Abstract
Question selection in Conversational Case-Based Reasoning (CCBR) is traditionally guided by the discriminativeness of questions, to minimize dialog length for retrieval. However, users may not always be able or willing to answer the most discriminative questions. This paper presents Accessibility Influenced Attribute Selection Plus (AIAS+), a method for customizing CCBR question selection to reflect the types of questions the user is likely to answer. Given background knowledge about response probabilities for different questions by different user groups, AIAS+ performs ongoing classification of new users, based on the questions they choose to answer, uses the classifications to predict the likelihood of the user answering particular questions, and applies those predictions to guide question selection. In addition, its question selection process balances questions’ information gain against their potential to aid user classification, to enable better selection of following questions. Experiments with simulated users show improvement over three alternative methods. Experiments in synthetic domains illuminate the domain characteristics under which the method is expected to be effective.
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References
Aha, D., Breslow, L., Munoz-Avila, H.: Conversational case-based reasoning. Applied Intelligence 14, 9–32 (2001)
Jalali, V., Leake, D.: Customizing question selection in conversational case-based reasoning. In: Proceedings of the 2012 Florida AI Research Symposium, FLAIRS (in press, 2012)
Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Mateo (1997)
Shimazu, H., Shibata, A., Nihei, K.: Expertguide: A conversational case-based reasoning tool for developing mentors in knowledge spaces. Appl. Intell. 14(1), 33–48 (2001)
Aha, D.W., McSherry, D., Yang, Q.: Advances in conversational case-based reasoning. Knowl. Eng. Rev. 20(3), 247–254 (2005)
Aha, D.W., Maney, T., Breslow, L.A.: Supporting Dialogue Inferencing in Conversational Case-Based Reasoning. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 262–273. Springer, Heidelberg (1998)
Gupta, K.M.: Taxonomic Conversational Case-Based Reasoning. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 219–233. Springer, Heidelberg (2001)
Gu, M., Aamodt, A.: A Knowledge-Intensive Method for Conversational CBR. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 296–311. Springer, Heidelberg (2005)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Doyle, M., Cunningham, P.: A Dynamic Approach to Reducing Dialog in On-Line Decision Guides. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 49–60. Springer, Heidelberg (2000)
McSherry, D.: Minimizing dialog length in interactive case-based reasoning. In: Proceedings of the seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 993–998. Morgan Kaufmann, San Mateo (2001)
Göker, M.H., Roth-Berghofer, T.R., Bergmann, R., Pantleon, T., Traphöner, R., Wess, S., Wilke, W.: The Development of HOMER: A Case-Based CAD/CAM Help-Desk Support Tool. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 346–357. Springer, Heidelberg (1998)
Shimazu, H.: Expertclerk: Navigating shoppers’ buying process with the combination of asking and proposing. In: Proceedings of the Seventeenth International Joint Conference on Articial Intelligence, pp. 1443–1448 (2001)
Yang, Q., Wu, J.: Enhancing the effectiveness of interactive case-based reasoning with clustering and decision forest. Computational Intelligence 14(1), 49–64 (2001)
McSherry, D.: Conversational case-based reasoning in medical decision making. Artificial Intelligence in Medicine 52(2), 59–66 (2011)
McSherry, D.: Increasing dialogue efficiency in case-based reasoning without loss of solution quality. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI 2003), pp. 121–126. Morgan Kaufmann, San Mateo (2003)
Carrick, C., Yang, Q., Abi-Zeid, I., Lamontagne, L.: Activating CBR Systems through Autonomous Information Gathering. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 74–88. Springer, Heidelberg (1999)
Kohlmaier, A., Schmitt, S., Bergmann, R.: A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogs. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 306–320. Springer, Heidelberg (2001)
Thompson, C., Göker, M., Langley, P.: A personalized system for conversational recommendations. Artificial Intelligence Research (JAIR) 21, 393–428 (2004)
Ricci, F., Venturini, A., Cavada, D., Mirzadeh, N., Blaas, D., Nones, M.: Product Recommendation with Interactive Query Management and Twofold Similarity. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 479–493. Springer, Heidelberg (2003)
Mirzadeh, N., Ricci, F., Bansal, M.: Feature selection methods for conversational recommender systems. In: IEEE International Conference on e-Technology, e-Commerce, and e-Services, pp. 772–777 (2005)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
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Jalali, V., Leake, D. (2012). Custom Accessibility-Based CCBR Question Selection by Ongoing User Classification. In: Agudo, B.D., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2012. Lecture Notes in Computer Science(), vol 7466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_16
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DOI: https://doi.org/10.1007/978-3-642-32986-9_16
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