An object matching method, a model training method and a server

Abstract

The present application provides an object matching method, device, device, and medium, wherein the method includes: the server acquires a set of objects to be matched, the set of objects to be matched includes at least two objects to be matched, and each object to be matched contains at least one attribute information; M prediction combinations are generated according to the set of objects to be matched, wherein each prediction combination includes at least two queues, and any queue includes at least one object to be matched; each prediction combination is obtained by matching the prediction model The matching probability information; the matching prediction model includes a winning or losing predicting model, the winning or losing predicting model takes at least one attribute information corresponding to each object in at least two queues as an input, and takes the winning or losing probability as an output; according to each Matching probability information corresponding to each combination is predicted to determine the target matching result. Adopting this method can effectively avoid side splits in which there is a huge difference in player strength, and optimize the matching results of the target battle groups.

Description

technical field [0001] The present application relates to the technical field of online games, in particular to an object matching method, a model training method, a server and a computer-readable storage medium. Background technique [0002] In recent years, with the rapid development of the e-sports industry, players are paying more and more attention to the fairness of competitive games. Among them, Multiplayer Online Battle Arena (MOBA) games are widely favored by players all over the world. Different from traditional RTS (real-time strategy, real-time strategy) games, MOBA simplifies the roles that players need to operate from buildings, multiple arms, etc. to a single arm - hero, and thus derives a game that focuses on the faction hero arena battle. Popular MOBA games such as Dota, League of Legends, Three Kingdoms, Glory of Kings, etc. [0003] The basic principle of competitive online games is to match players of the same level together, and try to avoid a crushing ...

AI Technical Summary

Problems solved by technology

[0004] Since the ELO matching system based on the ELO algorithm only uses ELO points to describe the real strength of the player from a single dimension, the algorithm error of the ELO algorithm is relatively large, resulting in a discrepancy between the player's points and the player's strength. The real strength of the team is too different

For example, a matching result that often occurs is that the sum of the ELO points of the two teams is similar, but in fact the strength of the two teams is very different. One team is the trumpet of a relatively strong player, while the other team It is the first time for the players to reach the score, which leads to the crushing of the opponent by the small team, which is a typical crushing round

[0005] Therefore, the matching system based on the ELO score algorithm only locates the player's level based on the ELO score, but its positioning is not accurate enough.

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