System and method for computational prediction and recommendation of real-world entities and their value assertion

A system for generating computational predictions in a computing environment comprising one or more computer processors, the system comprising:

• a Correlation Module having a controller configured to:
• analyze knowledge from a plurality of data sources, including self-generating one or more of the plurality of data sources based on predictive analysis on the plurality of data sources;
• determine one or more objectives associated with a project;
• apply a plurality of ensembles of machine learning models to each of the plurality of data sources and conduct a combinatorial analysis of the plurality of data sources, wherein the combinatorial analysis comprises:
• assigning one or more goals to one or more colonies of Artificial Intelligence (AI) colonizers, each colony of AI colonizers comprising a lead colonizer and a plurality of captive agents navigating collectively towards the one or more goals and generating one or more intermediary outputs, wherein the one or more goals correspond to the one or more objectives;
• and
• analyzing the one or more intermediary outputs in a weighted combination to generate weighted outputs;
• identify, by the lead colonizer, a set of relevant features in the plurality of data sources;
• analyze, by the lead colonizer, the set of relevant features in view of the one or more goals and generate a positive reinforcement metrics and an associated path;
• incentivize, by the lead colonizer, using the positive reinforcement metrics, the plurality of captive agents to move towards the associated path;
• subscribe the one or more of the plurality of captive agents to the lead colonizer based on the positive reinforcement metrics;
• and
• collaborate and move the one or more of the plurality of captive agents towards the associated path as incentivized by the lead agent;
• and
• assemble, using a supervisory algorithm, the weighted outputs based on the one or more objectives and generate a computational prediction associated with the project.

A non-transitory computer-readable storage medium, having stored thereon a computer-executable program which, when executed by at least one processor, causes the at least one processor to:

• analyze knowledge from a plurality of data sources, including self-generating one or more of the plurality of data sources based on predictive analysis on the plurality of data sources;
• determine one or more objectives associated with a project;
• apply a plurality of ensembles of machine learning models to each of the plurality of data sources and conduct a combinatorial analysis of the plurality of data sources, wherein the combinatorial analysis comprises:
• assigning one or more goals to one or more colonies of Artificial Intelligence (AI) colonizers, each colony of AI colonizers comprising a lead colonizer and a plurality of captive agents navigating collectively towards the one or more goals and generate one or more intermediary outputs, wherein the one or more goals correspond to the one or more objectives;
• and
• analyzing the one or more intermediary outputs in a weighted combination and generate weighted outputs;
• identify, by the lead colonizer, a set of relevant features in the plurality of data sources;
• analyze, by the lead colonizer, the set of relevant features in view of the one or more goals and generate a positive reinforcement metrics and an associated path;
• incentivize, by the lead colonizer, using the positive reinforcement metrics, the plurality of captive agents to move towards the associated path;
• subscribe the one or more of the plurality of captive agents to the lead colonizer based on the positive reinforcement metrics;
• and
• collaborate and move the one or more of the plurality of captive agents towards the associated path as incentivized by the lead agent;
• and
• assemble, using a supervisory algorithm, the weighted outputs based on the one or more objectives and generate a computational prediction associated with the project.