A method for assisting game play. The method includes monitoring game play of the user playing a gaming application, wherein the user has a defined task to accomplish, wherein the task is associated with a task type. The method includes determining a task type proficiency rule for the task type based on results of a plurality of players taking on a plurality of tasks having the task type. The method includes determining a player proficiency score for accomplishing the task based on the task type proficiency rule. The method includes determining a user predictive rate of success in accomplishing the task based on the player proficiency score, the task type proficiency rule, and the task. The method includes determining a recommendation for the user based on the user predictive rate of success.

An automated video game testing framework and method includes communicatively coupling an application programming interface (API) to an agent in a video game, where the video game includes a plurality of in-game objects that are native to the video game. The agent is managed as an in-game object of the video game. A test script is executed to control the agent, via the API, to induce gameplay and interrogate a behavior of a test object. The test object is identified from the plurality of in-game objects based on a query that specifies an object attribute of the test object.

The present disclosure refers to a method and a system for synthesizing videogame assets, the method includes receiving a first set of videogame assets as input in a videogame asset synthesizer. The videogame asset synthesizer may identify certain common features among the assets of the first set of videogame assets and clusters the videogame assets into different asset profiles based on the certain common features identified. The videogame asset synthesizer may synthesize a subset of asset candidates for an asset profile, based on the videogame assets clustered into the asset profile and rate the asset candidates synthesized, based on a threshold set for at least one parameter of the asset candidates. The assets may be stored in a database, based on the rating.

A method for dynamic adjustment of interactive game play includes identifying a game session for a game played between first and second players, with the first and second players being connected from geographic locations having first and second elevations, respectively. The method also includes determining an objective in the game that the first and second players are predicted to achieve, and identifying a first path to be traversed by the first player to reach the objective in the game and identifying a second path to be traversed by the second player to reach the objective in the game. Each of the first and second paths includes a respective plurality of game actions to be accomplished by the first and second players. The method further includes adjusting a physical activity rating of select ones of the respective plurality of game actions based on a difference between the first and second elevations.

Techniques are described herein for automatically pre-constructing bundles of play-items based on the degree of synergy between the play-items. The degree of synergy between play-items is determined empirically, based on contents of player-constructed game-sets and outcomes of actual games using those game-sets. Automatically pre-constructing bundles involves generating pairwise synergy scores between every possible pair of play-items, and then (for bundles containing more than two items) using those pairwise synergy scores to determine bundle-wise synergy scores. Techniques are provided for identifying candidate bundle(s) of play-items to offer or award to a player. The candidate bundles are determined based on synergies between the play-items in each of the pre-constructed bundles and the play-items in the player's specific game-set.

Multiplayer video games involve multiple players playing using either a single computer system or multiple computer systems connected together. While a first player of the multiplayer game is playing the multiplayer game, the first player's actions are tracked and stored. When the first player quits or stops responding before the multiplayer game has completed, the first player is replaced by a computer-simulated version of the first player that selects its actions based on probabilities calculated from the tracked actions of the first player. The multiplayer game is thus able to continue without interruption or disruption, and the first player's playstyle is preserved.

The game device includes a reception unit that is configured to receive instruction information created when a user performs an operation on an input device triggered by a sound output while a game progresses and a derivation unit that is configured to start measuring the degree of fatigue on the basis of the instruction information received by the reception unit and derive the degree of fatigue of the user on the basis of an operation performed by the user during the started measurement of the degree of fatigue.

Systems and methods for determining whether to enable color blind accessibility settings within the course of a user interactive narrative are described herein. Virtual color blindness indication objects containing colors that are visibly distinguishable within a single dichromatic visual spectrum can be utilized in objectives to determine a user's dichromatic visual deficiency type.

Systems and methods for improving initial results of a reinforcement learning model are described herein. In an embodiment, a server computer initiates a reinforcement learning model for a modeled system. While executing the reinforcement learning model, the server computer computes a first result value for a particular action using the reinforcement learning model and a second result value for the particular action using a heuristic separate from the reinforcement model. Based, at least in part, on the first result value for the particular action and the second result value for the particular action, the server computer performs the particular action. The server computer determining a result from performing the particular action and updates the reinforcement learning model.

In a game tendency analysis system applied to a game in that a plurality of players compete against each other using an aggregate of a plurality of physical game media, and analyzing a tendency related to constitution of the aggregate used in the game, at least a part of the game media included in an aggregate, which is constituted to be used in the game by each player, is identified, an identification result thereof is separately recorded, as constitution example information which indicates a constitution example of the aggregate, in predetermined constitution example data for each of the aggregates, and a frequent-use media group, which is a combination of game media included in the aggregate with a relatively high frequency, is estimated as an element representing at least a part of the tendency, based on the constitution example data.