Reinforcement learning approach to approximate a mental map of formal logic

A reinforcement learning system, comprising:

• one or more processors;
• and
• one or more programs residing on a memory and executable by the one or more processors, the one or more programs configured to:
• select an action to maximize an expected future value of a reward function;
• and, wherein the reward function depends on a logic-engine that upon receiving a logical sentence returns a positive reward and upon receiving an illogical sentence returns a negative reward;
• provide the reinforcement learning agent with a pool of states, actions, and rewards and a function approximator wherein using the function approximator the reinforcement learning agent predicts the best action to take resulting in maximum reward;
• upon obtaining an learning rate above a threshold save the weights of the function approximator to memory;
• wherein an optimized policy is obtained such that the reinforcement learning agent has generated a mental map of the logic of the one or more sentences.

A logical correction system, comprising:

• input sentence;
• one or more processors;
• and
• one or more programs residing on a memory and executable by the one or more processors, the one or more programs configured to:
• perform reinforcement learning utilizing an automated theorem prover as the reward mechanism wherein an optimal policy is achieved when a minimum number of edits results in a logical sentence;

A method for reinforcement learning, comprising the steps of:

• receiving one or more sentences;
• performing actions from a set of available actions on a state wherein the state is a sentence;
• selecting an action to maximize the expected future value of a reward function;
• wherein the reward function depends on at least partly on:
• restoring the sentences to a logical state. providing the reinforcement learning agent with a pool of states, actions, and rewards and a function approximator wherein using the function approximator the reinforcement learning agent predicts the best action to take resulting in maximum reward;

A real-time logic engine, comprising:

• one or more sentences;
• a physical hardware device consisting of a memory unit and processor;
• a software consisting of a computer program or computer programs;
• an output signal that indicates that one or more sentences is logical or one or more sentences is nonsensical;
• wherein one or more processors;
• and
• one or more programs residing on a memory and executable by the one or more processors, the one or more programs configured to:
• receive one or more sentences;
• generate a network from the one or more sentences;
• identify symmetry in the network;
• rank the symmetry in the network;
• negate the ranked symmetry of the network into logical relationships;
• generate logical equations by formalizing logical relationships into a formal logic;
• infer a proof by evaluating the logical equations with an automated theorem prover;
• wherein modifications made to one or more sentences can be evaluated to determine if the modifications results in a logical or nonsensical sentence.