Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-17T16:33:09.283Z Has data issue: false hasContentIssue false
  • English
  • Français

Agent mining approaches: an ontological view

Part of: Ontologies and Standards for Intelligent Systems

Published online by Cambridge University Press:  31 August 2021

Emmanuelle Grislin-Le Strugeon Open the ORCID record for Emmanuelle Grislin-Le Strugeon [Opens in a new window] ,
Kathia Marcal de Oliveira Open the ORCID record for Kathia Marcal de Oliveira [Opens in a new window] ,
Dorsaf Zekri Open the ORCID record for Dorsaf Zekri [Opens in a new window]  and
Marie Thilliez Open the ORCID record for Marie Thilliez [Opens in a new window]
Emmanuelle Grislin-Le Strugeon*
Affiliation:
Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France INSA Hauts-de-France, F-59313 Valenciennes, France
Kathia Marcal de Oliveira*
Affiliation:
Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France
Dorsaf Zekri*
Affiliation:
Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France ReDCAD Laboratory, University of Sfax, B.P. 1173, 3029 Sfax, Tunisia
Marie Thilliez*
Affiliation:
Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, F-59313 Valenciennes, France
*
E-mails: emmanuelle.grislin@uphf.fr, kathia.oliveira@uphf.fr, dorsaf.zekri2@uphf.fr, marie.thilliez@uphf.fr
E-mails: emmanuelle.grislin@uphf.fr, kathia.oliveira@uphf.fr, dorsaf.zekri2@uphf.fr, marie.thilliez@uphf.fr
E-mails: emmanuelle.grislin@uphf.fr, kathia.oliveira@uphf.fr, dorsaf.zekri2@uphf.fr, marie.thilliez@uphf.fr
E-mails: emmanuelle.grislin@uphf.fr, kathia.oliveira@uphf.fr, dorsaf.zekri2@uphf.fr, marie.thilliez@uphf.fr
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduced as an interdisciplinary area that combines multi-agent systems, data mining and knowledge discovery, agent mining is currently in practice. To develop agent mining applications involves a combination of different approaches (model, architecture, technique and so on) from software agent and data mining (DM) areas. This paper presents an investigation of the approaches used in the agent mining systems by deeply analyzing 121 papers resulting from a systematic literature review. An ontology was defined to capitalize the knowledge collected from this study. The ontology is organized according to seven main facets: the problem addressed, the application domain, the agent-related and the mining-related elements, the models, processes and algorithms. This ontology is aimed at providing support to decisions about agent mining application design.

Type
Review
Information
The Knowledge Engineering Review , Volume 36 , 2021 , e12
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abdelrahman, S., Bahgat, R. & Farag, G. 2011. Order statistics Bayesian-mining agent modelling for automated negotiation. Informatica (Ljubljana) 35(1), 123–137.Google Scholar
Aksvonov, K. & Antonova, A. 2018. Development of a hybrid decision-making method based on a simulation-genetic algorithm in a web-oriented metallurgical enterprise information system. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 197–202. doi: 10.1109/ICUFN.2018.8436676.CrossRefGoogle Scholar
Anand, N., Yang, M., van Duin, J. & Tavasszy, L. 2012. Genclon: an ontology for city logistics. Expert Systems with Applications 39(15), 11944–11960. doi: 10.1016/j.eswa.2012.03.068.CrossRefGoogle Scholar
Aridor, Y., Carmel, D., Maarek, Y. S., Soffer, A. & Lempel, R. 2002. Knowledge encapsulation for focused search from pervasive devices. ACM Transactions on Information Systems 20 (1), 25–46. doi: 10.1145/503104.503106.CrossRefGoogle Scholar
Atto, K. & Kotova, E. E. 2019. Data mining agents as means of communicating users in an e-learning environment. In Proceedings of the 2019 IEEE Communication Strategies in Digital Society Seminar, ComSDS 2019, 39–42.Google Scholar
Bajo, J., Campbell, A. T. & Zhou, X. 2016. Mobile sensing agents for social computing environments. Advances in Intelligent Systems and Computing 473, 157–167.CrossRefGoogle Scholar
Basili, V. R., Caldiera, G. & Rombach, H. D. 1994. The goal question metric approach. In Encyclopedia of Software Engineering. Wiley.Google Scholar
Bellifemine, F., Poggi, A. & Rimassa, G. 2001. JADE: a FIPA2000 compliant agent development environment. In Proceedings of the Fifth International Conference on Autonomous Agents, 216–217.Google Scholar
Beni, G. 2005. From swarm intelligence to swarm robotics. In Swarm Robotics. SR 2004, Şahin, E. & Spears, W. (eds.), 3342. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 1–9. doi: 10.1007/978-3-540-30552-1_1.Google Scholar
Bernon, C., Camps, V., Gleizes, M.-P. & Picard, G. 2004. Tools for self-organizing applications engineering. In Engineering Self-Organising Systems, Nature-Inspired Approaches to Software Engineering, 2977. LNCS. Springer, 283–298.Google Scholar
Botelho, W. T., Marietto, M. D. G. B., Mendes, E. D. L., Sousa, D. R. D., Pimentel, E. P., da Silva, V. L. & dos Santos, T. 2020. Toward an interdisciplinary integration between multi-agents systems and multi-robots systems: a case study. The Knowledge Engineering Review 35, e35. doi: 10.1017/S0269888920000375.CrossRefGoogle Scholar
Brank, J., Mladenić, D. & Grobelnik, M. 2006. Gold standard based ontology evaluation using instance assignment. In Proceedings of 4th International EON Workshop’06 Evaluation of Ontologies for the Web @WWW2006, CEUR Workshop Proceedings, Edinburgh, UK.Google Scholar
Bresciani, P., Perini, A., Giorgini, P., Giunchiglia, F. & Mylopoulos, J. 2004. Tropos: an agent-oriented software development methodology. Autonomous Agent and Multi-Agent Systems 3, 203–236.CrossRefGoogle Scholar
Calpur, M. C., Tatlidede, M. U. & Cataloglu, I. 2018. Domain specific conversational intelligent agents: natural language processing in real world applications. In Proceedings of the 12th Turkish National Software Engineering Symposium UTMS 2018. CEUR Workshop Proceedings.Google Scholar
Canito, A., Marreiros, G. & Corchado, J. M. 2019. Automatic document annotation with data mining algorithms. In Rocha, A., Adeli, H., Reis, L. & Costanzo, S., editors, New Knowledge in Information Systems and Technologies. WorldCIST’19 2019. Advances in Intelligent Systems and Computing, 930. Springer.CrossRefGoogle Scholar
Cao, L., Luo, C. & Zhang, C. 2007. Agent-mining interaction: an emerging area. In 2nd International Workshop Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, 4476. LNAI, 60–73.Google Scholar
Cao, L., Weiss, G. & Yu, P. S. 2012. A brief introduction to agent mining. Autonomous Agents and Multi-Agent Systems 25 (3), 419–424.CrossRefGoogle Scholar
Chen, C. & Chen, X. 2017. Scheduling optimization in restricted channels based on the agent technology and Bayesian network. In 2017 4th International Conference on Transportation Information and Safety, ICTIS 2017 – Proceedings, Yan, X., Zhong, M., Lu, M., Wu, C., Qiu, Z. & Hu, Z. (eds.). Institute of Electrical and Electronics Engineers Inc., 291–295. doi: 10.1109/ICTIS.2017.8047779.CrossRefGoogle Scholar
Chen, P. & Plale, B. A. 2015. Big data provenance analysis and visualization. In Proceedings – 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015. Institute of Electrical and Electronics Engineers Inc., 797–800. doi: 10.1109/CCGrid.2015.85.CrossRefGoogle Scholar
Chen, Y., Zeng, X., Chen, X. & Guo, W. 2020. A survey on automatic image annotation. Applied Intelligence 50(10), 3412–3428.CrossRefGoogle Scholar
Cobos, C., Niño, M., Mendoza, M., Fabregat, R. & Gomez, L. 2007. Learning management system based on SCORM, agents and mining. In 8th International Conference on Web Information Systems Engineering, WISE 2007, 4831. LNCS, 298–309.Google Scholar
Cordeiro, F., Werneck, V., Santos, N. & Cysneiros, L. 2016. Mas ontology: ontology for multiagent systems. In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016), 1. SCITEPRESS, 536–543.Google Scholar
Cossentino, M. 2005. From Requirements to Code with the PASSI Methodology, chapter IV, 79–106. Idea Group Publishing.CrossRefGoogle Scholar
Cranefield, S., Willmott, S. & Finin, T. 2002. Introduction to the special issue on ontologies in agent systems. The Knowledge Engineering Review 17(1), 1–5. doi: 10.1017/S0269888902000310.CrossRefGoogle Scholar
Dahl, V., Barahona, P., Bel-Enguix, G. & Krippahl, L. 2010. Biological concept formation grammars: a flexible, multiagent linguistic tool for biological processes. In ICAART 2010 – 2nd International Conference on Agents and Artificial Intelligence, Proceedings, 2, 388–394.Google Scholar
De Arruda, H. F., Silva, F. N., Costa, L. d. F. & Amancio, D. R. 2017. Knowledge acquisition: a Complex networks approach. Information Sciences 421, 154–166. doi: 10.1016/j.ins.2017.08.091.CrossRefGoogle Scholar
De Souza, R. M. M., Vilasbôas, F. G., Notargiacomo, P. & De Castro, L. 2019. Integrating an association rule mining agent in an ERP system: a proposal and a computational scalability analysis. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence, 2, 778–786.Google Scholar
Dignum, V., Javier, V.-S. & Frank, D. 2005. OMNI: Introducing Social Structure, Norms and Ontologies into Agent Organizations, 3346. LNCS. Springer-Verlag, 181–198.Google Scholar
Dileo, J., Jacobs, T. & DeLoach, S. 2002. Integrating ontologies into multiagent systems engineering. In Proceedings of 4th International Bi-Conference Workshop on Agent Oriented Information Systems (AOIS), 15–16.Google Scholar
Dimou, C., Symeonidis, A. L. & Mitkas, P. A. 2007. Evaluating knowledge intensive multi-agent systems. In 2nd International Workshop Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, 4476. LNAI, 74–87.Google Scholar
Dos Santos, C. T. & Bazzan, A. L. C. 2005. Integrating knowledge through cooperative negotiation – a case study in bioinformatics. In International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2005, 3505. LNAI, 277–288.Google Scholar
Duarte, V. A. R. & Julia, R. M. S. 2017. Improving netfeaturemap-based representation through frequent pattern mining in a specialized database. In Proceedings – 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016, Esposito, A., Alamaniotis, M., Mali, A. & Bourbakis, N. (eds.). Institute of Electrical and Electronics Engineers Inc., 954–961 doi: 10.1109/ICTAI.2016.0147.CrossRefGoogle Scholar
Esfandi, A. 2010. Efficient anomaly intrusion detection system in adhoc networks by mobile agents. In Proceedings – 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010, 7, 73–77. doi: 10.1109/ICCSIT.2010.5563804.CrossRefGoogle Scholar
Eugin Lilly, M. M. & Venkataraman, S. 2013. Predictive load balancing for data mining in distributed systems. Journal of Theoretical and Applied Information Technology 53(1), 13–23.Google Scholar
Fowler, C. A. & Hammell, R. J. 2015. Mining information assurance data with a hybrid intelligence/multi-agent system. In 2015 IEEE/ACIS 14th International Conference on Computer and Information Science, ICIS 2015 – Proceedings. Institute of Electrical and Electronics Engineers Inc., 23–28 doi: 10.1109/ICIS.2015.7166564.CrossRefGoogle Scholar
Freitas, A., Bordini, R. H. & Vieira, R. 2017. Model-driven engineering of multi-agent systems based on ontologies. Applied Ontology 12(2), 157–188.Google Scholar
Gago, P., Santos, M. F., Silva, S. A., Cortez, P., Neves, J. M. & Gomes, L. 2005. INTCare: a knowledge discovery based intelligent decision support system for intensive care medicine. Journal of Decision Systems 14(3), 241–259. doi: 10.3166/jds.14.241-259.CrossRefGoogle Scholar
Garcia-Sanchez, F., Tomas Fernandez-Breis, J., Valencia-Garcia, R., Miguel Gomez, J. & Martinez-Bejar, R. 2008. Combining semantic web technologies with multi-agent systems for integrated access to biological resources. Journal of Biomedical Informatics 41(5, SI), 848–859. doi: 10.1016/j.jbi.2008.05.007.CrossRefGoogle Scholar
Gaya, M. C. & Giráldez, J. I. 2011. Merging local patterns using an evolutionary approach. Knowledge and Information Systems 29(1), 1–24. doi: 10.1007/s10115-010-0332-x.CrossRefGoogle Scholar
Gerardo, B. D. & Lee, J. 2009. A framework for discovering relevant patterns using aggregation and intelligent data mining agents in telematics systems. Telematics and Informatics 26(4), 343–352. doi: 10.1016/j.tele.2008.05.003.CrossRefGoogle Scholar
Ghedini Ralha, C. & Sarmento Silva, C. V. 2012. A multi-agent data mining system for cartel detection in Brazilian government procurement. Expert Systems with Applications 39(14), 11642–11656. doi: 10.1016/j.eswa.2012.04.037.CrossRefGoogle Scholar
Gómez-Sanz, J. & Fuentes-Fernández, R. 2015. Understanding agent-oriented software engineering methodologies. The Knowledge Engineering Review 30(4), 375–393.CrossRefGoogle Scholar
Gorodetsky, V. 2013. Agents and distributed data mining in smart space: challenges and perspectives. In 8th International Workshop on Agents and Data Mining Interaction, ADMI 2012, 7607. LNAI, 153–165.Google Scholar
Gorodetsky, V., Karsaev, O. & Samoilov, V. 2005. Multi-agent and data mining technologies for situation assessment in security-related applications. In International Workshop “Monitoring, Security and Rescue Techniques in Multiagent Systems” (MSRAS 2004), 28, 411–422.Google Scholar
Gorodetsky, V., Karsaev, O., Samoylov, V. & Serebryakov, S. 2008. Interaction of agents and data mining in ubiquitous environment. In Proceedings – 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology – Workshops, WI-IAT Workshops 2008, 562–566. doi: 10.1109/WIIAT.2008.354.CrossRefGoogle Scholar
Gorunescu, F. 2011. Data Mining. Concepts, Models and Techniques, 12. Intelligent Systems Reference Library. Springer.CrossRefGoogle Scholar
Gottgtroy, P. 2007. Ontology driven knowledge discovery process: a proposal to integrate ontology engineering and KDD. In PACIS 2007 – 11th Pacific Asia Conference on Information Systems: Managing Diversity in Digital Enterprises.Google Scholar
Grislin-Le Strugeon, E., Oliveira, K. M., Thilliez, M. & Petit, D. 2021. A systematic mapping study on agent mining. Journal of Experimental & Theoretical Artificial Intelligence, 1–26. doi: 10.1080/0952813X.2020.1864784.CrossRefGoogle Scholar
Gruber, T. R. 1993. A translation approach to portable ontology specifications. Knowledge Acquisition 5(2), 199–220.CrossRefGoogle Scholar
Grüninger, M. & Fox, M. S. 1995. Methodology for the design and evaluation of ontologies. Technical report, University of Toronto, Toronto, Canada.Google Scholar
Guizzardi, G. & Wagner, G. 2005. Towards ontological foundations for agent modelling concepts using the unified foundational ontology (UFO). In Agent-Oriented Information Systems II, Bresciani, P., Giorgini, P., Henderson-Sellers, B., Low, G. & Winikoff, M. (eds.). Springer Berlin Heidelberg, 110–124. ISBN 978-3-540-31946-7.Google Scholar
Haider, K., Tweedale, J. & Jain, l. 2009. An intelligent decision support system using expert systems in a MAS. Studies in Computational Intelligence 199, 213–222.Google Scholar
Hamdi, M. S. 2007. Masacad: a multi-agent approach to information customization for the purpose of academic advising of students. Applied Soft Computing Journal 7(3), 746–771. doi: 10.1016/j.asoc.2006.02.001.CrossRefGoogle Scholar
Huhns, M. N. & Singh, M. P. 1997. Ontologies for agents. IEEE Internet Computing 1(6), 81–83.CrossRefGoogle Scholar
Ioniţă, I. & Ioniţă, L. 2013. An agent-based approach for building an intrusion detection system. In Proceedings – RoEduNet IEEE International Conference. IEEE Computer Society. doi: 10.1109/RoEduNet.2013.6714184.CrossRefGoogle Scholar
Islam, K. S. 2007. An approach to argumentation context mining from dialogue history in an e-market scenario. In AIDM ’07 Proceedings of the 2nd International Workshop on Integrating Artificial Intelligence and Data Mining, Ong, K.-L., Li, W. & Gao, J. (eds.), 84. Conferences in Research and Practice in Information Technology Series. Australian Computer Society, 73–81.Google Scholar
Izumi, K., Matsui, H. & Matsuo, Y. 2007. Socially embedded multi agent based simulation of financial market. In Proceedings of the International Conference on Autonomous Agents, 1110–1112. doi: 10.1145/1329125.1329337.CrossRefGoogle Scholar
Jayyousi, T. W. & Reynolds, R. G. 2014. Extracting urban occupational plans using cultural algorithms [application notes]. IEEE Computational Intelligence Magazine 9 (3), 66–87. doi: 10.1109/MCI.2014.2326103.CrossRefGoogle Scholar
Jiang, P., Mair, Q. & Feng, Z.-R. June 2007. Agent alliance formation using ART-networks as agent belief models. Journal of Intelligent Manufacturing 18 (3), 433–448. doi: 10.1007/s10845-007-0032-x.CrossRefGoogle Scholar
Kadhim, M. A., Alam, A. & Kaur, H. 2016. A multi-intelligent agent system for automatic construction of rule-based expert system. International Journal of Intelligent Systems and Applications 8(9), 62–68. doi: 10.5815/ijisa.2016.09.08.CrossRefGoogle Scholar
Kaschesky, M., Sobkowicz, P. & Bouchard, G. 2011. Opinion mining in social media: modeling, simulating, and visualizing political opinion formation in the web. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, 2011, 317–326. doi: 10.1145/2037556.2037607.CrossRefGoogle Scholar
Keet, C. M., Ławrynowicz, A., d’Amato, C., Kalousis, A., Nguyen, P., Palma, R., Stevens, R. & Hilario, M. 2015. The data mining optimization ontology. Journal of Web Semantics 32, 43–53.CrossRefGoogle Scholar
Kotonya, G., Sommerville, I. & Hall, S. 2003. Towards a classification model for component-based software engineering research. In Proceedings 29th Euromicro Conference, 43–52. doi: 10.1109/EURMIC.2003.1231566.CrossRefGoogle Scholar
Kotova, E. E. 2017. Intellectual data analysis in the educational process. In Proceedings of 2017 20th IEEE International Conference on Soft Computing and Measurements, SCM 2017, Shaposhnikov, S. (ed.). Institute of Electrical and Electronics Engineers Inc., 757–759 doi: 10.1109/SCM.2017.7970714.CrossRefGoogle Scholar
Kularbphettong, K., Meesad, P. & Clayton, G. 2012. A hybrid system based on multi-agent systems in case of e-WeddingThailand. In 7th International Workshop on Agents and Data Mining Interaction, ADMI 2011, 7103. LNAI, 344–359.Google Scholar
Kusumura, Y., Hijikata, Y. & Nishida, S. 2004. NTM-agent: text mining agent for net auction. IEICE Transactions on Information and Systems E87-D (6), 1386–1396.Google Scholar
Kwon, J., Kim, S. & Yoon, Y. 2004. Just-in-time recommendation using multi-agents for context-awareness in ubiquitous computing environment. In International Conference on Database Systems for Advanced Applications DASFAA 2004, 2973, 656–669.Google Scholar
Larsen, J. B., Burattin, A., Davis, C. J., Hjardem-Hansen, R. & Villadsen, J. 2019. A data driven agent elicitation pipeline for prediction models. Lecture Notes in Business Information Processing 362, 570–582.CrossRefGoogle Scholar
Lee, J. H. & Park, S. C. 2005. Intelligent profitable customers segmentation system based on business intelligence tools. Expert Systems with Applications 29(1), 145–152. doi: 10.1016/j.eswa.2005.01.013.CrossRefGoogle Scholar
Li, C.-T. & Lin, S.-D. 2012. Evaplanner: an evacuation planner with social-based flocking kinetics. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1568–1571. doi: 10.1145/2339530.2339782.CrossRefGoogle Scholar
Liu, X., Merrick, K. & Abbass, H. 2017. Designing artificial agents to detect the motive profile of users in virtual worlds and games. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/SSCI.2016.7850036.CrossRefGoogle Scholar
Liu, Z., Niu, D., Yang, X. & Sheng, W. 2009. Research on intelligent decision support system for power system. In 2009 IEEE International Conference on Information and Automation, ICIA 2009, 412–417. doi: 10.1109/ICINFA.2009.5204959.CrossRefGoogle Scholar
Loia, V., Pedrycz, W., Senatore, S. & Sessa, M. I. 2007. Interactive knowledge management for agent-assisted web navigation. International Journal of Intelligent Systems 22 (10), 1101–1122. doi: 10.1002/int.20239.CrossRefGoogle Scholar
Lopez, M. F., Gomez-Perez, A., Sierra, J. P. & Sierra, A. P. 1999. Building a chemical ontology using methontology and the ontology design environment. IEEE Intelligent Systems & Their Applications, 14 (1), 37–45.CrossRefGoogle Scholar
Matei, O., Di Orio, G., Jassbi, J., Barata, J. & Cenedese, C. 2016. Collaborative data mining for intelligent home appliances. IFIP Advances in Information and Communication Technology 480, 313–323.CrossRefGoogle Scholar
Mateo, R. M. A., Yoon, I. & Lee, J. 2008. Data-mining model based on multi-agent for the intelligent distributed framework. In 2nd KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2008, 4953, LNAI, 753–762. doi: 10.1007/978-3-540-78582-8_76.CrossRefGoogle Scholar
Menczer, F. 2003. Complementing search engines with online web mining agents. Decision Support Systems 35(2), 195–212. doi: 10.1016/S0167-9236(02)00106-9.CrossRefGoogle Scholar
Mgbemena, C. & Bell, D. 2016. Data-driven customer behaviour model generation for agent based exploration. In Proceedings of the 49th Annual Simulation Symposium, 48. Simulation Series, 155–161. The Society for Modeling and Simulation International.Google Scholar
Moemeng, C., Wang, C. & Cao, L. 2012. Obtaining an optimal MAS configuration for agent-enhanced mining using constraint optimization. In 7th International Workshop on Agents and Data Mining Interaction, ADMI 2011, 7103. LNAI, 46–57. doi: 10.1007/978-3-642-27609-5_5.CrossRefGoogle Scholar
Moemeng, P., Cao, L. & Zhang, C. 2008. F-trade 3.0: an agent-based integrated framework for data mining experiments. In Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT Workshops, 612–615. doi: 10.1109/WIIAT.2008.230.CrossRefGoogle Scholar
Musen, M. A. & The, Protégé Team. 2015. The Protégé project: a look back and a look forward. AI matters 1(4), 4–12.CrossRefGoogle Scholar
Noy, N. F. & McGuinness, D. L. 2001. Ontology development 101: a guide to creating your first ontology. Technical report, Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880.Google Scholar
Olivares-Alarcos, A., Beßler, D., Khamis, A., Goncalves, P., Habib, M. K., Bermejo-Alonso, J., Barreto, M., Diab, M., Rosell, J., Quintas, J., Olszewska, J., Nakawala, H., Pignaton, E., Gyrard, A., Borgo, S., Alenyà, G., Beetz, M. & Li, H. 2019. A review and comparison of ontology-based approaches to robot autonomy. The Knowledge Engineering Review 34, e29. doi: 10.1017/S0269888919000237.CrossRefGoogle Scholar
Oliveira, K. M., Bacha, F., Mnasser, H. & Abed, M. 2013. Transportation ontology definition and application for the content personalization of user interfaces. Expert Systems with Applications 40(8), 3145–3159. doi: https://doi.org/10.1016/j.eswa.2012.12.028.CrossRefGoogle Scholar
Oskouei, R. J. & Moradi-Kor, N. 07 2016. Proposing a novel adaptive learning management system: an application of behavior mining & intelligent agents. Intelligent Automation and Soft Computing, 1–7. doi: 10.1080/10798587.2016.1186429.Google Scholar
Pedrycz, W. & Rai, P. 2008. A multifaceted perspective at data analysis: a study in collaborative intelligent agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 38(4), 1062–1072. doi: 10.1109/TSMCB.2008.925728.CrossRefGoogle ScholarPubMed
Petersen, K., Vakkalanka, S. & Kuzniarz, L. 2015. Guidelines for conducting systematic mapping studies in software engineering: an update. Information and Software Technology 64, 1–18.CrossRefGoogle Scholar
Phillips, J. & Buchanan, B. G. 2001. Ontology-guided knowledge discovery in databases. In Proceedings of the First International Conference on Knowledge Capture. Association for Computing Machinery (ACM), 123–130.Google Scholar
Pi, D., Wang, Q., Li, W. & Lv, J. 2005. APA: an interior-oriented intrusion detection system based on multi-agents. In Third International Conference on Computer Network and Mobile Computing, ICCNMC 2005, 3619, 1227–1233.Google Scholar
Pignaton de Freitas, E., Bermejo-Alonso, J., Khamis, A., Li, H. & Olszewska, J. I. 2020. Ontologies for cloud robotics. The Knowledge Engineering Review 35, e25. doi: pignaton2020.CrossRefGoogle Scholar
Pinzón, C. I., De Paz, J. F., Herrero, Á., Corchado, E., Bajo, J. & Corchado, J. M. 2013. IdMAS-SQL: intrusion detection based on MAS to detect and block SQL injection through data mining. Information Sciences 231, 15–31. doi: 10.1016/j.ins.2011.06.020.CrossRefGoogle Scholar
Ponte, B., De La Fuente, D., Pino, R. & Rosillo, R. 2015. Real-time water demand forecasting system through an agent-based architecture. International Journal of Bio-Inspired Computation 7 (3), 147–156. doi: 10.1504/IJBIC.2015.069559.CrossRefGoogle Scholar
Pruengkarn, R., Wong, K. W. & Fung, C. C. 2017. A review of data mining techniques and applications. Journal of Advanced Computational Intelligence and Intelligent Informatics 21(1), 31–48.CrossRefGoogle Scholar
Pulmano, C. E. & Estuar, M. R. J. E. 2016. Towards developing an intelligent agent to assist in patient diagnosis using neural networks on unstructured patient clinical notes: Initial analysis and models. Procedia Computer Science 100, 263–270. doi: 10.1016/j.procs.2016.09.153.CrossRefGoogle Scholar
Pultar, E., Raubal, M. & Goodchild, M. F. 2008. GEDMWA: Geospatial Exploratory Data Mining Web Agent. In GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 499–502. doi: 10.1145/1463434.1463519.CrossRefGoogle Scholar
Qiu, J., Luo, L. & Zhang, Y. 2009. The design and assessment of web data analysis system. In Proceed. International Conference on E-Business and Information System Security, EBISS 2009. doi: 10.1109/EBISS.2009.5138109.CrossRefGoogle Scholar
Ribino, P., Cossentino, M., Lodato, C., Lopes, S., Sabatucci, L. & Seidita, V. 2013. Ontology and goal model in designing BDI multi-agent systems. In CEUR Workshop Proceedings, 1099, 66–72.Google Scholar
Rodríguez, M., Bermejo-Alonso, J., Hernandez Corbato, C. & Sanz, R. 2012. Ontology-driven description and engineering of autonomous systems: application to process systems engineering. In 22nd European Symposium on Computer Aided Process Engineering, Bogle, I. D. L. & Fairweather, M. (eds.), 30. Computer Aided Chemical Engineering. Elsevier, 717–721.Google Scholar
Roldán, J. J., Olivares-Méndez, M. A., del Cerro, J. & Barrientos, A. 2018. Analyzing and improving multi-robot missions by using process mining. Autonomous Robots 42(6), 1187–1205.CrossRefGoogle Scholar
Rosé, C. P. & Ferschke, O. 2016. Technology support for discussion based learning: from computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education 26(2), 660–678. doi: 10.1007/s40593-016-0107-y.CrossRefGoogle Scholar
Saadi, C. & Chaoui, H. 2017. Intrusion detection system based interaction on mobile agents and clust-density algorithm ’IDS-AM-Clust’. In Colloquium in Information Science and Technology, CIST, El Mohajir, M., El Mohajir, B., Chahhou, M. & Al Achhab, M. (eds.), 681–684. doi: 10.1109/CIST.2016.7804973.CrossRefGoogle Scholar
Sadhasivan, D. K. & Balasubramanian, K. 2017. A Fusion of Multiagent Functionalities for Effective Intrusion Detection System. Security and Communication Networks. doi: 10.1155/2017/6216078.Google Scholar
Samoylov, V. & Gorodetsky, V. 2005. Ontology issue in multi-agent distributed learning. In International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2005, 3505. LNAI, 215–230.Google Scholar
Santos, E. Jr. & Johnson, G. Jr. 2004. Toward detecting deception in intelligent systems. In Proceedings of SPIE – The International Society for Optical Engineering, Trevisani, D.A. & Sisti, A. F. (eds.), 5423, 130–141. doi: 10.1117/12.547296.CrossRefGoogle Scholar
Sateli, B. & Witte, R. 2017. Personal research agents on the web of linked open data. In Language, Data, and Knowledge: Proceedings of International Conference on Language, Data and Knowledge LDK 2017, McCrae, J., Hellmann, S., Buitelaar, P., Gracia, J., Bond, F. & Chiarcos, C. (eds.), 10318. LNAI, 10–25.Google Scholar
Sethi, K., Sai Rupesh, E., Kumar, R., Bera, P. & Madhav, Y. V. 2019. A context-aware robust intrusion detection system: a reinforcement learning-based approach. International Journal of Information Security 19, 657–678.CrossRefGoogle Scholar
Shih, D.-H., Chiang, H.-S. & Lin, B. 2008. Collaborative spam filtering with heterogeneous agents. Expert Systems with Applications 35(4), 1555–1566. doi: 10.1016/j.eswa.2007.08.062.CrossRefGoogle Scholar
Silva, C. V. S. & Ralha, C. G. 2011. Agmi – an agent-mining tool and its application to Brazilian government auditing. In WEBIST 2011 – Proceedings of the 7th International Conference on Web Information Systems and Technologies, 535–538.Google Scholar
Sinnappan, S., Williams, M.-A. & Muthaly, S. 2001. Agent based architecture for internet marketing. In 6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000, 2112. Springer Verlag, 158–169.Google Scholar
Šlapák, M. & Neruda, R. 2014. Multiobjective genetic programming of agent decision strategies. In 5th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2014, 303. Springer Verlag, 173–182. doi: 10.1007/978-3-319-08156-4_18.CrossRefGoogle Scholar
Strauss, A. & Corbin, J. 1998. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. SAGE Publications. ISBN 9780803959408.Google Scholar
Studer, R., Benjamins, V. R. & Fensel, D. 1998. Knowledge engineering: principles and methods. Data and Knowledge Engineering 25(1), 161–197. doi: https://doi.org/10.1016/S0169-023X(97)00056-6.CrossRefGoogle Scholar
Tangod, K. & Kulkarni, G. 2020. Secure communication through multiagent system-based diabetes diagnosing and classification. Journal of Intelligent Systems 29(1), 703–718.CrossRefGoogle Scholar
Thanawala, P. & Joshi, M. 2017. Autorec: intelligent agent for rapid recommendation to engender or sustain blogbased virtual community. In Proceedings – 2nd International Conference on Computing, Communication, Control and Automation, ICCUBEA 2016. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ICCUBEA.2016.7860027.CrossRefGoogle Scholar
Thanudas, B., Shridharan, S., Raj, V. C., Sairam, A. P., Gajmoti, V. & Joshi, P. 2019. A novel architecture for an integrated enterprise network security system. International Journal of Security and Networks, 14 (1), 47–60. doi: 10.1504/IJSN.2019.098919.CrossRefGoogle Scholar
Tounsi, Y., Anoun, H. & Hassouni, L. 2020. Csmas: improving multi-agent credit scoring system by integrating big data and the new generation of gradient boosting algorithms. In Proceedings of the 3rd International Conference on Networking, Information Systems & Security NISS2020, 1–7. doi: 10.1145/3386723.3387851.CrossRefGoogle Scholar
Tran, Q.-N. N. & Low, G. 2008. Mobmas: a methodology for ontology-based multi-agent systems development. Information and Software Technology 50 (7–8), 697–722.CrossRefGoogle Scholar
W3C. 11 Aug 2005. Simple part-whole relations in owl ontologies, w3c editor’s draft. Technical report, W3C.Google Scholar
Wang, X., Niu, W., Li, G., Yang, X. & Shi, Z. 2012. Mining frequent agent action patterns for effective multi-agent-based web service composition. In 7th International Workshop on Agents and Data Mining Interaction, ADMI 2011, 7103. LNAI, 211–227. doi: 10.1007/978-3-642-27609-5_14.CrossRefGoogle Scholar
Wohlin, C., Runeson, P., Höst, M., Ohlsson, M. C., Regnell, B. & Wesslén, A. 2012. Experimentation in Software Engineering. Springer-Verlag Berlin Heidelberg.CrossRefGoogle Scholar
Wu, C.-A., Lin, W., Tseng, M.-C. & Wu, C.-C. 2007. Ontology-incorporated mining of association rules in data warehouse. Journal of Internet Technology 8(4), 477–485.Google Scholar
Xiao, G., Calvanese, D., Kontchakov, R., Lembo, D., Poggi, A., Rosati, R. & Zakharyaschev, M. 2018. Ontology-based data access: a survey. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18. International Joint Conferences on Artificial Intelligence Organization, 5511–5519.Google Scholar
Xu, D.-S. & Yan, S.-L. 2009. Multi-agent in ant colony algorithm approach for solving traveling salesman problem. In 2009 International Workshop on Intelligent Systems and Applications, ISA 2009. doi: 10.1109/IWISA.2009.5072962.CrossRefGoogle Scholar
Xu, J., Yao, L., Li, L., Ji, M. & Tang, G. 2020. Argumentation based reinforcement learning for meta-knowledge extraction. Information Sciences 506, 258–272.CrossRefGoogle Scholar
Xu, Y., Zhang, W., Liu, W. & Ferrese, F. 2012. Multiagent-based reinforcement learning for optimal reactive power dispatch. IEEE Transactions on Systems Man and Cybernetics Part C - Applications and Reviews 42(6), 1742–1751. doi: 10.1109/TSMCC.2012.2218596.CrossRefGoogle Scholar
Xue, H., Guo, P., Zhang, H. & Kang, B. 2009. Study and realization of supplier business intelligence system for chain supermarket. In Proceedings – 2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009. doi: 10.1109/CISE.2009.5366538.CrossRefGoogle Scholar
Yang, J., Li, T., Liang, G., He, W. & Zhao, Y. 2019. A hierarchy distributed-agents model for network risk evaluation based on deep learning. CMES – Computer Modeling in Engineering and Sciences 120(1), 1–23.CrossRefGoogle Scholar
Yang, Q., Feng, B. & Song, P. 2007. Study on anti-money laundering service system of online payment based on union-bank mode. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007, 4986–4989. doi: 10.1109/WICOM.2007.1223.CrossRefGoogle Scholar
Yang, Y. J., Sung, T.-W., Wu, C. & Chen, H.-Y. 2010. An agent-based workflow system for enterprise based on fipa-os framework. Expert Systems with Applications 37(1), 393–400.CrossRefGoogle Scholar
Ying, W., Ray, P. & Lewis, L. 2013. A methodology for creating ontology-based multi-agent systems with an experiment in financial application development. In 46th Hawaii International Conference on System Sciences, 3397–3406.Google Scholar
Yu, H.-X., Zheng, W.-S., Wu, A., Guo, X., Gong, S. & Lai, J.-H. 2019. Unsupervised person re-identification by soft multilabel learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019-June, 2143–2152.Google Scholar
Zambonelli, F., Jennings, N. R. & Wooldridge, M. 2003. Developing multiagent systems: the gaia methodology. ACM Transactions on Software Engineering and Methodology 12(3), 317–370.CrossRefGoogle Scholar
Zhang, Y. 2018. Key technologies of confrontational intelligent decision support for multi-agent systems. Automatic Control and Computer Sciences 52 (4), 283–290.CrossRefGoogle Scholar
Zhao, M., Cai, W. & Turner, S. 2018. Clust simulating realistic crowd behaviour by mining pattern from crowd videos. Computer Graphics Forum 37(1), 184–201.CrossRefGoogle Scholar