Abstract
Chatbots as conversational recommender have gained increasing importance for research and practice with a lot of applications available today. In this paper, we present the methods to support conversational defaults within a human-chatbot conversation that simplifies communication with the purpose of improving the overall recommendation process. In particular, we discuss our model-based reasoning approach for easing user experience during a chat, e.g., in cases where user preferences are mentioned indirectly causing inconsistencies. As a consequence of inconsistencies, it would not be possible for the chatbot to provide answers and recommendations. The presented approach allows for removing inconsistencies during the interactions with the chatbot. Besides the basic foundations, we provide use cases from the intended tourism domain to show the simplification of the conversation process. In particular, we consider recommendations for booking hotels and planning trips.
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References
Davis, R.: Diagnostic reasoning based on structure and behavior. Artif. Intell. 24, 347–410 (1984)
Fattah, Y.E., Dechter, R.: Diagnosing tree-decomposable circuits. In: Proceedings 14th International Joint Conference on Artificial Intelligence, pp. 1742–1748 (1995)
Felfernig, A., Friedrich, G., Schubert, M., Mandl, M., Mairitsch, M., Teppan, E.: Plausible repairs for inconsistent requirements. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 791–796, January 2009
Gottlob, G., Leone, N., Scarcello, F.: Hypertree decomposition and tractable queries. In: Proceedings of 18th ACM SIGACT SIGMOD SIGART Symposium on Principles of Database Systems (PODS 1999), pp. 21–32, Philadelphia, PA (1999)
Gottlob, G., Leone, N., Scarcello, F.: A comparison of structural CSP decomposition methods. Artif. Intell. 124(2), 243–282 (2000)
Jannach, D.: Finding preferred query relaxations in content-based recommenders. IEEE Intell. Syst. 109, 81–97 (2008)
Jannach, D., Zanker, M., Fuchs, M.: Constraint-based recommendation in tourism: a multiperspective case study. J. IT Tour. 11, 139–155 (2009)
de Kleer, J., Williams, B.C.: Diagnosing multiple faults. Artif. Intell. 32(1), 97–130 (1987)
Mahmood, T., Ricci, F., Venturini, A.: Learning adaptive recommendation strategies for online travel planning. Inf. Commun. Technol. Tour. 2009, 149–160 (2009)
Mauss, J., Sachenbacher, M.: Conflict-driven diagnosis using relational aggregations. In: Proceedings of the 10th International Workshop on Principles of Diagnosis (DX), Loch Awe, Scotland (1999)
Nica, I., Tazl, O., Wotawa, F.: Chatbot-based tourist recommendations using model-based reasoning. In: CEUR Workshop Proceedings, vol. 2220, pp. 25–30 (2018)
Nica, I., Wotawa, F.: ConDiag - computing minimal diagnoses using a constraint solver. In: Proceedings of the 23rd International Workshop on Principles of Diagnosis (2012)
O’Sullivan, B., Papadopoulos, A., Faltings, B., Pu, P.: Representative explanations for over-constrained problems. In: Proceedings of the National Conference on Artificial Intelligence, vol. 1, July 2007
Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)
Sachenbacher, M., Williams, B.C.: Diagnosis as semiring-based constraint optimization. In: European Conference on Artificial Intelligence, pp. 873–877 (2004)
Shawar, B.A., Atwell, E.: Using corpora in machine-learning chatbot systems. Int. J. Corpus Linguist. 10, 489–516 (2005)
Stumptner, M., Wotawa, F.: Diagnosing tree-structured systems. In: Proceedings 15th International Joint Conference on Artificial Intelligence, Nagoya, Japan (1997)
Stumptner, M., Wotawa, F.: Diagnosing tree-structured systems. Artif. Intell. 127(1), 1–29 (2001)
Stumptner, M., Wotawa, F.: Coupling CSP decomposition methods and diagnosis algorithms for tree-structured systems. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), pp. 388–393, Acapulco, Mexico (2003)
Sun, Y., Zhang, Y.: Conversational recommender system. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 235–244. ACM, New York (2018)
Wallace, R.: The anatomy of A.L.I.C.E. In: Epstein, R., Roberts, G., Beber, G. (eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer, pp. 181–210. Springer, Dordrecht (2009). https://doi.org/10.1007/978-1-4020-6710-5_13
Weizenbaum, J.: Eliza-a computer program for the study of natural language communication between man and machine. Commun. ACM 9(1), 36–45 (1966)
Acknowledgement
The research presented in the paper has been funded in part by the Cooperation Programme Interreg V-A Slovenia-Austria under the project AS-IT-IC (Austrian-Slovenian Intelligent Tourist Information Center).
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Tazl, O.A., Wotawa, F. (2019). Using Model-Based Reasoning for Enhanced Chatbot Communication. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_67
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