A computer-based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower-dimensional map, if environmental data are captured during a learning phase of a computer-based model, evaluate the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, add new point to the compressed data and generate a signal indicating which human-like decision to use to correspond to the state of the operator.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower dimensional map, the method comprising a reception of data with a first dimension greater than one captured and transmitted by at least one sensor and a compression of the data or attributes derived from the data into compressed data with a second dimension lower than the first dimension, wherein, for two metric spaces X=(x,μx) and Y=(y,μy) with x a first space which dimension is greater or equal than the dimension of a second space y, μx a first metric on the first space x, μy a second metric on the second space y, the compression is realized by mapping pairs of points formed between a first set of n points P belonging to the first space x and a second set of n points Q belonging to the second space y, each point of the second set of n points Q being paired with a point of the first set of points P through the following equation: Q=argmintϵynd(A1(μx(P)),A2(μy(t))) (Equation 1) where μx(P) is a first symmetric distance matrix between points in P, μx(Q) is a second symmetric distance matrix between points in Q, and d(.,.) is a symmetric distance function on the space of square matrices, while A1 and A2 are any two functions holding the following properties: A:R+→[0, 1], A(0)=1, limx→∞A(z)=0, A(z1)>A(z2) if and only if z1<z2. 2. The computer based method of claim 1, further comprising an extraction of attributes from at least one data before initiating the bi-directional compression. 3. The computer based method according to claim 1, comprising a compression of a new point m belonging to the first space x on the second space y by solving the following equation: {tilde over (y)}=argmintϵynd(A1(μx(P,m)),A2(μy(Q,t))) (Equation 2) and a decompression of a new point t belonging to the second space y on the first space x by solving the following equation: {tilde over (x)}=argminmϵxnd(A1(μx(P,m)),A2(μy(Q,t))) (Equation 3) where μx(P,m) and μy(Q,t) are the distance vectors between the points of BiMap and the new points in the corresponding respective spaces, BiMap being a correspondence between points of the first space x and points of the second space y represented by equation 2 and equation 3, which Q points are calculated from points P by solving equation 1. 4. A computer based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into lower dimensional map according to claim 1 with the environmental data, if environmental data are captured during a learning phase of a computer-based model, evaluating the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, adding new points to the compressed data and generating a signal indicating which human-like decision to use to correspond to the state of the operator. 5. The computer based method according to claim 4, comprising pre-processing the environmental data with at least one filter before realising the computer based method for realising a bi-directional compression of data, the computer based method for realising a bi-directional compression of data receiving as inputs filtered environmental data. 6. The computer based method according to claim 4, wherein the environmental data comprise at least one sample of raw sound recorded inside a car's cockpit with at least one microphone, and the method further comprises inputting the environmental data into auditory models before pre-processing with at least one filter the data outputted by the auditory models deliver a plurality of different versions of the environmental data, the pre-processing being realized over every version of the environmental data. 7. A system for simulating a human-like behaviour in an environmental context comprising at least one sensor to capture environmental data, and an architecture with a memory for interacting with dynamic behaviours of a tool and an operator, characterized in that architecture is configured to process the steps of the computer-based method for simulating a human-like decision in an environmental context according to claim 4. 8. The system according to claim 7, wherein the memory of the architecture is an artificial memory, the architecture being a first neural network having structures and mechanisms for abstraction, generalisation and learning, the first neural network implementation defining the architecture comprising an artificial hierarchical memory system, comprising a receiving port configured to receive data generated by sensors (on the tool), one or more first nodes configured to learn and recognize frequently occurring input attributes and sequences received from the receiving port, the one or more first nodes forming a second neural network comprising neurons as components and edges connecting two or more of the neurons in a graph, and an output port configured to output data constructed by the architecture and associated with the behaviours, whereby each of the one or more or essentially all of the first nodes is adapted for time series analysis, and comprises components connected in a topological graph or temporal graph. 9. The system according to claim 7, further comprising a pre-processing filter to apply a set of different filters to the environmental data captured by the at least one sensor. 10. The system according to claim 7, wherein the environmental data comprise at least one sample of raw sound recorded inside a car's cockpit with a noise sensor, and the system further comprises auditory models configured to be applied to the recorded environmental data before the filtering pre-processing to output a plurality of different versions of the environmental data, the filtering pre-processing being realized over every version of the environmental data. 11. A computer program product having software to realize the computer based method according to claim 4, which when executed on a processing engine, can contain code segments that provide a Perceptual/Cognitive architecture with artificial memory for interacting with dynamic behaviours of a tool and an operator, wherein the Perceptual/Cognitive architecture is based on a Memory Prediction Framework implementation having structures and mechanisms for abstraction and generalization and optionally learning, the memory Prediction Framework implementation comprising an enhanced artificial hierarchical memory system. 12. An automotive vehicle comprising an electronic control unit including a system for simulating a human-like decision in an environmental context according to claim 7.