Learning opponent’s preferences for effective negotiation: an approach based on concept learning

We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user’s preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user’s preferences, but also its opponent’s preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer’s needs and to offer services that respect consumer’s preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer’s preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer’s preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.     

On Buckley��s approach to fuzzy estimation

In several works, Buckley (Soft Comput 9:512–518, 2005a; Soft Comput 9:769–775 2005b; Fuzzy statistics, Springer, Heidelberg, 2005c) have introduced and developed an approach to the estimation of unknown parameters in statistical models. In this paper, we introduce an improved method for the estimation of parameters for cases in which the Buckley’s approach presents some drawbacks, as for example when the underlying statistic has a non-symmetric distribution.     

The ELECTRE multicriteria analysis approach based on Atanassov’s intuitionistic fuzzy sets

In recent decades, intuitionistic fuzzy sets have been applied to many different fields; however, few current studies have used the ELECTRE method to solve multi-criteria decision-making problems with intuitionistic fuzzy information. The purpose of this paper is to develop a new method, the intuitionistic fuzzy ELECTRE method, for solving multi-criteria decision-making problems. Atanassov’s intuitionistic fuzzy set (A-IFS) characteristics are simultaneously concerned with the degree of membership, degree of non-membership, and intuitionistic index, and people can use A-IFS to describe uncertain situations in decision-making problems. We use the proposed method to rank all alternatives and determine the best alternative. The proposed method can also use imperfect or insufficient knowledge of data to deal with decision-making problems. Finally, two practical examples are given that illustrate the procedure of the proposed method.     

Atanassov’s intuitionistic fuzzy submodules with respect to a t-norm

In this paper, t-norm based intuitionistic fuzzy submodules are defined, and their various properties are investigated. We also show that if an intuitionistic fuzzy set of a module is an intuitionistic fuzzy submodule with respect to a t-norm, then it is also an intuitionistic fuzzy submodule with respect to the annihilation t-norm. We investigate the nature of intuitionistic fuzzy submodules of a module with the help of their respective cut sets. We obtain some results based on (α, β)-level sets and t-norm based α-cut sets of an intuitionistic fuzzy submodule with respect to a t-norm T.     

An approach to determining Shor’s dual quadratic estimates

For a general quadratic problem, an analog is formulated as a homogeneous quadratic problem. The estimates ψ* constructed based on Shor’s dual quadratic estimates for these problems are proved to be equal. It is shown that, for the case of a homogeneous quadratic problem, finding ψ* is reduced to an unconstraint minimization problem for a convex function. The study was partially sponsored by the grant UKM2-2812-KV-06 (CRDF Cooperative Grants Program). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 89–99, March–April 2008.     

A novel approach to exploring company’s financial soundness: Investor’s perspective

Prediction of company’s life cycle stage change; creation of an ordered 2D map allowing to explore company’s financial soundness from a rating agency perspective; and prediction of trends of main valuation attributes usually used by investors are the main objectives of this article. The developed algorithms are based on a random forest (RF) and a nonlinear data mapping technique “t-distributed stochastic neighbor embedding”.Information from five different perspectives, namely balance, income, cash flow, stock price, and risk indicators was aggregated via proximity matrices of RF to enable exploration of company’s financial soundness from a rating agency perspective. The proposed use of information not only from companies’ financial statements but also from the stock price and risk indicators perspectives has proved useful in creating ordered 2D maps of rated companies. The companies were well ordered according to the credit risk rating assigned by the Moody’s rating agency.Results of experimental investigations substantiate that the developed models are capable of predicting short term trends of the main valuation attributes, providing valuable information for investors, with low error. The models reflect financial soundness of actions taken by company’s management team. It was also found that company’s life cycle stage change can be determined with the average accuracy of 72.7%. Bearing in mind fuzziness of the transition moment, the obtained prediction accuracy is rather encouraging.     

A goal programming approach to fuzzy linear regression with fuzzy input�Coutput data

Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered in outputs and/or in inputs. This paper focuses on both fuzzy inputs and fuzzy outputs. First, some approximations for multiplication of two triangular fuzzy numbers are introduced. Then, to evaluate the fuzzy linear regression, the best approximation is selected to minimize a suitable function via goal programming. An important feature of the proposed model is that it takes into account the centers of fuzzy data as well as their spreads. Moreover, it is flexible to deal with both symmetric and non-symmetric data. Furthermore, it can handle the crisp inputs and trapezoidal fuzzy outputs easily. To show the efficiency of the proposed model, some numerical examples are solved and compared with some earlier methods.     

Fritz Carlson’s inequality for fuzzy integrals

In this paper we prove a Carlson type inequality for Sugeno integrals.     

An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach

In this paper, we present an effective and efficient diagnosis system using fuzzy k-nearest neighbor (FKNN) for Parkinson’s disease (PD) diagnosis. The proposed FKNN-based system is compared with the support vector machines (SVM) based approaches. In order to further improve the diagnosis accuracy for detection of PD, the principle component analysis was employed to construct the most discriminative new feature sets on which the optimal FKNN model was constructed. The effectiveness of the proposed system has been rigorously estimated on a PD data set in terms of classification accuracy, sensitivity, specificity and the area under the receiver operating characteristic (ROC) curve (AUC). Experimental results have demonstrated that the FKNN-based system greatly outperforms SVM-based approaches and other methods in the literature. The best classification accuracy (96.07%) obtained by the FKNN-based system using a 10-fold cross validation method can ensure a reliable diagnostic model for detection of PD. Promisingly, the proposed system might serve as a new candidate of powerful tools for diagnosing PD with excellent performance.     

Modeling agent’s preferences by its designer’s social value orientation

Human social preferences have been shown to play an important role in many areas of decision-making. There is evidence from the social science literature that human preferences in interpersonal interactions depend partly on a measurable personality trait called, Social Value Orientation (SVO). Automated agents are often written by humans to serve as their delegates when interacting with other agents. Thus, one might expect an agent’s behaviour to be influenced by the SVO of its human designer. With that in mind, we present the following: first, we explore, discuss and provide a solution to the question of how SVO tests that were designed for humans can be used to evaluate agents’ social preferences. Second, we show that in our example domain there is a medium–high positive correlation between the social preferences of agents and their human designers. Third, we exemplify how the SVO information of the designer can be used to improve the performance of some other agents playing against those agents, and lastly, we develop and exemplify the behavioural signature SVO model which allows us to better predict performances when interactions are repeated and behaviour is adapted.     

Chro-Ring: a time-oriented visual approach to represent writer’s history

Personal histories of ancient Chinese writers have attracted many people. This kind of information is always in the form of obscure texts and the multiple facets are interrelated, so learning personal history in a traditional way is time-consuming. Fortunately, information visualization technique can help us with the problem. To clearly present time-oriented information visually about a famous writer to users, we propose Chro-Ring, a visualization system for representing faceted temporal data. It is time-oriented and displays multiple facets of personal information using multiple visualization views with a delicate layout. We implemented it in a scenario and conducted a user study to evaluate its effectiveness.     

A parallel neural network approach to prediction of Parkinson’s Disease

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson’s Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson’s Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.     

H ∞ approach to T-S fuzzy controller for limiting reconstruction errors

In this article, the reconstruction error between a real system to be controlled and its Takagi–Sugeno (T-S) fuzzy model is considered in the context of control system design. Accordingly, we propose an H _∞ approach to an adaptive controller that consists of two parts: one is obtained by solving certain linear matrix inequalities (fixed part) and the other is acquired using a fuzzy approximator in which the related parameters are tuned by an adaptive law (variable part). The proposed controller can guarantee a convergent and uniformly bounded control state while maintaining the stability of all the signals involved.     

UK public’s intention to engage with academia via online technologies

Online technologies have made it possible for practitioners and the public to reach academics directly and seek information and knowledge in areas of professional or personal interest. This paper uses the extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT2) to examine the factors that affect the adoption of online technologies by practitioners and other members of the public in order to engage with the academic community. Understanding the driving factors of such online engagement is very important as it can inform academic practice and how institutional support is provided. The results of our online survey (N?=?241), derived by using Structural Equation Modelling, show that behavioural intention is mainly affected by the user's habit, performance expectancy and facilitating conditions. Also, educational level has a moderating effect in the cases of habit and social influence and gender in the cases of habit and hedonic motivation.     

Image segmentation using Atanassov’s intuitionistic fuzzy sets

The problem of segmentation in spite of all the work over the last decades, is still an important research field and also a critical preprocessing step for image processing, mostly due to the fact that finding a global optimal threshold that works well for all kind of images is indeed a very difficult task that, probably, will never be accomplished.During the past years, fuzzy logic theory has been successfully applied to image thresholding. In this paper we describe a thresholding technique using Atanassov’s intuitionistic fuzzy sets (A-IFSs). This approach uses Atanassov’s intuitionistic index values for representing the hesitance of the expert in determining whether the pixel belongs to the background or that it belongs to the object. First, we describe the general framework of this approach to bi-level thresholding. Then we present its natural extension to multilevel thresholding. This multilevel threshold methodology segments the image into several distinct regions which correspond to a background and several objects.Segmentation experimental results and comparison with Otsu’s multilevel thresholding algorithm for the calculation of two and three thresholds are presented.     

A new approach of Romero’s extended lexicographic goal programming: fuzzy extended lexicographic goal programming

Goal programming (GP) is perhaps one of the most widely used approaches in the field of multicriteria decision making. The major advantage of the GP model is its great flexibility which enables the decision maker to easily incorporate numerous variations on constraints and goals. Romero provides a general structure, extended lexicographic goal programming (ELGP) for GP and some multiobjective programming approaches. In this work, we propose the extension of this unifying framework to fuzzy multiobjective programming. Our extension is carried out by several methodologies developed by the authors in the fuzzy GP approach. An interval GP model has been constructed where the feasible set has been defined by means of a relationship between fuzzy numbers. We will apply this model to our fuzzy extended lexicographic goal programming (FELGP). The FELGP is a general primary structure with the same advantages as Romero’s ELGP and moreover it has the capacity of working with imprecise information. An example is given in order to illustrate the proposed method.     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

A novel approach to designing an adaptive committee applied to predicting company’s future performance

This article presents an approach to designing an adaptive, data dependent, committee of models applied to prediction of several financial attributes for assessing company’s future performance. Current liabilities/Current assets, Total liabilities/Total assets, Net income/Total assets, and Operating Income/Total liabilities are the attributes used in this paper. A self-organizing map (SOM) used for data mapping and analysis enables building committees, which are specific (committee size and aggregation weights) for each SOM node. The number of basic models aggregated into a committee and the aggregation weights depend on accuracy of basic models and their ability to generalize in the vicinity of the SOM node. A random forest is used a basic model in this study. The developed technique was tested on data concerning companies from ten sectors of the healthcare industry of the United States and compared with results obtained from averaging and weighted averaging committees. The proposed adaptivity of a committee size and aggregation weights led to a statistically significant increase in prediction accuracy if compared to other types of committees.