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

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

May the Guide Be With You: CA-facilitated Information Elicitation to Prevent Service Failure

Companies automate the delivery of their online services by deploying artificial intelligence-based conversational agents (CAs). However, contemporary CAs still struggle to reliably answer the full range of requests from support seekers. To avoid service failure, service delivery activities of CAs and service employees should be interconnected by a handover of requests. This form of hybrid service delivery requires support seekers to disclose relevant information so that CAs can relay them to service employees prior to an imminent failure. By integrating and extending design knowledge from two DSR projects, we derive four design principles (DPs) to prepare handovers. These DPs guided the implementation of a service script in a CA prototype to facilitate the elicitation of information from support seekers. Based on two evaluation episodes, we show that support seekers feel supported by the CA in disclosing information which results in elaborate input for subsequent processing by service employees after handover

RBC para o problema de suporte ao cliente nas empresas de prestação de serviço de software :: o caso Datasul /

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico

An intelligent modelling interface for process simulators in process industries

Over the past three decades, modelling packages for chemical processes have become more advanced and widely used. For example, equation-oriented dynamic simulators, such as gPROMS are useful for simulating plantwide processes as well as unit operations, and are widely used by process engineers. Whereas, other types of simulator (e.g. Simulink) are often used by control engineers to solve complex control problems. However, both these types of simulator rely on the user being proficient in modelling and familiar with their syntax beforehand. A useful development would be the integration of some knowledge into the formation of the process models and automatic syntax code generation. This would lead to the design engineers having a library of knowledge to check on first, much as an expert engineer uses their past experiences to help guide them through a design. If this could be incorporated into a modelling interface this would greatly help the design engineer, especially when tackling problems in areas that they have little, or no experience. The thesis addresses this problem and describes the design of an intelligent modelling interface that incorporates a knowledge base using some form of a priori case library and recall facility. The interface also incorporates an automatic input file generation stage. At present, the user can: specify a single unit operation problem to search for, retrieve similar cases from the database, specify their solution in the database based on past cases and experience, and then automatically generate an input file for either gPROMS or Simulink. These features are demonstrated through four case studies

Temporal case-based reasoning for insulin decision support

Type 1 diabetes mellitus is an autoimmune disease resulting in insucient insulin to regulate blood glucose levels. The condition can be successfully managed through eective blood glucose control, one aspect of which is the administration of bolus insulin. Formulas exist to estimate the required bolus, and have been adopted by existing mobile expert systems. These formulas are shown to be eective but are unable to automatically adapt to an individual. This research resolves the limitations of existing formula based calculators by using case-based reasoning to automatically improve bolus advice. Case-based reasoning is a method of articial intelligence that has successfully been adopted in the diabetes domain previously, but has primarily been limited to assisting doctors with therapy adjustments. Here case-based reasoning is instead used to directly assist the patient. The case-based reasoning process is enhanced for bolus advice through a temporal retrieval algorithm coupled with domain specic automated adjustment and revision. This temporal retrieval algorithm includes factors from previous events to improve the prediction of a bolus dose. The automated adjustment then renes the predicted bolus dose, and automated revision improves the prediction for future advice through the evaluation of the resulting blood glucose level. Analysis of the temporal retrieval algorithm found that it is capable of predicting bolus advice comparable to closed-loop simulation and existing formulas, with adapted advice resulting in improvements to simulated blood glucose control. The learning potential of the model is made evident through further improvements in blood glucose control when using revised advice. The system is implemented on a mobile device with a focus on safety using formal methods to help ensure actions performed do not violate the system constraints. Performance analysis demonstrated acceptable response times, providing evidence that this approach is viable. The research demonstrates how formula based mobile bolus calculators can be replaced by an articially intelligent alternative which continuously learns to improve advice

A utilização de uma ferramenta da inteligência artificial aplicada a resolução de não conformidades do sistema de saída de emergência das edificações

Dissertação (Mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico.Esta pesquisa trata da modelagem de um sistema de recuperação de informações referentes à resolução de não conformidades detectadas no sistema de saída de emergência das edificações, com o objetivo de proporcionar aos profissionais do Corpo de Bombeiros, suporte à tomada de decisão. Para sistematização das informações, desenvolveu-se uma ferramenta computacional aplicando as técnicas do Raciocínio Baseado em Casos. Esta ferramenta permitirá ao Corpo de Bombeiros gerenciar os processos de aprendizagem baseados em casos passados e já solucionados. O uso de informações adquiridas através das experiências passadas, possibilita ao Corpo de Bombeiros projetar mudanças e ações futuras, bem como, racionalizar o tempo gasto na execução de suas atividades em fiscalização, pesquisa e desenvolvimento de novas soluções