Contribution of Probabilistic Grammar Inference with K-Testable Language for Knowledge Modeling: Application on aging people

International audienceWe 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

Inference Of Timed Transition Systems

We extend Angluin's algorithm for on-line learning of regular languages to the setting of timed transition systems. More specifically, we describe a procedure for inferring systems that can be described by event-recording automata by asking a sequence of membership queries (does the system accept a given timed word?) and equivalence queries (is a hypothesized description equivalent to the correct one?). In the inferred description, states are identified by sequences of symbols together with timing information. The number of membership queries is polynomially in the region graph and in the biggest constant of the automaton to learn

Abstract INFINITY 2004 Preliminary Version Inference of Timed Transition Systems

We extend Angluin’s algorithm for on-line learning of regular languages to the setting of timed transition systems. More specifically, we describe a procedure for inferring systems that can be described by event-recording automata by asking a sequence of membership queries (does the system accept a given timed word?) and equivalence queries (is a hypothesized description equivalent to the correct one?). In the inferred description, states are identified by sequences of symbols together with timing information. The number of membership queries is polynomially in the region graph and in the biggest constant of the automaton to learn. Key words: model inference, model learning, timed systems