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Publication Date: 2007
Scientia Iranica (23453605) 14(6)pp. 631-640
In this paper, reinforcement learning is used in order to model the reputation of buying and selling agents. Two important factors, quality and price, are considered in the proposed model. Each selling agent learns to evaluate the reputation of buying agents, based on their profits for that seller and uses this reputation to dedicate a discount for reputable buying agents. Also, selling agents learn to maximize their expected profits by using reinforcement learning to adjust the quality and price of the products, in order to satisfy the buying agents' preferences. In contrast, buying agents evaluate the reputation of selling agents based on two different factors: Reputation based on quality and price. Therefore, buying agents avoid interacting with disreputable selling agents. In addition, the fact that buying agents can have different priorities on the quality and price of their goods is taken into account. The proposed model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/ buying agents that use the proposed algorithms in this paper obtain more satisfaction than the other selling/buying agents. © Sharif University of Technology, December 2007.
Publication Date: 2007
Journal of Theoretical and Applied Electronic Commerce Research (07181876) 2(1)pp. 1-17
In this paper, we propose a market model which Is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning. © 2007 Universidad de Talca.
Publication Date: 2008
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 5097pp. 681-692
In this paper, we propose a flexible parallel ant colony algorithm for classification rule discovery in the large databases. We call this algorithm Parallel Ant-Miner2. This model relies on the extension of real behavior of ants and data mining concepts. The artificial ants are firstly generated and separated into several groups. Each group is assigned a class label which is the consequent parts of the rules it should discover. Ants try to discover rules in parallel and then communicate with each other to update the pheromones in different paths. The communication methods help ants not to gather irrelevant terms of the rule. The parallel executions of ants reduce the speed of convergence and consequently make it possible to extract more new high quality rules by exploring all search space. Our experimental results show that the proposed model is more accurate than the other versions of Ant-Miner. © 2008 Springer-Verlag Berlin Heidelberg.
Publication Date: 2009
pp. 399-405
The correct use of constraints within relational DBMSs is essential for one to take full advantage of the significant benefits offered by the relational model. The use of formal descriptions to capture constraints early in the process may be appropriate in this regard. The theory of relational databases has much in common with the mathematical structures central to the Z notation. In this position paper, we describe how create suitable tables and sql code for Z specifications that have been made according to UML diagrams by Robert's methods[5].Here We use the Z notation as our specification language, and SQL as our implementation language. © 2009 IEEE.
Osinga, S.A. ,
Kramer, M.R. ,
Hofstede, G.J. ,
Roozmand, O. ,
Beulens, A.J. Publication Date: 2010
Lecture Notes in Economics and Mathematical Systems (00758442) 645pp. 177-188
This paper investigates the effect of a selected top-down measure (whatif scenario) on actual agent behaviour and total system behaviour by means of an agent-based simulation model, when agents' behaviour cannot fully be managed because the agents are autonomous. The Chinese pork sector serves as case. A multilevel perspective is adopted: the top-down information management measures for improving pork quality, the variation in individual farmer behaviour, and the interaction structures with supply chain partners, governmental representatives and peer farmers. To improve quality, farmers need information, which they can obtain from peers, suppliers and government. Satisfaction or dissatisfaction with their personal situation initiates change of behaviour. Aspects of personality and culture affect the agents' evaluations, decisions and actions. Results indicate that both incentive (demand) and the possibility to move (quality level within reach) on farmer level are requirements for an increase of total system quality. A more informative governmental representative enhances this effect. © Springer-Verlag Berlin Heidelberg 2010.
Roozmand, O. ,
Ghasem-aghaee, N. ,
Hofstede, G.J. ,
Nematbakhsh, M.A. ,
Baraani, A. ,
Verwaart, T. Publication Date: 2011
Knowledge-Based Systems (09507051) 24(7)pp. 1075-1095
Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede's model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae's five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. © 2011 Elsevier B.V. All rights reserved.
Publication Date: 2011
Scientia Iranica (23453605) 18(6)pp. 1460-1468
Self-Organizing Maps (SOMs) are among the most well-known, unsupervised neural network approaches to clustering, which are very efficient in handling large and high dimensional datasets. The original Particle Swarm Optimization (PSO) is another algorithm discovered through simplified social model simulation, which is effective in nonlinear optimization problems and easy to implement. In the present study, we combine these two methods and introduce a new method for anomaly detection. A discussion about our method is presented, its results are compared with some other methods and its advantages over them are demonstrated. In order to apply our method, we also performed a case study on forest fire detection. Our algorithm was shown to be simple and to function better than previous ones. We can apply it to different domains of anomaly detection. In fact, we observed our method to be a generic algorithm for anomaly detection that may need few changes for implementation in different domains. © 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
Sentiment analysis is the process of analyzing the characteristics of opinions, feelings, and emotions which are expressed in textual data. This paper presents a novel approach for generation of a lexical resource named PersianClues used for sentiment analysis in Persian language. Moreover, a novel unsupervised LDA-based sentiment analysis method called LDASA is proposed. In order to generate the PersianClues, at the first phase, an automatic translation approach is used to translate the existing English clues to Persian. Next, iterative refinement approach is used to correct the erroneous clues resulted from previous step. Then, topic-based polar sets are achieved from these clues and finally, each document is categorized into its related polarity using a classification algorithm. To evaluate this method, three resources about hotels, cell phones and digital cameras have been manually gathered from the e-shopping websites and the results of sentiment analysis on these resources are compared with a baseline named SVM-Unigrams. The experimental results demonstrate an improvement of 9% on average in polarity classification accuracy of the base system. © 2012 IEEE.
Shams, M. ,
Saffar, Mohammadtaghi ,
Shakery, Azadeh ,
Faili, Heshaam Publication Date: 2012
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (16113349)
The idea of applying a conjunction of sentiment and social network analysis to improve the performance of applications has recently attracted attention of researchers. In widely used online shopping websites, customers can provide reviews about a product. Also a number of relations like friendship, trust and similarity between products or users are being formed. In this paper a combination of sentiment analysis and social network analysis is employed for extracting classification rules for each customer. These rules represent customers' preferences for each cluster of products and can be seen as a user model. The combination helps the system to classify products based on customers' interests. We compared the results of our proposed method with a baseline method with no social network analysis. The experiments on Amazon's meta-data collection show improvements in the performance of the classification rules compared to the baseline method. © 2012 Springer-Verlag.
Clustering is the task of grouping related and similar data without any prior knowledge about the labels. In some real world applications, we face huge amounts of unstructured textual data with no organization. In these situations, clustering is a primitive operation that needs to be done to help future e-commerce tasks. Clustering can be used to enhance different e-commerce applications like recommender systems, customer relationship management systems or personal assistant agents. In this paper we propose a new method for text clustering, by constructing a term correlation graph, and then extracting topic word sets from it and finally, categorizing each document to its related topic with the help of a classification algorithm like SVM. This method provides a natural and understandable description for clusters by their topic word sets, and it also enables us to decide the cluster of documents only when needed and in a parallel fashion, thus significantly reducing the offline processing time. Our clustering method also outperforms the well-known k-means clustering algorithm according to clustering quality measures. © 2013 IEEE.
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