A Rough Sets-based Agent Trust Management Framework

A Rough Sets-based Agent Trust Management Framework

Автор: Sadra Abedinzadeh, Samira Sadaoui

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 4 vol.5, 2013 года.

Бесплатный доступ

In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identifying the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of multi agent systems. However, the features of the application domain must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user’s request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain of cross-language Web search. The resulting Web search system recommends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.

Еще

Theory of Rough Sets, Trust Management, Multi Agent Systems, Trust-based Service Selection

Короткий адрес: https://sciup.org/15010404

IDR: 15010404

Список литературы A Rough Sets-based Agent Trust Management Framework

  • A. Gutscher, A trust model for an open, decentralized reputation system, Trust Management, (2007) 285-300.
  • S. Abedinzadeh, S. Sadaoui, Trust Management based on Human Plausible Reasoning: Application to Web Search, in: The 4th ASE/IEEE International Conference on Information Privacy, Security, Risk and Trust IEEE, Amsterdam, Netherlands, 2012.
  • Firdawsi, D. Davis, Shahnameh : the Persian book of kings, Viking, New York, 2006.
  • S. Abedinzadeh, S. Sadaoui, A Rough Set Approach to Agent Trust Management in: The Second IEEE International Conference on Information Privacy, Security, Risk and Trust (PASSAT2010), IEEE, Minneapolis, Minnesota, US, 2010.
  • Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data Kluwer, Boston, 1991.
  • S.-M. Chen, J.-M. Tan, Handling multicriteria fuzzy decision-making problems based on vague set theory, Fuzzy Sets and Systems, 67 (1994) 163-172.
  • Y. Yao, Y. Zhao, Attribute reduction in decision-theoretic rough set models, Information Sciences, 178 (2008) 3356-3373.
  • W. Weijie, R. Gang, W. Wei, The Applications of Rough Set Theory in Civil Engineering, in: Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on, 2010, pp. 27-31.
  • K.A. Cyran, A. Mrózek, Rough sets in hybrid methods for pattern recognition, International Journal of Intelligent Systems, 16 (2001) 149-168.
  • L.-P. Khoo, L.-Y. Zhai, A prototype genetic algorithm-enhanced rough set-based rule induction system, Computers in Industry, 46 (2001) 95-106.
  • B. Chang, H.-M. Pei, J.-R. Chang, Using the Rough Set Theory to Investigate the Building Facilities for the Performing Arts from the Performer’s Perspectives Intelligent Decision Technologies, in: J. Watada, G. Phillips-Wren, L.C. Jain, R.J. Howlett (Eds.), Springer Berlin Heidelberg, 2011, pp. 647-657.
  • L.-Y. Zhai, L.-P. Khoo, Z.-W. Zhong, A rough set based decision support approach to improving consumer affective satisfaction in product design, International Journal of Industrial Ergonomics, 39 (2009) 295-302.
  • W. Al-Mayyan, H.S. Own, H. Zedan, Rough set approach to online signature identification, Digital Signal Processing, 21 (2011) 477-485.
  • P. Srinivasan, M.E. Ruiz, D.H. Kraft, J. Chen, Vocabulary mining for information retrieval: rough sets and fuzzy sets, Information Processing & Management, 37 (2001) 15-38.
  • P. Lingras, G. Peters, Applying rough set concepts to clustering, Rough Sets: Selected Methods and Applications in Management and Engineering, (2012) 23-37.
  • P. Yin, Z.-j. Wang, H.-x. Li, Corporate failure prediction of Chinese listed companies: A variable precision rough set theory, in: International
  • Conference on Management Science and Engineering, 2009. ICMSE 2009. , 2009, pp. 1290-1296.
  • E.M. Maximilien, M.P. Singh, A framework and ontology for dynamic web services selection, Internet Computing, IEEE, 8 (2004) 84-93.
  • C.H. Yew, H. Lutfiyya, A middleware-based approach to supporting trust-based service selection, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), , IEEE, London, ON, Canada, 2011, pp. 407-414.
  • P. Wang, K.M. Chao, C.C. Lo, R. Farmer, An evidence-based scheme for web service selection, Information Technology and Management, 12 (2011) 161-172.
  • P. Wang, QoS-aware web services selection with intuitionistic fuzzy set under consumer’s vague perception, Expert Systems with Applications, 36 (2009) 4460-4466.
  • S. Nusrat, J. Vassileva, Recommending services in a trust-based decentralized user modeling system, Advances in User Modeling, (2012) 230-242.
  • S. Wang, Q. Sun, H. Zou, F. Yang, Reputation measure approach of web service for service selection, Software, IET, 5 (2011) 466-473.
  • F. Bellifemine, A. Poggi, G. Rimassa, JADE: a FIPA2000 compliant agent development environment, in: Proceedings of the fifth international conference on Autonomous agents, ACM, Montreal, Quebec, Canada, 2001, pp. 216-217.
  • L. Tong, L. An, Incremental learning of decision rules based on rough set theory, in: Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on, 2002, pp. 420-425 vol.421.
  • N. Shan, W. Ziarko, Data-based Acquisition and Incremental Modification of Classification Rules, Computational Intelligence, 11 (1995) 357-370.
  • G. Sen, W. Zhi-Yan, W. Zhi-Cheng, Y. He-Ping, A novel dynamic incremental rules extraction algorithm based on rough set theory, in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005, pp. 1902-1907 Vol. 1903.
  • H. Chen, T. Li, S. Qiao, D. Ruan, A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values, International Journal of Intelligent Systems, 25 (2010) 1005-1026.
  • C. Hongmei, L. Tianrui, H. Chengxiang, J. Xiaolan, An incremental updating principle for computing approximations in information systems while the object set varies with time, in: Granular Computing, 2009, GRC '09. IEEE International Conference on, 2009, pp. 49-52.
  • G.B. Jan, S.S. Marcin, RSES and RSESlib - A Collection of Tools for Rough Set Computations, in: Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing, Springer-Verlag, 2001.
  • K. Er Øhrn Jan, ROSETTA -- A Rough Set Toolkit for Analysis of Data, in: P. Wang (Ed.) Proceedings of the Third Joint Annual Conference on Information Sciences, Durham, NC, 1997, pp. 403-407.
  • W. Guo-Yin, Z. Zheng, Z. Yi, RIDAS - a rough set based intelligent data analysis system, in: Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on, 2002, pp. 646-649 vol.642.
  • A.T. Bjorvand, "Rough enough"-a system supporting the rough sets approach, in: Proceedings of the sixth Scandinavian conference on Artificial intelligence, IOS Press, Helsinki, Finland, 1997, pp. 290-291.
  • I. Düntsch, G. Gediga, The rough set engine GROBIAN, in: The proceedings of the 15th IMACS World Congress, Berlin, Germany, 1997, pp. 613-618.
  • B. Prędki, S. Wilk, Rough set based data exploration using ROSE system Foundations of Intelligent Systems, in: Z. Ras, A. Skowron (Eds.), Springer Berlin / Heidelberg, 1999, pp. 172-180.
  • D. Wu, D. He, Exploring the Further Integration of Machine Translation in English-Chinese Cross Language Information Access, Program: electronic library and information systems, 46 (2012) 3-3.
  • L. Shaozi, Z. Changle, C. Huowang, Web document retrieval based on multi-agent, in: Proceedings of the Ninth International Conference on Computer Supported Cooperative Work in Design, 2005., 2005, pp. 469-474 Vol. 461.
  • E. Greengrass, Information Retrieval: A Survey, in, DOD Technical Report: TR-R52-008-001, , 2001.
  • W. Dayong, Z. Yu, Z. Shiqi, L. Ting, Identification of Web Query Intent Based on Query Text and Web Knowledge, in: Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on, 2010, pp. 128-131.
  • M.-Y. Chen, H.-C. Chu, Y.-M. Chen, Developing a semantic-enable information retrieval mechanism, Expert Systems with Applications, 37 (2010) 322-340.
  • A. De, S.K. Kopparapu, A rule-based Short Query Intent Identification System, in: Signal and Image Processing (ICSIP), 2010 International Conference on, 2010, pp. 212-216.
  • E. German, L. Sheremetov, An agent framework for processing FIPA-ACL messages based on interaction models, in: Proceedings of the 8th international conference on Agent-oriented software engineering VIII, Springer-Verlag, Honolulu, HI, USA, 2008, pp. 88-102.
  • M. Winikoff, Jack™ Intelligent Agents: An Industrial Strength Platform Multi-Agent Programming, in: R. Bordini, M. Dastani, J. Dix, A. Fallah Seghrouchni (Eds.), Springer US, 2005, pp. 175-193.
  • E. Makosa, Rule tuning, Uppsala University, Sweden, (2005) 1-51.
  • M. Sikora, Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms Transactions on Rough Sets XI, in: J. Peters, A. Skowron (Eds.), Springer Berlin / Heidelberg, 2010, pp. 130-160.
  • E. Tsang, Z. Suyun, Decision Table Reduction in KDD: Fuzzy Rough Based Approach Transactions on Rough Sets XI, in: J. Peters, A. Skowron (Eds.), Springer Berlin / Heidelberg, 2010, pp. 177-188.
  • J. Bazan, M. Szczuka, The Rough Set Exploration System Transactions on Rough Sets III, in: J. Peters, A. Skowron (Eds.), Springer Berlin / Heidelberg, 2005, pp. 25-42.
  • J. Bobadilla, F. Ortega, A. Hernando, J. Bernal, A collaborative filtering approach to mitigate the new user cold start problem, Knowledge-Based Systems, (2011).
  • A.I. Schein, A. Popescul, L.H. Ungar, D.M. Pennock, Methods and metrics for cold-start recommendations, in: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, Tampere, Finland, 2002, pp. 253-260.
  • D. Tingquan, C. Yanmei, X. Wenli, D. Qionghai, A novel approach to fuzzy rough sets based on a fuzzy covering, Inf. Sci., 177 (2007) 2308-2326.
  • W. Ziarko, Variable precision rough set model, Journal of computer and system sciences, 46 (1993) 39-59.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp