Recursive coupling of artificial learning units

Abstract

Provided is a method in a system of at least two artificial intelligence units, comprising inputting input values to at least a first artificial intelligence unit and a second artificial intelligence unit; obtaining first output values of the first artificial intelligence unit; forming one or more modulation functions based on the output values of the first artificial intelligence unit; applying the formed one or more modulation functions to one or more parameters of the second artificial intelligence unit, the one or more parameters influencing the processing of input values and the obtaining of output values in the second artificial intelligence unit; and finally obtaining second output values of the second artificial intelligence unit.

1 . A method in a system of at least two artificial intelligence units comprising: inputting input values (X i ) to at least a first artificial intelligence unit and a second artificial intelligence unit;
 obtaining first output values (Output 1 ) of the first artificial intelligence unit;   forming one or more modulation functions (f mod_f , f mod_w ) based on the output values (Output 1 ) of the first artificial intelligence unit;   applying the formed one or more modulation functions to one or more parameters of the second artificial intelligence unit, wherein the one or more parameters influence the processing of input values and the obtaining of output values in the second artificial intelligence unit;   obtaining second output values (Output 2 ) of the second artificial intelligence unit.   
     
     
         2 . The method of  claim 1 , wherein at least one of the artificial intelligence units comprises a neural network having a plurality of nodes, and wherein the one or more parameters is at least one of: a weighting (w i ) for a node of the neural network, an activation function (f akt ) of a node, an output function (f out ) of a node, a propagation function of a node. 
     
     
         3 . A method according to  claim 1 , wherein a classification memory is associated with each of the artificial intelligence units, wherein each of the artificial intelligence units performs a classification of the input values into one or more classes (K 1 , K 2 , . . . , K n , K m ) which are stored in the classification memory, the classes each being structured in one or more dependent levels, and a number of the classes (n) and/or the levels in a first classification memory of the first artificial intelligence unit being less than a number of the classes (m) and/or the levels in a second classification memory of the second artificial intelligence unit. 
     
     
         4 . The method according to  claim 1 , wherein applying the at least one modulation function causes a time-dependent superposition of parameters of the second artificial intelligence unit, and wherein the at least one modulation function (f mod_f , f mod_w ) comprises one of the following: a periodic function, a step function, a function with briefly increased amplitudes, a damped oscillation function, a beat function as a superposition of several periodic functions, a continuously increasing function, and a continuously decreasing function. 
     
     
         5 . The method of  claim 1 , wherein the second artificial intelligence unit comprises a second neural network having a plurality of nodes, and wherein applying the at least one modulation function causes deactivation of at least a portion of the nodes. 
     
     
         6 . The method of  claim 1 , further comprising:
 determining a currently dominant artificial intelligence entity in the system; and   forming total output values of the system from the output values of the currently dominating unit.   
     
     
         7 . The method of  claim 6 , wherein the first artificial intelligence unit is set as the dominant unit at least until one or more output values (Output 2 ) of the second artificial intelligence unit are available. 
     
     
         8 . The method according to  claim 6 , further comprising a comparison of current input values with previous input values by at least one of the artificial intelligence units of the system, wherein, if the comparison results in a deviation that is above a predetermined input threshold, the first artificial intelligence unit is determined to be the dominant unit. 
     
     
         9 . The method of  claim 6 , further comprising comparing current output values of the first artificial intelligence unit with previous output values of the first artificial unit, wherein, if the comparison results in a deviation that is above a predetermined output threshold, the first artificial intelligence unit is determined to be the dominant unit. 
     
     
         10 . The method of  claim 6 , wherein the system further comprises a timer storing one or more predetermined time periods associated with one or more of the artificial intelligence units, and wherein the timer is arranged to measure, for a respective one of the artificial intelligence units, the elapse of the predetermined time period associated with that unit. 
     
     
         11 . The method of  claim 10 , wherein measuring the assigned predetermined period of time for one of the artificial intelligence units is started when that artificial intelligence unit is determined to be the dominant unit. 
     
     
         12 . The method of  claim 10 , wherein the second artificial intelligence unit is determined to be the dominant unit if a first time period predetermined for the first artificial intelligence unit has elapsed in the timer. 
     
     
         13 . The method according to  claim 1 , wherein the input values (X i ) comprise at least one of the following: measured values detected by one or more sensors, data detected by a user interface, data retrieved from a memory, data received via a communication interface, and data output by a computing unit.