System and methods of training and operation for a general artificial intelligence system for data generation in behavioral control systems

A method of training, operating, and transforming an agentic artificial intelligence control system, the method comprising:

• collecting training data by recording raw state snapshot and corresponding decisions to form state-decision pairs;
• preprocessing and cleaning the collected data to generate a State-Decision Training Dataset;
• initializing an Agentic data model that maps state element identifiers to an associative container pre-configured with selection and collapse functions;
• training an Agent AI by updating weights in weighted-set within the associative container in response to each sample from the State-Decision Training Dataset;
• executing the trained Agent AI during operation by retrieving a weighted-set for a live state object and collapsing the weighted-set to output a command object;
• transforming the trained Agent AI into an alternative control-logic representation by:
• creating a mapping list of all data model and weighted-set pairs;
• constructing a base structure for a target representation;
• populating selector nodes or state transitions according to the data model;
• collapsing weighted-set into deterministic or stochastic logic nodes;
• merging generated structures into a unified logical representation;
• and
• serializing or exporting the representation for deployment;
• deploying the serialized representation to a target environment incapable of natively executing the weighted-set form.

A transformation engine for converting a trained Agentic AI into alternative control-logic representations, comprising:

• a BT transformation module configured to generate Behavior Trees with selector hierarchies and collapse-node leaves;
• a DT transformation module configured to generate Decision Trees with value-based branching;
• an FSM transformation module that incorporates a FSM transformation variant and state delta-frequency FSM transformation variant to define Finite State Machine states from low-variance data elements;
• a collapse node function library providing Max Path Collapse, Min Path Collapse, Sequence Collapse, and Random Selector Collapse;
• a FSM collapse node function library providing Max FSM Collapse, Min FSM Collapse and Weighted Roll FSM Collapse;
• a tree merging module operative to integrate partial logical structures into a unified logical representation having a single root node;
• a hard-coded logic serializer configured to output source-code level conditional logic corresponding to the unified logical representation;
• and
• a representation store communicatively coupled to the modules for persistent storage of generated Behavior Trees, Decision Trees, FSMs, and ASTs.