Abstract: A method controlling movement of an agent moving within an environment represented by a directed graph including vertices located at a border of an unknown portion of the environment, includes: guiding the moving agent via execution of at least one iteration of following successive operations: planning a path towards a destination vertex or border vertex, via a progressively increasing evaluation of a first heuristic of cost; controlling navigation of the agent towards the destination or border vertex returned by the planning; controlling an exploration in the unknown portion if the planning returns a border vertex as the destination. The planning includes, for each calculation of the first cost heuristic for a border vertex, calculation of a second heuristic of risk. The method provides, as the destination, the border vertex with lowest calculated heuristic risk evaluation as soon as the cost exceeds this heuristic risk evaluation before reaching the destination vertex.

Abstract: A method controlling movement of an agent moving within an environment represented by a directed graph including vertices located at a border of an unknown portion of the environment, includes: guiding the moving agent via execution of at least one iteration of following successive operations: planning a path towards a destination vertex or border vertex, via a progressively increasing evaluation of a first heuristic of cost; controlling navigation of the agent towards the destination or border vertex returned by the planning; controlling an exploration in the unknown portion if the planning returns a border vertex as the destination. The planning includes, for each calculation of the first cost heuristic for a border vertex, calculation of a second heuristic of risk. The method provides, as the destination, the border vertex with lowest calculated heuristic risk evaluation as soon as the cost exceeds this heuristic risk evaluation before reaching the destination vertex.

Abstract: A method and a system for evaluating the class of a test datum in a data space of dimension D where D?3, each datum belonging to at least one class grouping together several data, comprising: projecting a suite of reference data of the data space into a space of dimension Q where Q<D, the class of each reference datum being known, calculating a measurement of similarity of the test datum to each of the reference data, partitioning the projection space into a plurality of disjoint regions each containing the projection of one and only one reference datum, and finally evaluating the class of the test datum, this class being evaluated as being the same class as one of the reference data contained in one of the regions containing the reference data closest to the test datum in the sense of the similarity measurement.

Abstract: A method and a system for evaluating the class of a test datum in an original metric space, each datum belonging to at least one class grouping a plurality of data, includes a step of graphical representation of the spatial organization of a set of learning data of the original space in a representation metric space, a conjoint membership level indicating if any two data from the learning set belong to the same class. The method also includes a step of relating the test datum to the projections of the data from the learning set, the most probable class of the test datum being the class of the projections of the data from the learning set related to the test datum. Application: assistance with decision-making in discrimination, shape recognition.

Abstract: A method and a system for evaluating the class of a test datum in a data space of dimension D where D?3, each datum belonging to at least one class grouping together several data, comprising: projecting a suite of reference data of the data space into a space of dimension Q where Q<D, the class of each reference datum being known, calculating a measurement of similarity of the test datum to each of the reference data, partitioning the projection space into a plurality of disjoint regions each containing the projection of one and only one reference datum, and finally evaluating the class of the test datum, this class being evaluated as being the same class as one of the reference data contained in one of the regions containing the reference data closest to the test datum in the sense of the similarity measurement.