Abstract
Case-based reasoning (CBR) is broadly speaking a method of giving a verdict/decision on a new case query by comparing it with verdicts/decisions of known similar cases. Similarity of cases is determined either by best distance of the query case from the known cases and recently also using argumentation. The approach of this paper is not to rely on similarity or argumentation, but to use the entire set of known cases and their known verdicts to define the relative strength and importance of all the features involved in these cases. We then decide the verdict for the new case based on the strength of the features appearing in it.
Liuwen Yu has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie ITN EJD grant agreement. No 814177.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In Talmudic law, conspiring witnesses are witnesses whose testimony was found to be false testimony, the testimony of false witnesses given in court is refuted - demonstrating how they were not at the scene of the purported crime - they are then sentenced to the identical punishment that was to have been meted out onto the intended victim (Devarim 19:15-20).
- 2.
Intuitively, \(+\) and \(+\) cannot make a − and similarly − and − cases joined cannot make a \(+\).
- 3.
Note if we have a case \(E= \{c,a\}\), we may have a paradox, is the verdict guilty or not? This depends on the legal systems, some may say an alibi can be wrong but a signed confession is stronger, while other legal systems may say we do not accept confession, they can be obtained by torture.
- 4.
For example, if the set of elements stronger or weaker than x are more in number than the set of stronger or smaller than y, we say that \(x>_{\mathbb {K}}y\).
- 5.
This case will depend on the application area. For example, in legal murder cases, where verdict − means not guilty, we can say that if Case 1 does not hold, we enforce −, because it gives us “shadow of a doubt”.
- 6.
According to Talmudic law, \(W_1\) and \(W_2\) should be punished.
References
Amgoud, L., Cayrol, C.: Inferring from inconsistency in preference-based argumentation frameworks. J. Autom. Reason. 29(2), 125–169 (2002). https://doi.org/10.1023/A:1021603608656
Bonissone, P.P., Cheetham, W.: Fuzzy case-based reasoning for residential property valuation. In: Handbook of Fuzzy Computation, pp. G14–1. CRC Press (2020)
Calegari, R., Sartor, G.: Burdens of persuasion and standards of proof in structured argumentation. In: Baroni, P., Benzmüller, C., Wáng, Y.N. (eds.) CLAR 2021. LNCS (LNAI), vol. 13040, pp. 40–59. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89391-0_3
Cyras, K., Satoh, K., Toni, F.: Abstract argumentation for case-based reasoning. In: Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning (2016)
Čyras, K., Satoh, K., Toni, F.: Explanation for case-based reasoning via abstract argumentation. In: Computational Models of Argument, pp. 243–254. IOS Press (2016)
Finnie, G., Sun, Z.: Similarity and metrics in case-based reasoning. Int. J. Intell. Syst. 17(3), 273–287 (2002)
Finnie, G.R., Wittig, G.E., Desharnais, J.M.: A comparison of software effort estimation techniques: using function points with neural networks, case-based reasoning and regression models. J. Syst. Softw. 39(3), 281–289 (1997)
Kampik, T., Gabbay, D., Sartor, G.: The burden of persuasion in abstract argumentation. In: Baroni, P., Benzmüller, C., Wáng, Y.N. (eds.) CLAR 2021. LNCS (LNAI), vol. 13040, pp. 224–243. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-89391-0_13
Kim, G.H., An, S.H., Kang, K.I.: Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Build. Environ. 39(10), 1235–1242 (2004)
Leake, D., Ye, X., Crandall, D.J.: Supporting case-based reasoning with neural networks: an illustration for case adaptation. In: AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering, vol. 2 (2021)
Perner, P.: Case-based reasoning – methods, techniques, and applications. In: Nyström, I., Hernández Heredia, Y., Milián Núñez, V. (eds.) CIARP 2019. LNCS, vol. 11896, pp. 16–30. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33904-3_2
Prakken, H., Sartor, G.: Modelling reasoning with precedents in a formal dialogue game. In: Sartor, G., Branting, K. (eds.) Judicial Applications of Artificial Intelligence, pp. 127–183. Springer, Dordrecht (1998). https://doi.org/10.1007/978-94-015-9010-5_5
Prakken, H., Wyner, A., Bench-Capon, T., Atkinson, K.: A formalization of argumentation schemes for legal case-based reasoning in ASPIC+. J. Log. Comput. 25(5), 1141–1166 (2015)
Wyner, A.Z., Bench-Capon, T.J.M., Atkinson, K.: Three senses of “argument’’. In: Casanovas, P., Sartor, G., Casellas, N., Rubino, R. (eds.) Computable Models of the Law. LNCS (LNAI), vol. 4884, pp. 146–161. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85569-9_10
Zheng, H., Grossi, D., Verheij, B.: Case-based reasoning with precedent models: preliminary report. In: Computational Models of Argument, pp. 443–450. IOS Press (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yu, L., Gabbay, D. (2022). Case-Based Reasoning via Comparing the Strength Order of Features. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2022. Lecture Notes in Computer Science(), vol 13283. Springer, Cham. https://doi.org/10.1007/978-3-031-15565-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-15565-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15564-2
Online ISBN: 978-3-031-15565-9
eBook Packages: Computer ScienceComputer Science (R0)