Microsoft Technology Licensing, LLC

Benjamin David Van Durme

William B Dolan

Christopher John Brockett

Daniel Louis Klein

Adam David Pauls

Pengyu Chen

Jason Michael Eisner

Samuel McIntire Thomson

Gabriel Arien Desgarennes

Ryan H Volum

Emmanouil Antonios Platanios

Benjamin Lev Snyder

Eui Chul Shin

Christopher H Lin

Sudha Rao

Subhro Roy

Hamid Palangi

Aspects of the present disclosure relate to a personalized agent service that generates and evolves customized agents that can be instantiated in-game to play with users. Machine learning models are trained to control the agent's interactions with the game environment and the user during gameplay. A user may request that a personalized agent join the user's gameplay session. The user device sends a request for the personalized agent to a game platform. The game platform determines whether the user has a license to execute a second instance of the game. When the user has a license to execute a second instance of the game, the second instance of the game may be executed on the user device. Information received from a personalized agent service is used to instantiate a personalized agent in the second instance of the game.

Importing agent personalization instantiate a personalized agent in a user game session

Systems and methods are provided for automatically generating a program based on a natural language utterance using semantic parsing. The semantic parsing includes translating a natural language utterance into instructions in a logical form for execution. The methods use a pre-trained natural language model and generate a canonical utterance as an intermediate form before generating the logical form. The natural language model may be an auto-regressive natural language model with a transformer to paraphrase a sequence of words or tokens in the natural language utterance. The methods generate a prompt including exemplar input/output pairs as a few-shot learning technique for the natural language model to predict words or tokens. The methods further use constrained decoding to determine a canonical utterance, iteratively selecting sequence of words as predicted by the model against rules for canonical utterances. The methods generate a program based on the canonical utterance for execution in an application.

Semantic parsing of utterance using contractive paraphrasing

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Benjamin David Van Durme