Contribution of Probabilistic Grammar Inference with k-Testable Language for Knowledge Modeling - Application on Aging People

We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). The proposed approach is based on k-Testable Languages in the Strict Sense Inference algorithm in order to infer a probabilistic automaton from which a Markovian model which has a discrete (finite or countable) state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.