CBR model for the intelligent management of customer support centers

[EN] In this paper, a new CBR system for Technology Management Centers is presented. The system helps the staff of the centers to solve customer problems by finding solutions successfully applied to similar problems experienced in the past. This improves the satisfaction of customers and ensures a good reputation for the company who manages the center and thus, it may increase its profits. The CBR system is portable, flexible and multi-domain. It is implemented as a module of a help-desk application to make the CBR system as independent as possible of any change in the help-desk. Each phase of the reasoning cycle is implemented as a series of configurable plugins, making the CBR module easy to update and maintain. This system has been introduced and tested in a real Technology Management center ran by the Spanish company TISSAT S.A.Financial support from Spanish government under grant PROFIT FIT-340001-2004-11 is gratefully acknowledgeHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Julian Inglada, VJ.; Rebollo Pedruelo, M.; Botti, V. (2006). CBR model for the intelligent management of customer support centers. En Lecture Notes in Computer Science. Springer Verlag (Germany). 663-670. https://doi.org/10.1007/11875581_80S663670Acorn, T., Walden, S.: SMART: SupportManagement Automated Reasoning Technology for Compaq Customer Service. In: Scott, A., Klahr, P. (eds.) Proceedings of the 2 International Conference on Intelligent Tutoring Systems, ITS-92 Berlin, vol. 4, pp. 3–18. AAAI Press, Menlo Park (1992)Simoudis, E.: Using Case-Based Retrieval for Customer Technical Support. IEEE Intelligent Systems 7, 10–12 (1992)Kriegsman, M., Barletta, R.: Building a Case-Based Help Desk Application. IEEE Expert: Intelligent Systems and Their Applications 8, 18–26 (1993)Shimazu, H., Shibata, A., Nihei, K.: Case-Based Retrieval Interface Adapted to Customer-Initiated Dialogues in Help Desk Operations. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings of the 12th National Conference on Artificial Intelligence, vol. 1, pp. 513–518. AAAI Press, Menlo Park (1994)Raman, R., Chang, K.H., Carlisle, W.H., Cross, J.H.: A self-improving helpdesk service system using case-based reasoning techniques. Computers in Industry 2, 113–125 (1996)Kang, B.H., Yoshida, K., Motoda, H., Compton, P.: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11, 611–631 (1997)Roth-Berghofer, T., Iglezakis, I.: Developing an Integrated Multilevel Help-Desk Support System. In: Proceedings of the 8th German Workshop on Case-Based Reasoning, pp. 145–155 (2000)Goker, M., Roth-Berghofer, T.: The development and utilization of the case-based help-desk support system HOMER. Engineering Applications of Artificial Intelligence 12, 665–680 (1999)Roth-Berghofer, T.R.: Learning from HOMER, a case-based help-desk support system. In: Melnik, G., Holz, H. (eds.) Advances in Learning Software Organizations, pp. 88–97. Springer, Heidelberg (2004)Bergmann, R., Althoff, K.D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. In: The INRECA Methodology, 2nd edn. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)eGain (2006), http://www.egain.comKaidara Software Corporation (2006), http://www.kaidara.com/Empolis Knowledge Management GmbH - Arvato AG (2006), http://www.empolis.com/Althoff, K.D., Auriol, E., Barletta, R., Manago, M.: A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report. Goodall, A., Oxford (1995)Watson, I.: Applying Case-Based Reasoning. Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California (1997)empolis: empolis Orenge Technology Whitepaper. Technical report, empolis GmbH (2002)Tissat, S.A. (2006), http://www.tissat.esGiraud-Carrier, C., Martinez, T.R.: An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research 3, 147–185 (1995)Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yanez, J.C.: Neuro-symbolic system for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 1–10. Springer, Heidelberg (2004)Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1997

Using Design Patterns, Analysis Pattern, and Case-Based Reasoning to Improve Information Modeling and Method Engineering in Systems Development

Information modeling (IM) is the process of identifying information needs and models based onuser requirements and systems analysts’ perceptions during systems analysis and design. WhenIM is done correctly, it facilitates communication between the analysts and end-users about thefinal software product. In addition, successful IM provides a formal basis for both the analystsand the end-users about the tools and techniques that will be used in software development(SD), which, in turn, reduces costly overruns in time and money during systemsimplementation. Method engineering (ME) is the process of designing, constructing, andadapting information modeling methods for information systems development. As Siau (2003)and Kavakli (2005) point out that, while there has been a steady increase in IM and ME research(e.g. Kawalek & Wastell 2003, Kavakli 2005, Matulevicius 2005), most of the models reportedin recent literature are still primarily based on common sense approach, and, as a result, lack aslid theoretical foundation.This paper discusses the feasibility of combining design patterns (DPs), analysis patterns (APs) andcase-based reasoning (CBR) to improve information modeling and method engineering. Recentresearch in DP, AP, and CBR has proven that all those methods are effective in softwaredevelopment. In this paper, we propose a model that combines DP, AP and CBR as a tool toimprove IM and ME. We believe that the use of DP and AP, along with CBR will facilitate easiercommunication among systems analysts, end-users and software engineers thus improve on heefficiency in software development. In the paper, we also provide illustrative examples fromaccounting systems design to show the effectiveness of our proposed model. Finally, we provideevidence in this paper that the practical application of DPs, APs and CBR to systems developmentmakes it possible to identify and resolve critical issues and risks at earlier stages in IM and ME, andeventually lead to high quality end product

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system.This work was supported in part by the EU's H 2020 research and innovation programme under the Marie SklodowskaCurie Grant Agreement 641794 (project DREAM-GO) and Grant Agreement 703689 (project ADAPT), in part by the FEDER Funds through COMPETE program, and in part by the National Funds through FCT under the Project UID/EEA/00760/2013. (Corresponding author: Tiago Pinto.)info:eu-repo/semantics/publishedVersio

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

[EN] This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system

Towards an agent-based framework for online after-sales services

The multi-agent paradigm for building intelligent systems has gradually been accepted by researchers and practitioners in the research field of artificial intelligence. There are also attempts of adapting agents and agent-based systems for creating industrial applications and providing e-services. In this paper, we present an attempt to use agents for constructing an online after-sale services system. The system is decomposed into four major cooperative agents, and in which each agent concentrates on particular aspects in the system and expresses intelligence by using various techniques. The proposed agent-based framework for the system is presented at both the micro-level and the macro-level according to the Gaia methodology. UML notations are also used to represent some software design models. As the result of this, agents are implemented into a framework for which exploits Case-Based Reasoning (CBR) technique to fulfil real life on-line services' diagnoses and tasks

Multi-domain case-based module for customer support

[EN] Technology management centres provide technological and customer support services for private or public organisations. Commonly, these centres offer support by using a helpdesk software that facilitates the work of their operators. In this paper, a CBR module that acts as a solution recommender for customer support environments is presented. The CBR module is flexible and multi-domain, in order to be easily integrable with any existing helpdesk software in the company. (c) 2008 Elsevier Ltd. All rights reserved.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under PROFIT FIT-340001-2004-11, CICYT TIN2005-03395 and TIN2006-14630-C0301 projectsHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Botti, V.; Rebollo Pedruelo, M.; Julian Inglada, VJ. (2009). Multi-domain case-based module for customer support. Expert Systems with Applications. 36(3):6866-6873. https://doi.org/10.1016/j.eswa.2008.08.003S6866687336

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

The Anatomy of Real-Time CRM

In the digital economy of the 21st century, the focus of production efficiency and product differentiation is shifted to value creation and relationship management. Customer relationship management is a critical business strategy in gaining competitive advantages. The ubiquity of the Internet has changed the way businesses are conducted. Real-time CRM is becoming increasingly significant to enable the agility of businesses to provide quick, accurate and complete responses to customer needs. This paper examines the structural makeup of real-time CRM that consists of e-business enabled CRM (ECRM), knowledge enabled CRM (KCRM) and business intelligence enabled CRM (ICRM). An architecture is developed for real-time CRM utilizing the components of e-business, knowledge-based systems, virtual data warehousing and real-time analytics

A Competency Mapping for Educational Institution: Expert System Approach

This paper presents the development of expert system to assist in the operation of competence management in educational institution. The knowledge based consists of a rule-based expert system for the competence management and subsequent performance assessment. It is generally recognized that an expert system can cope with many of the common problems relative with the operation and control of the competence management process. In this work an expert system is developed which emphasize on various steps involved in the competence management process. The knowledge acquisition to develop this expert system involved an exhaustive literature review on competence management operation and interviews with experienced deans and the competence managers. The development tool for this system is an expert system shell

A multi-agents based E-maintenance system with case-based reasoning decision support

International audienceToday, one challenge of a manufacturer is to maintain with the consumer, the expected service of the supplied product during the whole product life cycle, no matter where the product and the consumer are located. The combination of modern information processing and communication tools, commonly referred to as Tele-service, offers the technical support required to implement this remote service maintenance. However, this technical support is insufficient to face new remote maintenance decision-makings which requires not only informational exchanges between customers and suppliers but also cooperation and negotiation based on the sharing of different complementary and/or contradictory knowledge. It requires an evolution from Tele-service to E-service and e-Maintenance in particular where the maintenance decision-making results from collaboration of maintenance processes and experts to form a DAI environment. For this purpose, a Problem-Oriented Multi-Agent-Based E-Service System (POMAESS) is introduced in this paper. The protocol of negotiation for multi agents and the CBR-based decision support function within this system are discussed, emphasised at the service maintenance problem solving. A prototype system based on these methodologies is developed to demonstrate the feasibility