Systems and methods for machine learning using a network of decision-making nodes

1. An apparatus for machine learning, comprising:

at least one processor; and

at least one memory in electronic communication with the processor, the at least one memory storing instructions that are executable by the at least one processor to:

obtain a set of inputs;

search the at least one memory for a node uniquely associated with the set of inputs;

if a node uniquely associated with the set of inputs is not found in the at least one memory:

create the node in the at least one memory;

associate the node with the set of inputs in the at least one memory;

randomly generate a decision set using a random number generator;

receive a score evaluating the decision set at solving a problem; and

store, in the at least one memory in association with the node, the decision set with the score for the decision set; and

if the node uniquely associated with the set of inputs is found in the at least one memory:

retrieve, from the at least one memory, a decision set associated with the node with a best previous score;

responsive to a value of a randomness factor, either output, via an output device, the decision set with the best previous score as a best solution to the problem or generate a new decision set, wherein the new decision set is either the same as the retrieved decision set or is randomized, wherein a chance of the new decision set being randomized is determined by the randomness factor; and

if a new decision set is generated:

receive a score evaluating the new decision set at solving the problem; and

store, in the at least one memory in association with the node, the new decision set and the score for the new decision set;

wherein each node is a decision-making neuron in network of decision-making neurons.

2. The apparatus of claim 1 , wherein the value of the randomness factor at least one of increases and decreases from a first cycle to a second cycle based on a predetermined number of cycles and a threshold.

3. The apparatus of claim 1 , wherein the instructions are further executable by the at least one processor to:

determine a best decision set for the node based on a plurality of decision sets produced by one or more other nodes.

4. The apparatus of claim 3 , wherein the input set for a first node comprises a first input and a second input, and wherein the instructions to determine the best decision set for the first node comprise instructions executable by the at least one processor to:

determine that the first input has a linear relationship with a third input, wherein the third input is associated with a second node;

obtain a plurality of decision sets and associated scores from the second node;

compare the plurality of decision sets with a plurality of stored decision sets and associated scores; and

identify a best decision set based on the associated scores.

5. The apparatus of claim 3 , wherein the instructions to determine the best decision set for the node comprise instructions executable by the at least one processor to:

identify a critical data point based on the plurality of decision sets and associated scores; and

determine the best decision set based on the identified critical data point.

6. The apparatus of claim 3 , wherein the instructions to determine the best decision set for the node comprise instructions executable by the at least one processor to:

obtain a plurality of global outputs, wherein a global output is based on at least one output set from at least one node;

identify a trend between a plurality of stored decision sets for the node and the plurality of global outputs, wherein the trend relates stored decision sets with desirable global outputs according to a set of criteria; and

determine the best decision set for the node based on the identified trend.

7. The apparatus of claim 6 , wherein the instructions to determine the best decision set based on the identified trend comprise instructions executable by the at least one processor to:

identify a stored decision set where the associated score leads the trend, wherein the identified decision set is designated as the best decision set.

8. The apparatus of claim 6 , wherein the instructions are further executable by the at least one processor to:

associate each global output of the plurality of global outputs with its corresponding stored decision set.

9. The apparatus of claim 1 , wherein the randomized new decision set is based at least in part on a previous best decision set.