This invention relates to an artificial memory system and a method of continuous learning for predicting and anticipating human operator's action as response to ego-intention as well as environmental influences during machine operation. More specifically the invention relates to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the network implementation comprising an artificial hierarchical memory system.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-26. (canceled) 27. An architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the first neural network implementation comprising an artificial hierarchical memory system, the architecture further 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 patterns and sequences received from the receiving port, said one or more first nodes forming a second neural network comprising neurons as components and edges connecting two or more of said neurons in a graph; and an output port configured to output data constructed by the architecture and associated with the behaviors, whereby each of the one or more or essentially all of said first nodes is adapted for time series analysis, and comprises components connected in a topological graph or temporal graph. 28. The architecture of claim 27, wherein said first neural network is based on a Memory Prediction Framework implementation or a Deep Learning Architecture implementation. 29. The architecture of claim 27, wherein the one or more first nodes are configured to learn and recognize frequently occurring input patterns as such. 30. The architecture of claim 27, whereby among at least two or more of said first nodes, signals representing a probability density function are used for communication. 31. The architecture of claim 27, whereby one or more or essentially all of said first nodes are adapted to execute methods based on neural gas concepts. 32. The architecture of claim 27, wherein for one or more of the first nodes a neural gas model extended to include time, the one or more first nodes providing a representation of a generalized Markov process through introduction of a temporal graph used side-by-side with an original topological graph leveraged by the neural gas algorithm. 33. The architecture of claim 27, whereby among at least two or more of said neurons within said one or more first nodes edges, represent temporal sequences of activation of said neurons. 34. The architecture of claim 27, wherein each neuron represents information about an input pattern, a temporal context and activation timing statistics. 35. The architecture of claim 31 wherein said neural gas concept is provided with an automatically during-training-derived temporal graph, where nodes of said temporal graph represent neurons, each neuron representing a temporal state for the neural gas, and edges between said nodes of said temporal graph represent the temporal sequence in which different neurons were activated during training 36. The architecture of claim 31, wherein said neural gas concept is provided with neurons, each neuron representing a temporal state for the neural gas, wherein each neuron retains information about at least an input pattern, a temporal context and statistics about the time duration permanence of the previous state to which it responds. 37. The architecture of claim 31, wherein said neural gas concept with information on permanence on the states is configured to provide for autonomous learning of a homogeneous Generalized Semi-Markov Decision Model for at least its input space. 38. The architecture of claim 37, wherein said one or more first nodes are provided with inference methods exploiting the homogeneous Generalized Semi-Markov Decision Model. 39. The architecture of claim 37, wherein the one or more first nodes are provided with temporal prediction methods exploiting the learned Generalized Semi-Markov Decision Model. 40. The architecture of the claim 37, wherein said temporal prediction methods are based on a path search in said temporal graph representing a Generalized Semi-Markov Decision Model. 41. The architecture of claim 39, wherein said prediction methods make use of a Viterbi method. 42. The architecture of claim 38, wherein said inference and prediction processes are characterized in that for each new input pattern, an activation state of a plurality of neurons is stored. 43. The architecture of claim 27, wherein the feedback input to each first node influences values of activation of enclosed neurons. 44. The architecture of claim 27, wherein each neuron retains statistical information about labels of the input data activated in said neuron. 45. The architecture of claim 44, wherein the statistical information about the labels is used to perform semi-supervised grouping or to improve the neural gas performance during training or inference. 46. A computer based method for interacting with dynamic behaviors of a tool and an operator, the method comprising: receiving data generated by one or more sensors; sending said data to the architecture according to claim 27, processing said data, by said architecture, using methods based on neural gas concepts and outputting the resulting data. 47. A computer based method for training/teaching patterns to one or more of the nodes of an architecture according to any of claim 27, the method comprising: providing input patterns to one or more of said nodes; and automatically deriving a temporal graph or homogeneous Generalized Semi-Markov Decision Model for at least one of said nodes. 48. The method of claim 47, wherein statistical information about labels is used to perform semi-supervised grouping or to improve the neural gas performance during training or inference. 49. A computer based method for determining an output related to an input, the method comprising: providing said input to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the first neural network implementation comprising an artificial hierarchical memory system, the architecture further 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 patterns and sequences received from the receiving port, said one or more first nodes forming a second neural network comprising neurons as components and edges connecting two or more of said neurons in a graph; and an output port configured to output data constructed by the architecture and associated with the behaviors, whereby each of the one or more or essentially all of said first nodes is adapted for time series analysis, and comprises components connected in a topological graph or temporal graph; executing an inference method on said architecture triggering inference methods on said nodes by use of the homogeneous Generalized Semi-Decision Markov Model as trained/learned by the method in claim 47 to determine said output. 50. A computer based method for predicting outputs related to an input, the method comprising: providing said input to an architecture with artificial memory for interacting with dynamic behaviors of a tool and an operator, wherein the architecture is a first neural network having structures and mechanisms for abstraction, generalization and learning, the first neural network implementation comprising an artificial hierarchical memory system, the architecture further 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 patterns and sequences received from the receiving port, said one or more first nodes forming a second neural network comprising neurons as components and edges connecting two or more of said neurons in a graph; and an output port configured to output data constructed by the architecture and associated with the behaviors, whereby each of the one or more or essentially all of said first nodes is adapted for time series analysis, and comprises components connected in a topological graph or temporal graph; and executing a temporal prediction method on said architecture triggering temporal prediction methods on said nodes by use of the homogeneous Generalized Semi-Decision Markov Model as trained/learned by the method in claim 47.