Skip to main content
SpringerLink
Log in

Intention model based multi-round dialogue strategies for conversational AI bots

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Conversational AI (CoAI) bot, such as customer service bots, navigation bots, chat bots and etc., is a new form of software application. How to accurately identify user requirements and provide appropriate services is one of the biggest challenges that hinder its widespread use. This requires CoAI to get rid of the template and standard knowledge structure, enhance the reasoning ability of service and the knowledge involved. Furthermore, It have the ability to guide users to express complete requirements through multiple rounds of dialogue. To achieve this, firstly, this study purposes a new intention model, and Knowledge Graph of Requirements (KGR) to expand the requirement knowledge scope of conversational AI bots. Secondly, this study purposes a knowledge processing method based on the theory of granular computing. This method could help CoAI bot to determine the next round of inquiry content automatically before the dialogue begins. At last, considering the commonality of the requirement pattern (RP) repository and the personalization of the KGR, this study proposes three optimization methods to improve the dialogue strategy based on two intention models for scenarios with different characteristics. The experimental result shows that, compared with the existing methods, the proposed method can reduce redundancy in dialogues, and integrate the commonality and individuality of user requirements successfully. Meanwhile, this method effectively improves the efficiency and performance of requirement elicitation, and takes the user experience into account as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. https://github.com/ownthink/KnowledgeGraphData

  2. https://github.com/linannn/APIN_2021_IM-based-MDS-for-CoAI-Bots

References

  1. Arora C, Sabetzadeh M, Nejati S, Briand L (2019) An active learning approach for improving the accuracy of automated domain model extraction. ACM Trans Softw Eng Methodol (TOSEM) 28(1):1–34

    Article  Google Scholar 

  2. Bezdek JC, Ehrlich R, Full W (1984) Fcm: The fuzzy c -means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  3. Boyer KE, Phillips R, Ingram A, Ha EY, Wallis M, Vouk M, Lester J (2010) Characterizing the effectiveness of tutorial dialogue with hidden markov models. In: International conference on intelligent tutoring systems. Springer, pp 55–64

  4. Chen C, Peng J, Wang F, Xu J, Wu H (2019) Generating multiple diverse responses with multi-mapping and posterior mapping selection

  5. Chen X, Zhi J (2016) Capturing requirements from expected interactions between software and its interactive environment: an ontology based approach. Int J Softw Eng Knowl Eng 26(1):15–39

    Article  Google Scholar 

  6. Dhankar A, Singh V (2016) User search intention in interactive data exploration: a brief review. In: International conference on advances in computing and data sciences. Springer, pp 409–419

  7. Gao J, Galley M, Li L (2018) Neural approaches to conversational ai. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 1371–1374

  8. Hathaway RJ, Davenport JW, Bezdek JC (1989) Relational duals of the c-means clustering algorithms. Pattern Recog 22(2):205–212

    Article  MathSciNet  Google Scholar 

  9. Higashinaka R, Sudoh K, Nakano M (2006) Incorporating discourse features into confidence scoring of intention recognition results in spoken dialogue systems. Speech Comm 48(3-4):417–436

    Article  Google Scholar 

  10. Hobbs JR (1990) Granularity. In: Readings in qualitative reasoning about physical systems. Elsevier, pp 542–545

  11. Hwang SY, Hsu CC, Lee CH (2017) Service selection for web services with probabilistic qos. IEEE Trans Serv Comput 8(3):467–480

    Article  Google Scholar 

  12. Jeon H, Kim T, Choi J (2008) Ontology-based user intention recognition for proactive planning of intelligent robot behavior. In: 2008 International conference on multimedia and ubiquitous engineering (mue 2008). IEEE, pp 244–248

  13. Jian X, Lin L, Wei Q, Yang J (2007) Srem: a service requirements elicitation mechanism based on ontology. In: International computer software & applications conference

  14. Johnson P (1992) Human computer interaction: psychology, task analysis, and software engineering McGraw-Hill

  15. Kaiya H (2006) Using domain ontology as domain knowledge for requirements elicitation. Proc.intl Req.eng.conf

  16. Khanna rJPT KGS Venkatesan Dr V (2015) Mining user profile exploitation cluster from computer program logs. International Journal of Pharmacy & Technology 3(3):1556–1561

    Google Scholar 

  17. Lieberman H, et al. (1995) Letizia: an agent that assists web browsing. IJCAI (1) 1995:924–929

    Google Scholar 

  18. Lin T (2003) Granular computing Springer International

  19. Lopatovska I, Rink K, Knight I, Raines K, Cosenza K, Williams H, Sorsche P, Hirsch D, Li Q, Martinez A (2019) Talk to me: exploring user interactions with the amazon alexa. J Librariansh Inf Sci 51(4):984–997

    Article  Google Scholar 

  20. Maiti RR, Mitropoulos FJ (2015) Capturing, eliciting, predicting and prioritizing (cepp) non-functional requirements metadata during the early stages of agile software development. In: Southeastcon 2015. IEEE, pp 1–8

  21. Milward D, Beveridge M (2003) Ontology-based dialogue systems. In: Proc. 3rd workshop on knowledge and reasoning in practical dialogue systems (IJCAI03), pp 9–18

  22. Peng M, Qin Y, Tang C, Deng X (2016) An e-commerce customer service robot based on intention recognition model. Journal of Electronic Commerce in Organizations (JECO) 14(1):34–44

    Article  Google Scholar 

  23. Pudlitz F, Brokhausen F, Vogelsang A (2019) Extraction of system states from natural language requirements. In: 2019 IEEE 27Th International requirements engineering conference (RE). IEEE, pp 211–222

  24. Serban I, Sordoni A, Bengio Y, Courville A, Pineau J (2016) Building end-to-end dialogue systems using generative hierarchical neural network models. In: Proceedings of the AAAI conference on artificial intelligence, vol 30

  25. Souag A, Salinesi C, Mazo R, Comyn-Wattiau I (2015) A security ontology for security requirements elicitation International Symposium on Engineering Secure Software and Systems (ESSos)

  26. Srinivasan P, Batri K (2013) Reducing replica of user query cluster-content and sub-hyperlinks in the search engine log based user profile. JJ Theor Appl Mech Inform Technol 52(3):357–365

    Google Scholar 

  27. Tian J, Tu Z, Wang Z, Xu X, Liu M (2020) User intention recognition and requirement elicitation method for conversational ai services. In: 2020 IEEE International conference on web services (ICWS). IEEE, pp 273–280

  28. Verma P, Yadava R (2015) Polymer selection for saw sensor array based electronic noses by fuzzy c-means clustering of partition coefficients: Model studies on detection of freshness and spoilage of milk and fish. Sensors & Actuators B Chemical 209:751–769

    Article  Google Scholar 

  29. Wang Z, Li J, Liu Z, Tang J (2016) Text-enhanced representation learning for knowledge graph. In: Proceedings of International joint conference on artificial intelligent (IJCAI), pp 4–17

  30. Wei Z, Liu Q, Peng B, Tou H, Chen T, Huang XJ, Wong KF, Dai X (2018) Task-oriented dialogue system for automatic diagnosis. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 2: Short Papers), pp 201–207

  31. Weining Y, Yue W, Guoping W, Shihai WHD (2005) Architecture of intelligent interaction systems based on context awareness. Journal of Computer-Aided Design & Computer Graphics 17(1):74–79

    Google Scholar 

  32. Xu H, Wang X, Wang Y, Li N, Tu Z, Wang Z, Xu X (2020) Domain priori knowledge based integrated solution design for internet of services. In: 2020 IEEE International conference on services computing (SCC). IEEE, pp 446–453

  33. Xu L, Zhou Q, Gong K, Liang X, Tang J, Lin L (2019) End-to-end knowledge-routed relational dialogue system for automatic diagnosis. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 7346–7353

  34. Xu Y, Yin J, Deng S, Xiong NN, Huang J (2016) Context-aware qos prediction for web service recommendation and selection. Expert Syst Appl 53(Jul.):75–86

    Article  Google Scholar 

  35. Yadgar O, Yorke-Smith N, Peintner B, Tur G, Ayan NF, Wolverton MJ, Acharya G, Parimi VS, Mark WS, Wang W et al (2017) Generic virtual personal assistant platform. US Patent 9,575,964

  36. Yang Y, Tang J (2015) Beyond query: interactive user intention understanding. In: 2015 IEEE International conference on data mining. IEEE, pp 519–528

  37. Zhang XL, Chi CH, Ding C, Wong RK (2013) Non-functional requirement analysis and recommendation for software services pp 555–562

  38. Zhang Y, Wallace B (2015) A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification Computer Science

  39. Zhu Q, Huang K, Zhang Z, Zhu X, Huang M (2020) Crosswoz: a large-scale chinese cross-domain task-oriented dialogue dataset. Transactions of the Association for Computational Linguistics 8:281–295

    Article  Google Scholar 

Download references

Acknowledgements

Research in this paper is partially supported by the National Key Research and Development Program of China (No 2018YFB1402500), the National Science Foundation of China (61802089, 61832004, 61772155, 61832014).

Author information

Authors and Affiliations

  1. Research Center of Intelligent Computing for Enterprises and Services, Harbin Institute of Technology, Harbin, China

    Junrui Tian, Zhiying Tu, Nan Li, Tonghua Su, Xiaofei Xu & Zhongjie Wang

Authors
  1. Junrui Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiying Tu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tonghua Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaofei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhongjie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhongjie Wang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, J., Tu, Z., Li, N. et al. Intention model based multi-round dialogue strategies for conversational AI bots. Appl Intell 52, 13916–13940 (2022). https://doi.org/10.1007/s10489-022-03288-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03288-8

Keywords

Search

Navigation