Abstract
A dichotomous Case-Based Reasoning (CBR) model is one in which two kinds of reasoning mechanisms are employed; these may be for realizing fast and slow problem-solving as demanded by the nature of the incoming query. Such dichotomous operation is inspired by Daniel Kahneman’s seminal work on the two modes of thinking observed in humans. In this paper, we present the following three directions of refinement for a dichotomous CBR model: selection of attributes for a fast thinking model based on parsimonious CBR, switching from fast to slow thinking based on constraints derived from domain knowledge and arriving at a complexity measure for evaluating dichotomous models. For all the three improvements identified, we discuss the results on real-world data sets and empirically analyse the effectiveness of the same.
Keywords
- Fast and slow thinking
- Dichotomous CBR models
- Cognitive CBR
This is a preview of subscription content, access via your institution.
Buying options
References
Aamodt, A., Plaza, E.: Case-based reasoning and the upswing of AI (2017)
Ayres-de Campos, D., Bernardes, J., Garrido, A., Marques-de Sa, J., Pereira-Leite, L.: Sisporto 2.0: a program for automated analysis of cardiotocograms. J. Matern. Fetal Neonatal. Med. 9(5), 311–318 (2000)
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decis. Support Syst. 47(4), 547–553 (2009)
Craw, S., Aamodt, A.: Case based reasoning as a model for cognitive artificial intelligence. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 62–77. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_5
Dileep, K.V.S., Chakraborti, S.: Eager to be lazy: towards a complexity-guided textual case-based reasoning system. In: Goel, A., Díaz-Agudo, M.B., Roth-Berghofer, T. (eds.) ICCBR 2016. LNCS (LNAI), vol. 9969, pp. 77–92. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47096-2_6
Dua, D., Graff, C.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml
Fox, S., Leake, D.: Using introspective reasoning to re ne indexing. In: IJCAI, vol. 391397 (1995)
Ganesan, D., Chakraborti, S.: An empirical study of knowledge tradeoffs in case-based reasoning. In: IJCAI, pp. 1817–1823 (2018)
Ganesan, D., Chakraborti, S.: A reachability-based complexity measure for case-based reasoners. In: The Thirty-Second International Flairs Conference (2019)
Kahneman, D.: Thinking, Fast and Slow. Macmillan (2011)
Kannengiesser, U., Gero, J.S.: Design thinking, fast and slow: a framework for Kahneman’s dual-system theory in design. Design Sci. 5, 1–21 (2019)
Kaurav, S., Ganesan, D., Padmanabhan, D., Chakraborti, S.: Thinking fast and slow: a CBR perspective. In: 34th Florida Artificial Intelligence Research Society Conference (2021)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, Burlington (2014)
Raghunandan, M.A., Chakraborti, S., Khemani, D.: Robust measures of complexity in TCBR. In: McGinty, L., Wilson, D.C. (eds.) ICCBR 2009. LNCS (LNAI), vol. 5650, pp. 270–284. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02998-1_20
Smith, J.W., Everhart, J., Dickson, W., Knowler, W., Johannes, R.: Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the ASCA in Medical Care, p. 261 (1988)
Smyt, B., McKenna, E.: Footprint-based retrieval. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS, vol. 1650, pp. 343–357. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48508-2_25
Zilberstein, S.: Using anytime algorithms in intelligent systems. AI Mag. 17(3), 73 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Kaurav, S., Ganesan, D., P, D., Chakraborti, S. (2021). Revisiting Fast and Slow Thinking in Case-Based Reasoning. In: Sánchez-Ruiz, A.A., Floyd, M.W. (eds) Case-Based Reasoning Research and Development. ICCBR 2021. Lecture Notes in Computer Science(), vol 12877. Springer, Cham. https://doi.org/10.1007/978-3-030-86957-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-86957-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86956-4
Online ISBN: 978-3-030-86957-1
eBook Packages: Computer ScienceComputer Science (R0)