Abstract: Program code representing a semantic natural language processing (NLP) machine learning (ML) algorithm is stored in a memory. A processor executes the semantic NLP ML algorithm to generate initial scores that represent a degree of matching between candidate responses and an input phrase provided by a user during execution of program code. The processor also modifies one or more of the initial scores using one or more rules that associate a first phrase with a second phrase. The one or more rules are selected to modify the initial scores based on semantic similarity of the user input phrase and the first phrase determined by the semantic NLP ML algorithm and the semantic similarity of the response phrase with a corresponding candidate response. Execution of the program code is modified based on the modified initial scores. In some cases, the semantic NLP ML algorithm is used to implement a video game.

Abstract: A semantic natural language processing (NLP) machine learning (ML) model accesses an expression space that includes first natural language phrases that are mapped to actions that are available to an agent in a video game. The semantic NLP ML model receives a second natural language phrase that represents a stimulus for the agent in the video game. One of the actions for the agent is selected based on comparisons of the first natural language phrases and the second natural language phrase. The agent in the video game is then caused to perform the selected one of the actions. In some cases, the expression space is constructed based on an action grammar that defines one or more sentence structures that include one or more tokens that are replaced by natural language phrases to form natural language sentences in the expression space.

Abstract: A memory stores information representing a set of canonical utterances. A processor receives information representing an utterance from a first user of an application and selects a canonical utterance from the set of canonical utterances based on semantic comparisons of the utterance from the first user and the set of canonical utterances. The semantic comparisons include semantic retrieval and semantic similarity operations that can be performed by a semantic natural language processing machine learning model. The processor presents the canonical utterance to a second user of the application instead of presenting the utterance from the first user. In some cases, the processor replaces the utterances from the user in a text stream or a voice chat with the canonical utterances in the set of canonical utterances.

Abstract: A video game system provides dialog responses based on a natural language model (NLM). The NLM is a language model that receives a language input, such as a dialog selection, audio recording, or natural language text input provided by a user of the video game system. In response to the language input, and based on a corpus of natural language candidate lines, the NLM identifies one or more potential responses. The video game system selects a final response from the identified potential responses and provides the selected response to the user via, for example, one or more display frames or via an audio output.

Abstract: A module that implements a state machine generates a first environmental condition experienced by a player in a video game. The first environmental condition is produced by the module operating in a first state of the state machine and the first state is associated with a first natural language tag. The state machine transitions from the first state to a second state based on a semantic similarity of an input phrase and a second natural language tag associated with the second state. The module, while operating in the second state, generates a second environmental condition experienced by the player in the video game. In some cases, the module selects the second natural language tag based on a ranking of tags for a plurality of states on their semantic similarity to an input phrase. The ranking is generated by a semantic natural language processing (NLP) machine learning (ML) algorithm.

Abstract: Game decisions are coordinated using a semantic natural language processing (NLP) machine learning (ML) algorithm, which is stored in a memory in some cases. In response to a game event, a processor records a text string that represents the game event in a text log that includes a sequence of text strings that represent game events that have transpired during a portion of the game. The processor also generates, using the semantic NLP ML algorithm, scores for labeled actions or content based on the text log and a curve that represents a target player experience as a function of progress through the game. The processor further serves one or more of the labeled actions or content that is selected based on the scores. The labeled actions or content are served to a display associated with the processor.

Abstract: A processor executes program code that represents a portion of a video game and adds a sequence of text strings that represent game events to a text log during execution of the program code. The processor (or another processor that has access to the text log) performs a natural language processing (NLP) analysis of the text log to determine one or more characteristics of the portion of the video game. In some cases, the NLP analysis includes a sentiment analysis that attempts to determine characteristics of a player's experience while playing the video game, summarization technology that creates a human-readable summary of an aspect of the game or a portion of the video game, a semantic NLP ML algorithm in the semantic similarity modality to answer questions regarding the player's experience during the video game, or grouping players in a multiplayer game based on in-game behavior.