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Michael Gr. Voskoglou. (2020). Applications of Finite Markov Chains to Artificial Intelligence. International Journal of Innovation (ijinn) ISSN:0975 – 9808, 10(1), 1–10. http://doi.org/10.5281/zenodo.3662434
The theory of MCs is a smart combination of Linear Algebra and Probability theory offering ideal conditions for the study and mathematical modelling of situations depending on random variables and finding important applications to problems of Artificial Intelligence. In the paper at hands an absorbing Markov chain is introduced on the phases of decision making and an application is presented illustrating the importance of the constructed model in practice. Further, the Case-Based Reasoning process is modeled with the help of an ergodic Markov chain defined on its steps and through it a measure is obtained for the effectiveness of a Case-Based Reasoning system.
Keywords: Markov Chains (MC’s), Absorbing MC’s (AMC’s), Ergodic MC’s (EMC’s), Artificial Intelligence (AI), Decision Making (DM), Case-Based Reasoning (CBR).