A label prediction model training method, label prediction method and device

Abstract

The application discloses a label prediction model training method, a label prediction method and a device, which are used in the field of artificial intelligence. The application method includes: obtaining game data to be trained from game video samples; obtaining image features according to the game data to be trained; obtaining internal information features according to the game data to be trained; obtaining M overall view labels according to the game data to be trained, where M is greater than Or an integer equal to 2; according to image features, local information features and M global view labels, train the global view label prediction model. For the same frame of training data, this application can extract the corresponding big-picture labels from multiple categories, and combine multiple categories of big-picture labels to train the model, avoiding a single macro intention, so as to better describe the player's real intention . It can also automatically label the training data through the program, which saves the labor cost of expert labeling and improves the convenience of labeling.

Description

technical field [0001] The present application relates to the field of artificial intelligence, and in particular to a method for training a label prediction model, a method and a device for label prediction. Background technique [0002] Artificial intelligence (AI) programs have already beaten professional players at board games with well-defined rules. In contrast, the operation of multiplayer online battle arena (MOBA) games is more complicated. For MOBA games, micro-operations mainly refer to the specific operations of game characters in the current scene, while the overall view mainly refers to the large-scale transfer and scheduling of game characters to form certain strategies and tactics. [0003] At present, a hierarchical macro strategy model based on MOBA games is proposed. The input of the model is attribute information (such as heroes, wild monsters, and soldier lines, etc.), map information (such as obstacles, etc.), global information (such as time, etc.) an...

AI Technical Summary

Problems solved by technology

[0004] However, the definition of the big picture label in the prior art is based on the location of the attacking resources. Therefore, in the micro-operation prediction process, only the macro strategy for the purpose of attack is considered, while in the actual operation of the player, the macro Intentions include more than attacks. The overall view of the label is one-sided, and it is difficult to fully describe the player's true intentions.

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