ABSTRACT
The usability of touch-tone Interactive Voice Response (IVR) systems is dismal. Clients would rather speak to a contact centre agent than navigate through the menu structure found in these systems. Contact centres, due to a variety of reasons, most notably high personnel costs, tend to utilise IVR as their solution for automation. IVR is an example of a simple forward chaining rule based expert system. An evaluation was conducted to determine whether a natural language interface would provide a more effective automation technique in comparison to current techniques utilised by contact centres. This evaluation compared the advantages and disadvantages of a natural language interface and a rule based expert system interface (modelled to resemble an IVR) and concluded that a hybrid system utilising a combination of these techniques would provide a better solution. This paper discusses two models that could be employed in the combination of a rule based expert system with a natural language interface.
- Abraham, A. 2005. Rule-Based Expert Systems. Handbook of measuring system design. Oklahoma state University, Stillwater, OK, USA.Google Scholar
- Davis, R. and King, J. J. 1977. The Origin of Rule-Based Systems in AI.Google Scholar
- Dimension Data, 2006. Merchants Global Contact Centre Benchmarking Report 2006. Dimension Data: Pages 1 --120Google Scholar
- Gans, N., Koole, G., and Mandelbaum, A. 2003. Commissioned Paper - telephone call centres: Tutorial, Review and Research Prospects. Manufacturing & Service operations management, vol.5, no.2, pp. 79--141. Google ScholarDigital Library
- Giarrantano, J. and Riley, G. 1989. Expert systems: Principles and Programming. Thomson Information/Publishing Group. USA. Google ScholarDigital Library
- Gupta, A., Forgy, C., and Newell, A. 1989. High Speed Implementations of Rule-based Systems. ACM Transactions on Computer Systems. Vol.7, No.2, May 1989, Pages 119--146. Google ScholarDigital Library
- Hayes-Roth, F. 1985. Rule-Based Expert Systems. Communications of ACM. ACM.Google Scholar
- Kaish, S. 2004. Challenges for customer care centre - Enhancing services and reducing costs with Cosmocall Universe. DOI=http://www.cosmocom.com/whitepapers/whitepaper-telecustomercare.htmGoogle Scholar
- Khan, G. and McDermott, J. 1984. The MUD system. In the first conference on artificial intelligence applications, IEEE Computer Society and AAAI, Dec. 1984.Google Scholar
- Lin, J., Quan, D., Sinha, V., Bakshi, K., Huynh, D., Katz, B., and Karger. D. R. 2003. What makes a good answer? The Role of Context in Question Answering. MIT AI library. Cambridge, MA 02138, USA.Google Scholar
- Loebner Prize, 2008. Loebner Prize for Artificial Intelligence. DOI = http://www.loebner.net/Prizef/loebner-prize.htmlGoogle Scholar
- Long, B., 1994. Natural Language as an Interface style. DOI = http://www.dgp.toronto.edu/people/byron/papers/nli.htmlGoogle Scholar
- Mandelbaum, A. and Koole, G. 2001. Queuing Models of Call Centres: An Introduction. DOI http://www.cs.vu.nl/obp.callcentresGoogle Scholar
- Moerdler, G. D., McKeown, K. R., and Ensor, J. R. 1987. Building natural language interfaces for rule based expert systems.Google Scholar
- Munteanu, C., and Boldea, M. 2000. MDWOZ: A Wizard of OZ Environment for Dialog Systems Development. In proceedings of LREC.Google Scholar
- Owda, M., Bandar, Z., and Crockett, K. 2007. Coversational-Based Natural Language Interface to Relational Databases. International conference on Web Intelligence and Intelligent Agent Technology. Google ScholarDigital Library
- Preece, J., Rogers, J., and Sharp, H. 2002. Interaction Design: Beyond Human - Computer Interaction. Wiley. ISBN: 9780471492788 Google ScholarDigital Library
- Saluja, A. S. 2006. A Wake up call for your IVR.Google Scholar
- Steel, A. 2003. Understanding and Enhancing Call Centre Computer-Human-Human Interaction. Proceedings on human factors in computing systems. Florida. New York, NY, ACM Press. Google ScholarDigital Library
- Wallace, R. S. 2005. Be your own botmaster: The step by step guide to creating, hosting and selling your own A.I. chatbot on Pandorabots. ALICE A.I. FoundationGoogle Scholar
Index Terms
- Models towards a hybrid conversational agent for contact centres
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