for a number of previous states. The latter operates in offline mode, after the end of a trial updating backwards the predictions for all visited states. They are both shown to be approximately equivalent to TD lambda with variable lambda values selected in a particular way. This is true even if they perform only TD 0 updates. The experimental results show that replayed TD 0 is competitive to TD lambda with regard to learning speed and quality. [ABSTRACT FROM AUTHOR]