IJInn 2020 Volume 10 Issue 1

International Journal of Innovation (IJInn)

ISSN:0975 – 9808

Indexed by PROQUEST

The IJBST Journal Group Serves Free since Establishment in year 2008

IJBST Journal Group -- Open Access -- NO Fees -- NO Processing Charges -- 100% Non Profit Initiatives

Free University / Institutional Subscription of the IJBST Journal Group https://subscription.approvals.ijbst.org

The IJBST Journal Group subscribes to the San Francisco Declaration on Research Assessment and The European Code of Conduct for Research Integrity

The IJBST Journal Group Archive can also be accessed at https://archive.org/details/IJBSTJournalGroup

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).