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This study presents Bargaining Chips: a framework for one-to-many concurrent composite negotiations, where multiple deals can be reached and combined. Our framework is designed to mirror the salient aspects of real-life procurement and trading scenarios, in which a buyer seeks to acquire a number of items from different sellers at the same time. To do so, the buyer needs to successfully perform multiple concurrent bilateral negotiations as well as coordinate the composite outcome resulting from each interdependent negotiation. This paper contributes to the state of the art by: (1) presenting a model and test-bed for addressing such challenges; (2) by proposing a new, asynchronous interaction protocol for coordinating concurrent negotiation threads; and (3) by providing classes of multi-deal coordinators that are able to navigate this new one-to-many multi-deal setting. We show that Bargaining Chips can be used to evaluate general asynchronous negotiation and coordination strategies in a setting that generalizes over a number of existing negotiation approaches. © 2021 Owner/Author.
The Internet of Things (IoT) enables smart Things to communicate via the Internet. Things are growing in number, and their need for multiple resources in a complementary manner engenders serious problems in resource allocation. Combinatorial Auctions (CA) are the optimal market mechanism for allocating such indivisible bundles. Since the abundance of bundles in the IoT market makes it impossible to bid on all bundles, Things express their preferences on some (and not all) bundles to make the winner determination amenable. We address the winner determination problem by proposing an allocation mechanism based on social choice methods, which operates on the number of requested resources, the number of bundles, the offered price, and the preferred weight of each bundle. These methods include Borda, Copeland, Average without Misery, Least Misery, and Hare. Finally, we demonstrate the evaluation of these methods in terms of execution time and envy-freeness among the Things. © 2023 IEEE.
For a long time, culture has been an influencing parameter in negotiations. Growth of international trades and business competitions has increased the importance of negotiations among countries and different cultures. Developing new technologies, particularly the use of artificial intelligence in electronic trading areas, has provided us with the application of intelligent agents to resolve challenges in e- negotiations. In this study, a model is developed and implemented to arm intelligent agents with time-sensitivity cultural parameter in negotiations in electronic commerce context. The seller's proposals are offered based on the estimated value of the buyers' time-sensitivity in delivering the products. It starts from the highest price which satisfies the buyer's time sensitivity. The simulations are based on the Salacuse's Cultural dataset related to five countries, Finland, Mexico, Turkey, India, and the United States of America. The negotiation algorithms were implemented in Java platform and MySQL database for both cases of with and without cultural differences in time sensitivity. The evaluation shows that the cultural-based model starts the negotiation from an offer close to the buyer's desire. This yields less number of rounds and total negotiation time period. The simulation results also show that the buyer's budget as an economic factor can be effective in the negotiation outcomes in some cases. © 2014 IEEE.
Automated negotiating agents are usually designed and implemented in a general way so that they can negotiate successfully in front of a vast variety of opponents. In the real world, most opponents are single-peaked. Gaussian agents that use such distribution function to rate the negotiation items are important sorts of such opponents. Modeling the opponents is of great importance since it enables us to adjust our next decisions accordingly. This can bring us short-time compromises, ideal eventual utility, more satisfaction, and so on. In negotiating with Gaussian opponents, the estimation of the opponent's peak point is the core. In this regard, we have paid particular attention to how accurate the existing automated agents attended in Automated Negotiating Agents Competition (ANAC) during 2010-2019 can model Gaussian bidders and showed the result of the experiments. © 2021 IEEE.
Feedback is an essential component of learning, as it helps students identify their strengths and weaknesses, and improve their performance. However, the students may not be able to understand how their work has been judged. One way to address this issue is to let the students assess and comment on the work of their peers, Peer Assessment (PA). PA has benefits such as enhancing learning outcomes, developing self-assess and critical thinking skills, and fostering collaboration. However, PA also poses some challenges such as ensuring fairness, anonymity, and reliability. In this study, we designed and implemented an anonymous electronic PA for a few classes participating EU-Iran STEM/UNITEL project. We used several digital tools to simulate a double-blind PA process, and a rubric based on the students' own criteria and weights. The grades collected from 70 students represent positive feedback. We discuss the functionality, advantages, and limitations of our approach. © 2024 IEEE.
Delay and capacity (throughput) are two important parameters to route data packets in Mobile Ad Hoc Networks (MANETs). In this paper, a new connectionless routing algorithm has been proposed to overcome the performance limit. The proposed algorithm is an extension of dynamic virtual route (DVR) algorithm. Mobility degree of nodes' neighborhood is used to calculate two mobility metrics. Mobility metrics are utilized to establish a more stable route between source and destination. Simulation study shows that the proposed algorithm can improve the network throughput and decrease average end-to-end delay significantly. © 2014 IEEE.
The 13th automated negotiation competition was held in 2 leagues (ANL2022 and SCML2022) in conjunction with the 31st IJCAI conference. The ANL for 2022 is bilateral negotiation under the SOAP protocol. Agents are allowed to learn from their previous negotiations. The agents could have 3 main BOA components: a Bidding strategy that decides which bid and when must be sent to the opponent, an Opponent model that tries to model the opponent's preferences, and an Acceptance strategy that decides whether to accept the opponent's offer or not. This paper explains our LuckyAgent2022's BOA components and its learning methods over negotiation sessions. To improve its utility over sessions, we propose SLM, a LSN Stop-Learning mechanism, to prevent overfitting by adapting it to a multi-armed bandit problem. It finds the best value for variables of a time-dependent bidding strategy for the opponent. © 2022 IEEE.
In any negotiation, one of the most important parts of the negotiator's task is deciding whether or not to accept the opponent's offer. Actually, the most challenging thing is answering this question: which offer and when must be accepted? A wide range of simple to sophisticated acceptance strategies have been proposed: simple acceptance strategies which have the constant threshold value and sophisticated strategies that notice both utility and time in order to determine acceptance thresholds. This study introduces a novel statistical acceptance strategy with considering the similarity between the opponent's offer and our previous offers, which is combined with existing usual acceptance strategies. Experiments show our strategy has advantages against the state-of-the-art acceptance strategies. © 2020 IEEE.
Taghiyarrenani, Zahra ,
Fanian, A. ,
Mahdavi, E. ,
Mirzaei, A. ,
Farsi, Hamed
In the past decades, machine learning based intrusion detection systems have been developed. This paper discloses a new aspect of machine learning based intrusion detection systems. The proposed method detects normal and anomaly behaviors in the desired network where there are not any labeled samples as training dataset. That is while a plenty of labeled samples may exist in another network that is different from the desired network. Because of the difference between two networks, their samples produce in different manners. So, direct utilizing of labeled samples of a different network as training samples does not provide acceptable accuracy to detect anomaly behaviors in the desired network. In this paper, we propose a transfer learning based intrusion detection method which transfers knowledge between the networks and eliminates the problem of providing training samples that is a costly procedure. Comparing the experimental results with the results of a basic machine learning method (SVM) and also baseline method(DAMA) shows the effectiveness of the proposed method for transferring knowledge for intrusion detection systems. © 2018 IEEE.
Mala, H. ,
Dakhil-alian, M. ,
Brenjkoub, M. 2pp. 3304-3308
Proxy signature schemes allow a proxy signer to generate a proxy signature on behalf of an original signer. In this paper we propose an Identity-based proxy signature scheme from bilinear pairings. In comparison with the Xu et al's scheme, our scheme is more efficient in computation and requires fewer pairing operations especially in verification phase. © 2006 IEEE.
Shahgholi, B. ,
Shahbazi, H. ,
Kazemifard M. ,
Zamanifar, K. ,
Shahgholi, B. ,
Shahbazi, H. ,
Kazemifard M. ,
Zamanifar, K. 2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 74-80
RoboCupRescue Simulation System is a platform for designing and implementing various artificial intelligent issues. In rescue simulation environments, Firebrigades should select fire points in a collaborative manner such that the total achieved result is optimized. In this work, we are going to propose a new method for fire prediction and selection in Firebrigade agents. This method is based on Evolving Fuzzy Neural Networks to obtain a set of trained fuzzy rules as rule base of Firebrigades Fire Selection System to select targets autonomously.
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 2006pp. 156-161
Growing demand for different services over cellular mobile networks has emphasized the necessity of QOS provisioning. However, nodes mobility jeopardizes the resource allocation process, and decreases the quality of service provided to delay sensitive traffic. So, in such networks, the remote location prediction has significant impact on near-optimum resource management procedure. In the other hand, in recent years, MPLS has been considered as the preeminent technology to incur QOS for integrated services. In this paper we propose a new location prediction method based on Neural Networks, to manage LSPs in an MPLS domain. Proposed predictor uses geographical characteristics of underlying area, in addition to the movement history of that remote. A set of confidence ratios is considered as the output of our predictor. That set is considered as a criterion for establishing and managing LSPs. Each output of the predictor indicates the degree of confidence for the corresponding neighboring cell, showing that how likely the remote may move to that cell. This procedure proposes two types of Pre-Established LSP, called "Simple LSP" and "ConstRaint based LSP". A set of simulations with typical assumptions has been carried out to evaluate the performance and response time of the proposed method on QOS.
Ghasem-aghaee, N. ,
Poormohamadbagher, L. ,
Kaedi, M. ,
Ören, T.I. Proceedings of the IASTED International Conference on Modelling and Simulation (10218181) pp. 44-49
In this paper we have developed a fuzzy intelligent agent with a fuzzy emotional component. This component evaluates the degree of anger emotion which the intelligent agent may feel when it encounters to a persecuting event. For this purpose we have implemented a fuzzy inference system using Java environment and we have considered three parameters related on human anger emotion as the inputs of this system. The system outputs can be used in agent decision making process by combination this anger intensity to other emotion intensities and applies them for choosing a proper action from some available actions.
Computer Communications (1873703X) 30(13)pp. 2676-2685
In recent years, Multi Protocol Label Switching (MPLS) has been considered as the preeminent technology to incur Quality of Service (QoS) for integrated services. However, in wireless networks the remotes mobility endangers resource management procedure and QoS provisioning. In this paper we propose a new location prediction method based on Evolving Fuzzy Neural Networks (EFuNNs), to manage Label Switched Paths (LSPs) in an MPLS domain. The proposed predictor employs geographical characteristics of underlying area and the movement history of a remote, to produce a set of confidence ratios as the output. That set is considered as a criterion for establishing and managing LSPs so that QoS preserved. The simulation results have shown superior performance in terms of prediction accuracy and utilization improvement for the proposed methods. © 2007 Elsevier B.V. All rights reserved.
In this paper a steganalysis method is presented for a steganographic routine. The steganography is based on pixel value differencing, PVD, and no attacked has been offered for it yet. By modification of an existing method, that uses χ2 measure, the PVD steganography was successfully and accurately attacked. ©2007 IEEE.
Simulation and Gaming (1552826X) 39(1)pp. 83-100
Distributed Artificial Intelligence techniques have evolved toward multi-agent systems (MASs) where agents solve specific problems. Bargaining is a challenging area well-explored in both MAS and economics. To make agents more human-like and to increase their flexibility to reach an agreement, the authors investigated the role of personality behaviors of participants in a multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. The authors simulate a computational approach based on a heuristic bargaining protocol and personality model on artificial stereotypes. The results suggest compound behaviors appropriate to gain the best overall utility in the role of buyer and seller and with regard to social welfare and market activeness. This generic personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in areas involving negotiation and bargaining. © 2008 Sage Publications.
The major drawback of fuzzy data mining is that after applying fuzzy data mining on the quantitative data, the number of extracted fuzzy association rules is very huge. When many association rules are obtained, the usefulness of them will be reduced. In this paper, we introduce an approach to reduce and summarize the extracted fuzzy association rules after fuzzy data mining. In our approach, in first, we encode each obtained fuzzy association rule to a string of numbers. Then we use self-organizing map (SOM) neural network iteratively in a tree structure for clustering these encoded rules and summarizing them to a smaller collection of fuzzy association rules. This approach has been applied on a data base containing information about 5000 employees and has shown good results. © 2008 IADIS.
The bargaining problem in two-person games is selecting a particular point (i.e., an equilibrium) in the utility set to reach a jointly optimal result. Nonetheless, there are games with no or even more than one equilibrium, where in non-zero-sum games with multiple equilibria, all equilibria do not necessarily result in the same utility set. Each player not only should play with her/his equilibrium strategies, but also (s)he would be better to select the strategy that leads her/him to a better utility than the other equilibria. We present a BNE approach consisting of three parts: a 2-layer graph representation of the game that encompasses both strategic and extensive representations; a game reduction method where all Nash Equilibria remain unchanged; and locating the best NE strategy (which results in the best social welfare among all NEs) to start with, along with determining the first mover. The study shows the computationally correctness of the approach in well-known 2×2 and different sizes of sample 2-person games.
Neural Computing And Applications (09410643) 17(2)pp. 193-200
Optimizing the traffic signal control has an essential impact on intersections efficiency in urban transportation. This paper presents a two-stage method for intersection signal timing control. First, the traffic volume is predicted using a neuro-fuzzy network called Adaptive neuro-fuzzy inference system (ANFIS). The inputs of this network include two-dimensional, hourly and daily, traffic volume correlations. In the second stage, appropriate signal cycle and optimized timing of each phase of the signal are estimated using a combination of Self Organizing and Hopfield neural networks. The energy function of the Hopfield network is based on a traffic model derived by queuing analysis. The performance of the proposed method has been evaluated for real data. The two-dimensional correlation presents superior performance compared to hourly traffic correlation. The evaluation of proposed overall method shows considerable intersection throughput improvement comparing to the results taken form Synchro software. © 2007 Springer-Verlag London Limited.
International Journal of Human Computer Studies (10959300) 67(1)pp. 1-35
Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains. © 2008 Elsevier Ltd. All rights reserved.
Pourmohammadbagher, L. ,
Kaedi, M. ,
Ghasem-aghaee, N. ,
Ören, T.I. Mathematical and Computer Modelling of Dynamical Systems (17445051) 15(6)pp. 535-553
Personality and emotions are effective factors in human decision-making processes. Thus, when an agent has to emulate human behaviour, not only should this agent think and reason but also should have emotions and personality. In this article a fuzzy agent with dynamic personality is modelled based on a five-factor personality model and implemented in a Java environment. Then it is extended with a fuzzy emotion component. This emotion component uses calculated personality factors and some related parameters and then determines the degree of anger. The proposed personality and emotion model provides a proper framework for human-like agent decision-making tasks. © 2009 Taylor & Francis.
Journal of Systems Architecture (13837621) 55(3)pp. 180-187
Nowadays, quantum cellular automata (QCA) has been considered as the pioneer technology in next generation computer designs. QCA provides the computer computations at nano level using molecular components as computation units. Although the QCA technology provides smaller chip area and eliminates the spatial constraints than earlier CMOS technology, but different characteristics and design limitations of QCA architectures have led to essential attentions in replacement of traditional structures with QCA ones. Inherent information flow control, limited wire length, and consumed area are of such features and restrictions. In this paper, D flip-flop structure has been considered and we have proposed two new D flip-flop structures which employ the inherent capabilities of QCA in timing and data flow control, rather than ordinary replacement of CMOS elements with equivalent QCA ones. The introduced structures involve small number of cells in contrast to earlier proposed ones in presence of the same or even lower input to output delay. The proposed structures are simulated using the QCADesigner and the validity of them has been proved. © 2008 Elsevier B.V. All rights reserved.
Proceedings of the International Conference on e-Learning, ICEL (20488882) 2009pp. 386-393
Nowadays e-learning has an important role in learning and education. There is no doubt in importance of personalized and adaptive learning. In this field there are many adaptation methods such as adaptive presentation and navigation methods. The idea of various adaptive presentation techniques is to adapt the content of a page accessed by a particular user to current knowledge, interests, and other characteristics of the user. In this paper we present a new method for content adaptation: Personalized Summary Generation. We consider user knowledge and interests to generate personalized summary for text-based courses in e-learning systems. If we can generate personalized summaries it can increase learning speed and improve learning process. Personalized summaries reduce the volume of irrelevance learning content. Our summarization method generates summary regarding to user knowledge and interests through four phases: Preprocessing, Initial scoring, scoring based on interest & knowledge and Personalized summary generation. We will test our proposed method and show experimental results. Copyright The Authors, 2009. All Rights Reserved.
Information Sciences (00200255) 179(3)pp. 248-266
In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers' profits in promotion and marketing models. In addition to the sellers' profits, three important factors - annoyance of customers, sellers' reputations, and customers' anonymity - are considered in this study. Without considering the customer's profile, we minimize unrelated and disliked offers to reduce the customer's annoyance and elevate the seller's reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information. © 2008 Elsevier Inc. All rights reserved.
Blinded data mining is a branch of data mining technique which is focused on protecting user privacy. To mine sensitive data such as medical information, it is desirable to protect privacy and there is not worry about revealing personalized data. In this paper a new approach for blinded data mining is suggested. It is based on ontology and k-anonymity generalization method. Our method generalizes a private table by considering table fields' ontology, so that each tuple will become k-anonymous and less specific to not reveal sensitive information. This method is implemented using protégé java for evaluation. ©2009 IEEE.
Mala, H. ,
Shakiba, M. ,
Dakhilalian, M. ,
Bagherikaram, G. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 5867pp. 281-294
Camellia, a 128-bit block cipher which has been accepted by ISO/IEC as an international standard, is increasingly being used in many cryptographic applications. In this paper, using the redundancy in the key schedule and accelerating the filtration of wrong pairs, we present a new impossible differential attack to reduced-round Camellia. By this attack 12-round Camellia-128 without FL/FL - 1 functions and whitening is breakable with a total complexity of about 2116.6 encryptions and 2 116.3 chosen plaintexts. In terms of the numbers of the attacked rounds, our attack is better than any previously known attack on Camellia-128. © 2009 Springer-Verlag Berlin Heidelberg.
Nowadays e-learning has an important role in learning and education. Obviously one of the most important challenges in e-learning is to produce appropriate learning contents for learners. One solution might be using annotations of learners to select and edit learning contents. Due to the possibility of adding annotations to a specific learning content, exploiting learners' annotations can help author to improve learning content. Regarding to concepts' ontology and contents' annotations, it is possible to edit certain content in contents hierarchy. In addition it is possible to create learning contents by selecting high rated learners' annotations and presenting them to new learners. The benefits of the method using annotations for editing contents, is that it is implicit and having each annotation the related content can be found and if necessary it can be edited or improved . In this paper we try to present a framework for using learners' annotations in order to selecting and editing learning contents in e-learning systems. We describe details of annotations classification and method of rating annotations. In addition, the proposed solution has been tested and benefits of annotations analyze to produce feedbacks for authors have been shown. © 2009 IEEE.
Journal of Systems and Software (01641212) 83(4)pp. 702-709
In this paper, we introduce a new impossible differential cryptanalysis of Zodiac that is considerably more effective than the one in the previous work (Hong et al., 2002). Using two new 13-round impossible differential characteristics and the early abort technique, this 3R-Attack breaks 128-bit key full-round Zodiac with complexity less than 271.3 encryptions, which is practical. This result is approximately 248 times better than what mentioned in the earlier work. Our result reveals depth of Zodiac's weakness against impossible differential cryptanalysis due to its poor diffusion layer. We also obtain a tighter upper bound for time complexity. © 2009 Elsevier Inc. All rights reserved.
Mala, H. ,
Dakhilalian, M. ,
Rijmen, V. ,
Modarres hashemi m., M. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 6498pp. 282-291
Using a new 4-round impossible differential in AES that allows us to exploit the redundancy in the key schedule of AES-128 in a way more effective than previous work, we present a new impossible differential attack on 7 rounds of this block cipher. By this attack, 7-round AES-128 is breakable with a data complexity of about 2106 chosen plaintexts and a time complexity equivalent to about 2110 encryptions. This result is better than any previously known attack on AES-128 in the single-key scenario. © 2010 Springer-Verlag Berlin Heidelberg.
Computer Standards and Interfaces (09205489) 32(4)pp. 222-227
Crypton is a 128-bit block cipher which was submitted to the Advanced Encryption Standard competition. In this paper, we present two new impossible differential attacks to reduced-round Crypton. Using two new observations on the diffusion layer of Crypton, exploiting a 4-round impossible differential, and appropriately choosing three additional rounds, we mount the first impossible differential attack on 7-round Crypton. The proposed attacks require 2121 chosen plaintexts each. The first attack requires 2125.2 encryptions. We then utilize more pre-computation and memory to reduce the time complexity to 2116.2 encryptions in the second attack. © 2010 Elsevier B.V. All rights reserved.
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