Systems and methods for enforcing constraints to predictions

A system comprising:

• at least one processor, and
• memory storing a plurality of executable instructions which, when executed by the at least one processor, cause the system to:
• input, to a machine learning algorithm (MLA), input data and a first state of the MLA, wherein the MLA was trained to predict a probability distribution that the input data corresponds to pairs, each pair comprising a class in a set of classes and a next state of the MLA;
• output, by the MLA and based on the input data and the first state, the probability distribution, wherein the probability distribution comprises a predicted probability for each pair of a plurality of pairs, each pair comprising a class in the set of classes and a corresponding state of the MLA;
• add the first state and each state of the probability distribution to a set of states to be searched;
• apply a search to the set of states to be searched, wherein applying the search comprises:
• removing, from the set of states to be searched, any states that are end states,
• removing, from the set of states to be searched, any states that fail every constraint of the one of the one or more constraints,
• selecting, to input to the MLA, one or more states of the set of states to be searched,
• inputting, to the MLA, each of the selected one or more states, thereby generating one or more additional probability distributions,
• adding, to the set of states to be searched, one or more states of the one or more additional probability distributions thereby generating an updated set of states to be searched, and
• repeating the search with the updated set of states to be searched;
• and
• output one or more output states selected by the search, wherein each of the one or more output states contains an end state and wherein each of the one or more output states comprise a respective sequence satisfying at least one of the one or more constraints.