Skip to main content
SpringerLink
Log in

Dialogue Explanations for Rule-Based AI Systems

  • Conference paper
  • First Online:
Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14127))

Abstract

The need for AI systems to explain themselves is increasingly recognised as a priority, particularly in domains where incorrect decisions can result in harm and, in the worst cases, death. Explainable Artificial Intelligence (XAI) tries to produce human-understandable explanations for AI decisions. However, most XAI systems prioritize factors such as technical complexities and research-oriented goals over end-user needs, risking information overload. This research attempts to bridge a gap in current understanding and provide insights for assisting users in comprehending the rule-based system’s reasoning through dialogue. The hypothesis is that employing dialogue as a mechanism can be effective in constructing explanations. A dialogue framework for rule-based AI systems is presented, allowing the system to explain its decisions by engaging in “Why?” and “Why not?” questions and answers. We establish formal properties of this framework and present a small user study with encouraging results that compares dialogue-based explanations with proof trees produced by the AI System.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We don’t need to label rules for our system to work, but labels are a useful convenience when referring to rules.

References

  1. Apt, K.R., Van Emden, M.H.: Contributions to the theory of logic programming. J. ACM (JACM) 29(3), 841–862 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  2. Arioua, A., Tamani, N., Croitoru, M.: Query answering explanation in inconsistent datalog\(+/-\) knowledge bases. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds.) DEXA 2015. LNCS, vol. 9261, pp. 203–219. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22849-5_15

    Chapter  Google Scholar 

  3. Bex, F., Walton, D.: Combining explanation and argumentation in dialogue. Argument Comput. 7(1), 55–68 (2016)

    Article  Google Scholar 

  4. Cavedon, L., Lloyd, J.: A completeness theorem for SLDNF resolution. J. Logic Program. 7(3), 177–191 (1989). https://www.sciencedirect.com/science/article/pii/0743106689900204

  5. Clancey, W.J.: The epistemology of a rule-based expert system-a framework for explanation. Artif. Intell. 20(3), 215–251 (1983)

    Article  Google Scholar 

  6. Clocksin, W.F., Mellish, C.S.: Programming in Prolog, 5 edn. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-642-55481-0

  7. Cocarascu, O., Stylianou, A., Čyras, K., Toni, F.: Data-empowered argumentation for dialectically explainable predictions. In: ECAI 2020, pp. 2449–2456. IOS Press (2020)

    Google Scholar 

  8. Dennis, L.A., Oren, N.: Explaining BDI agent behaviour through dialogue. In: Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) (2021)

    Google Scholar 

  9. Fiedler, A.: Dialog-driven adaptation of explanations of proofs. In: International Joint Conference on Artificial Intelligence, vol. 17, pp. 1295–1300. Citeseer (2001)

    Google Scholar 

  10. Huth, M., Ryan, M.: Logic in Computer Science: Modelling and Reasoning about Systems. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  11. Johnson-Laird, P.N.: Mental models in cognitive science. Cogn. Sci. 4(1), 71–115 (1980)

    Article  Google Scholar 

  12. Kass, R., Finin, T., et al.: The need for user models in generating expert system explanations. Int. J. Expert Syst. 1(4) (1988)

    Google Scholar 

  13. Lacave, C., Díez, F.J.: A review of explanation methods for Bayesian networks. Knowl. Eng. Rev. 17(2), 107–127 (2002)

    Article  Google Scholar 

  14. Lacave, C., Diez, F.J.: A review of explanation methods for heuristic expert systems. Knowl. Eng. Rev. 19(2), 133–146 (2004)

    Article  Google Scholar 

  15. Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: A grounded interaction protocol for explainable artificial intelligence. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1033–1041 (2019)

    Google Scholar 

  16. Miller, T., Howe, P., Sonenberg, L.: Explainable AI: beware of inmates running the asylum or: how i learnt to stop worrying and love the social and behavioural sciences. arXiv preprint arXiv:1712.00547 (2017)

  17. Moore, J.D., Paris, C.L.: Requirements for an expert system explanation facility. Comput. Intell. 7(4), 367–370 (1991)

    Article  Google Scholar 

  18. Oren, N., van Deemter, K., Vasconcelos, W.W.: Argument-based plan explanation. In: Vallati, M., Kitchin, D. (eds.) Knowledge Engineering Tools and Techniques for AI Planning. LNCS, pp. 173–188. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38561-3_9

    Chapter  Google Scholar 

  19. Reggia, J.A., Perricone, B.T.: Answer justification in medical decision support systems based on Bayesian classification. Comput. Biol. Med. 15(4), 161–167 (1985)

    Article  Google Scholar 

  20. Sendi, N., Abchiche-Mimouni, N., Zehraoui, F.: A new transparent ensemble method based on deep learning. Procedia Comput. Sci. 159, 271–280 (2019)

    Article  Google Scholar 

  21. Shams, Z., et al.: REM: an integrative rule extraction methodology for explainable data analysis in healthcare (2021)

    Google Scholar 

  22. Shortliffe, E.H., Axline, S.G., Buchanan, B.G., Merigan, T.C., Cohen, S.N.: An artificial intelligence program to advise physicians regarding antimicrobial therapy. Comput. Biomed. Res. 6(6), 544–560 (1973)

    Article  Google Scholar 

  23. Singh, R., Miller, T., Newn, J., Sonenberg, L., Velloso, E., Vetere, F.: Combining planning with gaze for online human intention recognition. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 488–496 (2018)

    Google Scholar 

  24. Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)

    Article  MATH  Google Scholar 

  25. Swartout, W.R.: XPLAIN: a system for creating and explaining expert consulting programs. Artif. Intell. 21(3), 285–325 (1983)

    Article  Google Scholar 

  26. Vassiliades, A., Bassiliades, N., Patkos, T.: Argumentation and explainable artificial intelligence: a survey. Knowl. Eng. Review 36, e5 (2021)

    Article  Google Scholar 

  27. Walton, D.: A dialogue system specification for explanation. Synthese 182, 349–374 (2011)

    Article  Google Scholar 

  28. Walton, D.: A Dialogue System for Evaluating Explanations, pp. 69–116. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-19626-8_3

  29. Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artif. Intell. 54(1–2), 33–70 (1992)

    Article  Google Scholar 

  30. Winikoff, M., Sidorenko, G., Dignum, V., Dignum, F.: Why bad coffee? Explaining BDI agent behaviour with valuings. Artif. Intell. 300, 103554 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  31. Zarlenga, M.E., Shams, Z., Jamnik, M.: Efficient decompositional rule extraction for deep neural networks. arXiv preprint arXiv:2111.12628 (2021)

Download references

Acknowledgement

This work is supported by EPSRC, through EP/W01081X (Computational Agent Responsibility).

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Manchester, Manchester, UK

    Yifan Xu, Joe Collenette, Louise Dennis & Clare Dixon

Authors
  1. Yifan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joe Collenette
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Louise Dennis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Clare Dixon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yifan Xu .

Editor information

Editors and Affiliations

  1. University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland

    Davide Calvaresi

  2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Amro Najjar

  3. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Andrea Omicini

  4. Ozyegin University, Istanbul, Türkiye

    Reyhan Aydogan

  5. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Rachele Carli

  6. Alma Mater Studiorum, Università di Bologna, Bologna, Italy

    Giovanni Ciatto

  7. Université de Technologie de Belfort-Montbéliard, Belfort Cedex, France

    Yazan Mualla

  8. Umeå University, Umeå, Sweden

    Kary Främling

Ethics declarations

Data Access Statement

The code and data supporting the findings reported in this paper are available for open access at https://github.com/xuLily9/RBS_TheoryI (Code) and https://doi.org/10.6084/m9.figshare.22220494.v3 (User Evaluation).

Ethical Approval

We performed a light-touch ethical review for the user evaluation, using a tool provided by our university. This tool advised that since the only personal data gathered was names on consent forms and these were stored in a locked cabinet separate from the rest of the gathered data, further ethical approval was not required.

Open Access

For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, Y., Collenette, J., Dennis, L., Dixon, C. (2023). Dialogue Explanations for Rule-Based AI Systems. In: Calvaresi, D., et al. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023. Lecture Notes in Computer Science(), vol 14127. Springer, Cham. https://doi.org/10.1007/978-3-031-40878-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40878-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40877-9

  • Online ISBN: 978-3-031-40878-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation