Exploiting Taxonomic and Causal Relations
in Conversational Case Retrieval

Kalyan Moy Gupta1, David W. Aha2, and Nabil Sandhu1

1ITT Industries, AES Division, Alexandria, VA 22303
2Navy Center for Applied Research in Artificial Intelligence,
Naval Research Laboratory (Code 5515), Washington, DC 20375
{Gupta, Aha, Sandhu}@aic.nrl.navy.mil



Abstract. Conversational case-based reasoning (CCBR) systems engage their
users in a series of questions and answers and present them with cases that are
most applicable to their decision problem. In previous research, we introduced
the Taxonomic CCBR methodology, an extension of standard CCBR that
improved performance by organizing features related by abstraction into
taxonomies. We recently extended this methodology to include causal relations
between taxonomies and claimed that it could yield additional performance
gains. In this paper, we formalize the causal extension of Taxonomic CCBR,
called Causal CCBR, and empirically assess its benefits using a new
methodology for evaluating CCBR performance. Evaluation of Taxonomic and
Causal CCBR systems in troubleshooting and customer support domains
demonstrates that they significantly outperform the standard CCBR approach.
In addition, Causal CCBR outperforms Taxonomic CCBR to the extent causal
relations are incorporated in the case bases.
References

Acorn, T.L., & Walden, S.H. (1992). SMART: Support management automated
reasoning technology for COMPAQ customer service. Proceedings of the Fourth
Annual Conference on Innovative Applications of Artificial Intelligence. San Jose,
CA: AAAI Press.
Aha, D.W., & Gupta K.M. (2002). Causal query elaboration in conversational case-based 
reasoning. In Proceedings of the Fifteenth Conference of the Florida AI
Research Society (pp. 95-100). Pensacola Beach, FL: AAAI Press.
Aha, D.W., Breslow, L.A., & Munoz-Avila, H. (2001). Conversational case-based
reasoning. Applied Intelligence, 14(1), 9-32.
Aha, D. W., Maney, T., & Breslow, L. A. (1998). Supporting dialogue inferencing in
conversational case-based reasoning. Proceedings of the Fourth European
Workshop on Case-Based Reasoning (pp. 262-273). Dublin, Ireland: Springer.
Carrick, C., Yang, Q., Abi-Zeid, I., & Lamontagne, L. (1999). Activating CBR
systems through autonomous information gathering. Proceedings of the Third
International Conference on Case-Based Reasoning (pp. 74-88). Seeon, Germany:
Springer.
Doyle, M., & Cunningham, P. (2000). A dynamic approach to reducing dialog in on-line 
decision guides. Proceedings of the Fifth European Workshop on Case-Based
Reasoning (pp. 49-60). Trento, Italy: Springer.
Giampapa, J.A., & Sycara, K. (2001). Conversational case-based planning for agent
team coordination. Proceedings of the Fourth International Conference on Case-Based 
Reasoning (pp. 189-203). Vancouver, Canada: Springer.
Gker, M., Roth-Berghofer, T., Bergmann, R., Pantleon, T., Traphoner, R., Wess, S.,
& Wilke, W. (1998). The development of HOMER: A case-based CAD/CAM
help-desk support tool. Proceedings of the Fourth European Workshop on Case-Based 
Reasoning (pp. 346-357). Dublin, Ireland: Springer.
Gker, M., & Thompson, C.A. (2000). Personalized conversational case-based
recommendation. Proceedings of the Fifth European Workshop on Case-Based
Reasoning (pp. 99-111). Trento, Italy: Springer.
Gupta K.M. (2001). Taxonomic case-based reasoning. Proceedings of the Fourth
International Conference on Case-Based Reasoning (pp. 219-233). Vancouver,
Canada:	Springer.
Gupta, K.M. (1998). Knowledge-based system for troubleshooting complex
equipment. International Journal of Information and Computing Science, 1(1), 29-
41.
Gupta, K.M. (1997). Case base engineering for large-scale industrial applications. In
B.R. Gaines & R. Uthurusamy (Eds.) Intelligence in Knowledge Management:
Papers from the AAAI Spring Symposium (Technical Report SS-97-01). Stanford,
CA: AAAI Press.
Kohlmaier, A., Schmitt, S., & Bergmann, R. (2001). A similarity-based approach to
attribute selection in user-adaptive sales dialogs. Proceedings of the Fourth
International Conference on Case-Based Reasoning (pp. 306-320). Vancouver,
Canada: Springer.
Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.
Lenz, M., & Burkhard H. D. (1996). Case retrieval nets: Basic ideas and extensions.
In G. Grz & S. Hlldobler (Eds.) KI-96: Advances in Artificial Intelligence.
Berlin: Springer.
McSherry, D. (2001a). Interactive case-based reasoning in sequential diagnosis.
Applied Intelligence, 14(1), 65-76.
McSherry, D. (2001b). Minimizing dialog length in interactive case-based reasoning.
Proceedings of the Seventeenth International Joint Conference on Artificial
Intelligence (pp. 993-998). Seattle, WA: Morgan Kaufmann.
Montazemi A.R., & Gupta K.M. (1996), An adaptive agent for case description in
diagnostic CBR systems. Computers in Industry, 29(3), 209-224.
Shimazu, H. (2001). ExpertClerk: Navigating shoppers buying process with the
combination of asking and proposing. Proceedings of the Seventeenth International
Conference on Artificial Intelligence (pp. 1443-1448). Seatfie, WA: Morgan Kaufmann.
Shimazu, H., Shibata, A., & Nihei, K. (1994). Case-based retrieval interface adapted to
customer-initiated dialogues in help desk operations. Prnceedings of the Twelfth National
Conference on Artificial Intelligence (pp.513-518). Seattle, WA: AAAI Press.
Yang, Q., & Wu, J. (2001). Enhancing the effectiveness of interactive case-based
reasoning with clustering and decision forests. Applied Intelligence, 14(1),
49-64.
