Compositional Adaptation of Explanations in Textual Case-Based Reasoning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9969)

Abstract

When problem solving systems are deployed in real life, it is usually not enough to provide only a solution without any explanation. Users need an explanation in order to trust the system’s decisions. At the same time, explanations may also function internally in the system’s own reasoning process. One way to come up with an explanation for a new problem is to adapt an explanation from a similar problem encountered earlier, which is the idea behind the case-based explanation approach introduced by [29]. The original approach relies on manual construction of cases with explanations, which is difficult to scale up. In earlier work, therefore, we developed a system for automatic acquisition of cases with explanations from textual reports, including retrieval and adaptation of such cases [32, 33]. In this paper, we improve the adaptation method by combining explanations from more than one case, which we call compositional adaptation. The method is evaluated on an incident analysis task where the goal is to identify the root causes of a transportation incident, explaining it in terms of the information contained in the incident description. The evaluation results show that the proposed approach increases both the recall and the precision of the system.

Keywords

Textual case-based reasoning Compositional adaptation Explanation Incident analysis 

References

  1. 1.
    Aamodt, A.: A knowledge-intensive, integrated approach to problem solving and sustained learning. Knowledge Engineering and Image Processing Group. University of Trondheim, pp. 27–85 (1991)Google Scholar
  2. 2.
    Aamodt, A.: Knowledge-intensive case-based reasoning in CREEK. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 1–15. Springer, Heidelberg (2004). doi:10.1007/978-3-540-28631-8_1 CrossRefGoogle Scholar
  3. 3.
    Abedin, M.A.U., Ng, V., Khan, L.: Cause identification from aviation safety incident reports via weakly supervised semantic lexicon construction. J. Artif. Intell. Res. 38(1), 569–631 (2010)Google Scholar
  4. 4.
    Adeyanju, I., Wiratunga, N., Recio-García, J.A., Lothian, R.: Learning to author text with textual CBR. In: ECAI, pp. 777–782 (2010)Google Scholar
  5. 5.
    Aha, D.W., Breslow, L.A., Muñoz-Avila, H.: Conversational case-based reasoning. Appl. Intell. 14(1), 9–32 (2001)CrossRefMATHGoogle Scholar
  6. 6.
    Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. J. Artif. Intell. Res. 38, 135–187 (2010)MATHGoogle Scholar
  7. 7.
    Arshadi, N., Badie, K.: A compositional approach to solution adaptation in case-based reasoning and its application to tutoring library. In: Proceedings of 8th German Workshop on Case-Based Reasoning, Lammerbuckel (2000)Google Scholar
  8. 8.
    Bergmann, R., Pews, G., Wilke, W.: Explanation-based similarity: a unifying approach for integrating domain knowledge into case-based reasoning for diagnosis and planning tasks. In: Wess, S., Althoff, K.-D., Richter, M.M. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 182–196. Springer, Heidelberg (1994). doi:10.1007/3-540-58330-0_86 CrossRefGoogle Scholar
  9. 9.
    Bridge, D., Gomes, P., Seco, N.: Analysing air incident reports: workshop challenge. In: Proceedings of the 4th Workshop on Textual Case-Based Reasoning (2007)Google Scholar
  10. 10.
    Brüninghaus, S., Ashley, K.D.: Combining case-based and model-based reasoning for predicting the outcome of legal cases. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 65–79. Springer, Heidelberg (2003). doi:10.1007/3-540-45006-8_8 CrossRefGoogle Scholar
  11. 11.
    Carthy, J., Wilson, D.C., Wang, R., Dunnion, J., Drummond, A.: Using T-Ret system to improve incident report retrieval. In: Gelbukh, A. (ed.) CICLing 2004. LNCS, vol. 2945, pp. 468–471. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24630-5_57 CrossRefGoogle Scholar
  12. 12.
    Clancey, W.J.: The epistemology of a rule-based expert system–a framework for explanation. Artif. Intell. 20(3), 215–251 (1983)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Cox, M.T., Ram, A.: Introspective multistrategy learning: on the construction of learning strategies. Artif. Intell. 112(1–2), 1–55 (1999)CrossRefMATHGoogle Scholar
  14. 14.
    Cunningham, P., Doyle, D., Loughrey, J.: An evaluation of the usefulness of case-based explanation. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 122–130. Springer, Heidelberg (2003). doi:10.1007/3-540-45006-8_12 CrossRefGoogle Scholar
  15. 15.
    Gupta, K.M., Aha, D.W.: Conversation for textual case-based reasoning. In: Proceedings of the 4th Workshop on Textual Case-Based Reasoning (2007)Google Scholar
  16. 16.
    Kass, A.M., Leake, D.B.: Case-based reasoning applied to constructing explanations. In: Proceedings of a Workshop on Case-Based Reasoning, pp. 190–208. Holiday Inn., Clearwater Beach (1988)Google Scholar
  17. 17.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Leacock, C., Miller, G.A., Chodorow, M.: Using corpus statistics and WordNet relations for sense identification. Comput. Linguist. 24(1), 147–165 (1998)Google Scholar
  19. 19.
    Leake, D.B.: Evaluating Explanations: A Content Theory. L. Erlbaum Associates, Hillsdale (1992)Google Scholar
  20. 20.
    Leake, D.B., Birnbaum, L., Hammond, K., Marlow, C., Yang, H.: An integrated interface for proactive, experience-based design support. In: Proceedings of the 6th International Conference on Intelligent User Interfaces, pp. 101–108. ACM (2001)Google Scholar
  21. 21.
    Massie, S., Craw, S., Wiratunga, N.: Visualisation of case-base reasoning for explanation. In: Proceedings of the ECCBR 2004 Workshops, Madrid, pp. 135–144. Citeseer (2004)Google Scholar
  22. 22.
    Massie, S., Wiratunga, N., Craw, S., Donati, A., Vicari, E.: From anomaly reports to cases. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 359–373. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74141-1_25 CrossRefGoogle Scholar
  23. 23.
    McArdle, G., Wilson, D.: Visualising case-base usage. In: Workshop Proceedings ICCBR, pp. 105–114 (2003)Google Scholar
  24. 24.
    McSherry, D.: Explanation in recommender systems. Artif. Intell. Rev. 24(2), 179–197 (2005)CrossRefMATHGoogle Scholar
  25. 25.
    Miller, G.A.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  26. 26.
    Orecchioni, A., Wiratunga, N., Massie, S., Chakraborti, S., Mukras, R.: Learning incident causes. In: Proceedings of the 4th Workshop on Textual Case-Based Reasoning (2007)Google Scholar
  27. 27.
    Plaza, E., Armengol, E., Ontañón, S.: The explanatory power of symbolic similarity in case-based reasoning. Artif. Intell. Rev. 24(2), 145–161 (2005)CrossRefMATHGoogle Scholar
  28. 28.
    Recio-Garcıa, J.A., Dıaz-Agudo, B., González-Calero, P.A.: Textual CBR in jCOLIBRI: from retrieval to reuse. In: Proceedings of the ICCBR 2007 Workshop on Textual Case-Based Reasoning: Beyond Retrieval, pp. 217–226. Citeseer (2007)Google Scholar
  29. 29.
    Schank, R.C.: Explanation: a first pass. In: Kolodner, J., Riesbeck, C. (eds.) Experience, Memory, and Reasoning, pp. 139–165. Lawrence Erlbaum associates, Hillsdale (1986)Google Scholar
  30. 30.
    Schank, R.C., Kass, A.: Inside Case-Based Explanation. Psychology Press, Hove (1994)Google Scholar
  31. 31.
    Schank, R.C., Leake, D.B.: Creativity and learning in a case-based explainer. Artif. Intell. 40(1), 353–385 (1989)CrossRefGoogle Scholar
  32. 32.
    Sizov, G., Öztürk, P., Aamodt, A.: Evidence-driven retrieval in textual CBR: bridging the gap between retrieval and reuse. In: Hüllermeier, E., Minor, M. (eds.) ICCBR 2015. LNCS (LNAI), vol. 9343, pp. 351–365. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24586-7_24 CrossRefGoogle Scholar
  33. 33.
    Sizov, G., Öztürk, P., Štyrák, J.: Acquisition and reuse of reasoning knowledge from textual cases for automated analysis. In: Lamontagne, L., Plaza, E. (eds.) ICCBR 2014. LNCS (LNAI), vol. 8765, pp. 465–479. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11209-1_33 Google Scholar
  34. 34.
    Tirunagari, S., Hanninen, M., Stanhlberg, K., Kujala, P.: Mining causal relations and concepts in maritime accidents investigation reports. Int. J. Innovative Res. Dev. 1(10), 548–566 (2012)Google Scholar
  35. 35.
    Toolan, F., Carthy, J., Drummond, A., Dunnion, J.: Automating incident analysis: a challenge paper. In: 2nd Workshop on the Investigation and Reporting of Incidents and Accidents, Williamsburg, Virginia, United States, pp. 99–109 (2003)Google Scholar
  36. 36.
    Tsatsoulis, C., Amthauer, H.A.: Finding clusters of similar events within clinical incident reports: a novel methodology combining case based reasoning and information retrieval. Qual. Saf. Health Care 12(Suppl 2), ii24–ii32 (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway

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