AUTHOR(S):

TITLE 
PDF FULLTEXT HTML 
ABSTRACT CaseBased Reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems. Here a Markov Chain model is constructed for a mathematical description of the CBR process by introducing an absorbing MC on its main steps. A method is also developed with the help of this model for evaluating the effectiveness of CBR systems, accompanied by suitable examples and hints are given for future research on the subject. 
KEYWORDS CaseBased Reasoning (CBR), Markov Chains (MCs), Absorbing MCs, CBR Systems, Artificial Intelligence (AI) 
REFERENCES [1] Schank, R. (1982), Dynamic memory; A theory of reminding and learning in computers and people, Cambridge Univ. Press. [2] Kolodner, J. (1983), Reconstructive Memory: A Computer Model, Cognitive Science, 7, pp. 281328. [3] Lebowitz, M. (1983), MemoryBased Parsing, Artificial Intelligence, 21, pp. 363404. [4] Voskoglou, M. Gr. (2008), CaseBased Reasoning: A recent theory for problemsolving and learning for computers and people, Communications in Computer and Information Science (WSKS 08), 19, pp. 314319. [5] Voskoglou, M. Gr. & Salem, AB. M, (2014), AnalogyBased and Case Based Reasoning: Two Sides of the Same Coin, International Journal of Applications of Fuzzy Sets and Artificial Intelligence, 4, pp. 551. [6] Kemeny, J. & Snell, J. l. (1976), Finite Markov Chains, SpringerVerlag, New York. [7] Voskoglou, M. Gr. (2007), A stochastic model for the modelling process, In C. Chaines et al. (Eds), Mathematical Modelling: Education, Engineering and Economics (ICTMA 12), pp. 149157, Horwood Publ. , Chichester. [8] Aamodt, A. & Plaza, E. (1994), Case Based Reasoning:: Foundational Issues, Methodological Variations, and System Approaches, A. I. Communications, 7, no. 1, pp. 39 52. [9] Porter, B. & Bareiss, B. (1986), PROTOS: An experiment in knowledge acquisition for heuristic classification tasks, In Proceedings of the 1st International Meeting on Advances in Learning (IMAL), pp. 159174, Les Arcs, France. [10] Rissland, E. (1983), Examples in legal reasoning: Legal hypotheticals, In Proceedings of the Eight International Joint Conference on Artificial Intelligence (IJCAI), Karlsruhe [11] Stanfill, C. & Waltz, D. (1988), The memory based reasoning paradigm, In Casebased reasoning, Proceedings from a workshop, pp. 414424, Morgan Kaufmann Publ., Clearwater Beach, Florida. 
Cite this paper Michael Gr. Voskoglou. (2017) An Absorbing Markov Chain Model for Case – Based Reasoning. International Journal of Computers, 2, 99105 
