Artificial intelligence based system and method for dynamic goal planning

A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to:

• receive an initial goal plan comprising an initial action sequence including a plurality of actions ordered in a forward direction;
• process the initial action sequence through an encoder of a bidirectional recurrent neural network (“RNN”) to generate an encoder output, including a first hidden state representation;
• process the encoder output through a decoder of the bidirectional RNN to generate a decoder output, including a forward hidden state representation and a backward hidden state representation for each action of the initial action sequence;
• apply a context vector to the decoder output to generate a weighted decoder output;
• obtain a state of the initial goal plan, wherein the state of the initial goal plan includes a plurality of states each corresponding to an action of the initial goal plan;
• convert the state of the initial goal plan into vector embeddings;
• concatenate the weighted decoder output with the vector embeddings;
• and
• process the concatenated weighted decoder output and vector embeddings through a SoftMax classifier to determine an updated goal plan.

A system for updating a dynamic goal plan, comprising:

• one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to:
• receive an initial goal plan comprising an initial action sequence including a plurality of actions ordered in a forward direction;
• process the initial action sequence through an encoder of a bidirectional recurrent neural network (“RNN”) to generate an encoder output, including a first hidden state representation;
• process the encoder output through a decoder of the bidirectional RNN to generate a decoder output, including a forward hidden state representation and a backward hidden state representation for each action of the initial action sequence;
• apply a context vector to the decoder output to generate a weighted decoder output;
• obtain a state of the initial goal plan, wherein the state of the initial goal plan includes a plurality of states each corresponding to an action of the initial goal plan;
• convert the state of the initial goal plan into vector embeddings;
• concatenate the weighted decoder output with the vector embeddings;
• and
• process the concatenated weighted decoder output and vector embeddings through a SoftMax classifier to determine an updated goal plan.