Method And System For Implementing Artificial Intelligence

Patent metadata

Field	Value
Publication number	US-2015126286-A1
Application number	US-201514597124-A
Country	US
Kind code	A1
Filing date	Jan 14, 2015
Priority date	Sep 27, 2013
Publication date	May 7, 2015
Grant date	—

Abstract

A method of implementing artificial intelligence for a non-playing character in a game includes: collecting respective real-user response strategy data associated with each of a plurality of game interactions between two human users, including respective parameter values for a action performed by a respective first human user in a respective game scenario, a respective response performed by a respective second human user in response to the respective action, and a respective outcome of the game interaction; identifying recommended game response types for each of a plurality of possible game action types based on the respective outcomes for the plurality of game interactions; and providing the recommended game response types for each possible game action type for selection by a second device serving as the non-playing character in a game session of the game played between a human user and the non-playing character.

First claim

What is claimed is:

1 . A method of implementing artificial intelligence for a non-playing character in a game:
at a first device having one or more processors and memory:
collecting respective real-user response strategy data associated with each of a plurality of game interactions between two human users while playing the game, the respective real-user response strategy data for each game interaction including respective parameter values for: (1) a set of action parameters for a respective game action performed by a respective first human user in a respective game scenario, (2) a set of response parameters for a respective game response performed by a respective second human user in the respective game scenario in response to the respective game action performed by the respective first human user, and (3) a set of response outcome parameters for a respective outcome of the game interaction;
identifying, from the collected real-user response strategy data, a respective set of recommended game response types for each of a plurality of possible game action types based at least on the respective values for the set of response outcome parameters for each of the plurality of game interactions; and
providing the respective set of recommended game response types for each of the plurality of possible game action types for selection by a second device serving as a non-playing character in a game session of the game played between a human user and the non-playing character.

2 . The method of claim 1 , wherein the respective real-user response strategy data for each of the plurality of game interactions further comprises respective parameter values for a set of environmental attribute parameters for a respective game scenario in which the game interaction has occurred, and wherein the method further comprises:
identifying, from the set of environmental attribute parameters, a respective set of controlling environmental attributes for a first possible game action type of the plurality of possible game action types, based at least on the respective values for the set of response parameters for a first plurality of game responses included in the collected real-user response strategy data, wherein the first plurality of game responses have been performed in response to respective game actions of the first possible game action type.

3 . The method of claim 1 , further comprising:
determining a respective selection priority for each of the respective set of recommended game response types for a first possible game action type of the plurality of possible game action types, based at least on the respective values for the set of response outcome parameters associated with the respective game actions of the first possible game action type found in the collected real-user response strategy data.

4 . The method of claim 1 , further comprising:
determining a respective selection priority for each of the respective set of recommended game response types for a first possible game action type of the plurality of possible game action types, based at least on a total number of times that the recommended game response type is used by a respective human user when responding to the respective game actions of the first possible game action type, as recorded in the collected real-user response strategy data.

5 . The method of claim 1 , wherein providing the respective set of recommended game response types for each of the plurality of possible game action types for selection by a second device further comprises:
providing, with each recommended game response type for each possible game action type, a corresponding game scenario type defining a respective game scenario in which said each recommended game response type is available for selection by the second device to generate a response to a game action of said each possible game action type.

6 . The method of claim 1 , wherein providing the respective set of recommended game response types for each of the plurality of possible game action types for selection by a second device further comprises:
providing, with each recommended game response type for each possible game action type, a corresponding selection priority defining a respective probability by which said each recommended game response type is to be selected by the second device to generate a response to a game action of said each possible game action type.

7 . The method of claim 1 , wherein the game is a fighting game and each game interaction includes one or more offense moves and one or more counter moves in a single exchange between two players.

8 . The method of claim 1 , wherein the non-playing character is a new character added to an updated version of the game after the collection of the game response strategy data.

9 . A system for implementing artificial intelligence for a non-playing character in a game, the system comprising:
one or more processors; and memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to perform operations comprising: collecting respective real-user response strategy data associated with each of a plurality of game interactions between two human users while playing the game, the respective real-user response strategy data for each game interaction including respective parameter values for: (1) a set of action parameters for a respective game action performed by a respective first human user in a respective game scenario, (2) a set of response parameters for a respective game response performed by a respective second human user in the respective game scenario in response to the respective game action performed by the respective first human user, and (3) a set of response outcome parameters for a respective outcome of the game interaction; identifying, from the collected real-user response strategy data, a respective set of recommended game response types for each of a plurality of possible game action types based at least on the respective values for the set of response outcome parameters for each of the plurality of game interactions; and providing the respective set of recommended game response types for each of the plurality of possible game action types for selection by an application device serving as a non-playing character in a game session of the game played between a human user and the non-playing character.

10 . The system of claim 9 , wherein the respective real-user response strategy data for each of the plurality of game interactions further comprises respective parameter values for a set of environmental attribute parameters for a respective game scenario in which the game interaction has occurred, and wherein the operations further comprise:
identifying, from the set of environmental attribute parameters, a respective set of controlling environmental attributes for a first possible game action type of the plurality of possible game action types, based at least on the respective values for the set of response parameters for a first plurality of game responses included in the collected real-user response strategy data, wherein the first plurality of game responses have been performed in response to respective game actions of the first possible game action type.

11 . The system of claim 9 , wherein the operations further comprise:
determining a respective selection priority for each of the respective set of recommended game response types for a first possible game action type of the plurality of possible game action types, based at least on the respective values for the set of response outcome parameters associated with the respective game actions of the first possible game

Assignees

• Tencent Tech Shenzhen Co Ltd

Inventors

• Guo Kangping

Classifications

• A63F13/67 Primary

  adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use · CPC title

• A63F13/55 Primary

  Controlling game characters or game objects based on the game progress · CPC title

• A63F13/79

  involving player-related data, e.g. identities, accounts, preferences or play histories · CPC title