:- module net.
:- public main / 0,
          restart / 0.
:- extrn main_hlpr / 0.
1 1 : 0
1 0 : 1
0 1 : 1
0 0 : 0
formula : A or B
Formulas attachted to nodes :
      Input to node = input from node formula
      Input from formula =
          if formula is a node output then that output
          else if formula = not formula1 then
Activation of an NOT node
               Result := ( 1 - INPUT )
Activation of an OR node
               begin
               Result := 0;
               for i ranging over all inputs to this node do
                     Result := Result +
                               ( 1 - Result ) * OUTPUT(i) * EDGE_FROM_i
               end.
Activation of an AND node
               begin
               Result := 1;
               for i ranging over all inputs to this node do
                     Result := Result *  OUTPUT(i) * EDGE_FROM_i
               end.
ARCHITECTURE OF NETWORK
1.  Given input nodes and their NOTs
2.  1st hidden layer : some number of AND nodes
3:  output layer : a problem-specific number of OR nodes
LEARNING ALGORITHM
Starting at the output and working toward the input, do
      repeat,
         Compute desired change of a neuron.
         Compute desired changes of edges leading to it.
         Move 1 layer toward input
Desired change of a neuron :
     Output Neuron :       ( DESIRED - ACTUAL )
     Hidden neuron:
        Let wij be an edge to neuron j in the next layer
        SUM  wij * CHANGE(J)
        edges to next layer
Desired change of an edge :
        Let  wij be an edge from neuron i to neuron j in the
                next layer.
        CHANGE( wij ) = ACTIVATION( i ) * change( j )
NEW EDGE WEIGHT GIVEN CHANGE
        if change > 0 then
            new := current + ( 1 - current ) * change
        if change < 0 then
            new := current + current * change
INITIALIZATION
Edges based on intuition
When you don't know, make it a 1.
Hidden layer generation
Given : list of input neurons
      I1,    I2,    I3,    I4,
    Hidden layer 1 :  attached formulas on edges
E1_1        I1  &  I2  &  I3  &  I4
E1_2        I1  &  I2  &  I3  & -I4
E1_3        I1  &  I2  & -I3  &  I4
E1_4        I1  &  I2  & -I3  & -I4
E1_5        I1  & -I2  &  I3  &  I4
E1_6        I1  & -I2  &  I3  & -I4
E1_7        I1  & -I2  & -I3  &  I4
E1_8        I1  & -I2  & -I3  & -I4
E1_9       -I1  &  I2  &  I3  &  I4
E1_10      -I1  &  I2  &  I3  & -I4
E1_11      -I1  &  I2  & -I3  &  I4
E1_12      -I1  &  I2  & -I3  & -I4
E1_13      -I1  & -I2  &  I3  &  I4
E1_14      -I1  & -I2  &  I3  & -I4
E1_15      -I1  & -I2  & -I3  &  I4
E1_16      -I1  & -I2  & -I3  & -I4
    Hidden layer 2 :
neuron(   LAYER , NUMBER, FORMULA ).
       LAYER = input, not( NUMBER), and, or
       OUTPUT_LAYER,
       INPUT_NEURON,
       OUTPUT_NEURON,
       TIME,
       ACTIVATION).
input(  KEY, NEURON_NUMBER, ACTIVATION).
output(  KEY, NEURON_NUMBER, ACTIVATION).
state( neuron,
       LAYER,
       NUMBER,
       TIME,
       ACTIVATION).
state( edge  ,
       OUTPUT_LAYER,
       INPUT_NEURON,
       OUTPUT_NEURON,
       TIME,
       ACTIVATION).
input(  KEY, NEURON_NUMBER, ACTIVATION).
output(  KEY, NEURON_NUMBER, ACTIVATION).
%%%%%%%%%%%%%%% main program %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
main :-
   main_hlpr.
restart :- halt.
%%%%%%%%%%%%%%%%%% eof %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5