AI Virtual Object Community Influence Range Intersection
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Solution Overview
Problem
Existing methods for controlling AI virtual objects in open-world games result in high performance overheads due to the need for individual intersection tests with a player's influence range, leading to inconsistent behavior among AI virtual objects within the same community.
Innovation Solution
A method and apparatus for controlling AI virtual objects by generating a first influence range for a virtual object community and multiple second influence ranges for a player virtual object, determining a target second influence range that intersects with the first influence range and meets specific size conditions, and using this target range to control the activity of AI virtual objects within the community.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual intersection tests are performed for each AI virtual object with the player's influence range, then measurement precision of AI object interaction is improved, but performance overhead increases and device complexity increases
Solution Approach 1:
The patent merges multiple individual AI virtual objects into a single virtual object community, performing one intersection test for the entire community instead of individual tests for each AI object. This combining approach maintains interaction detection accuracy while significantly reducing computational overhead and improving system productivity.
Solution Approach 2:
The virtual object community serves multiple functions: it represents the collective interaction state of all member AI objects, performs unified intersection testing with player influence ranges, and enables batch control policy application. This multi-functionality reduces the number of operations needed while maintaining measurement precision.
2Measurement precision
If individual intersection tests are performed for each AI virtual object, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple individual AI virtual objects into a single virtual object community, performing one intersection test for the entire community instead of individual tests for each AI object. This combining approach maintains interaction detection accuracy while significantly reducing computational overhead and improving system productivity.
3Measurement precision
If LOD intersection tests are performed with multiple levels (LOD0, LOD1, LOD2), then measurement precision of influence range intersection is improved, but performance overhead increases
Solution Approach 1:
The patent segments the influence range detection into hierarchical levels (LOD0, LOD1, LOD2) where broader community-level tests are performed first, followed by more detailed individual object tests only when necessary. This segmented approach maintains measurement precision while reducing overall computational overhead by avoiding redundant detailed tests.
Data Source
AI summary
A method and apparatus for controlling an artificial intelligence (AI) virtual object to reduce performance overhead of a computing device. The method comprises: obtaining a virtual object community for a plurality of AI virtual objects; generating a first influence range corresponding to the virtual object community and a plurality of second influence ranges corresponding to a player virtual object, the first influence range corresponding to an activity range of the virtual object community in a virtual environment, and the second influence range being an influence range generated with the player virtual object as a center; determining a second influence range that intersects with the first influence range and that has a range size meeting a condition as a target second influence range; controlling, an activity of an AI virtual object in the plurality of AI virtual objects based on a control policy corresponding to the target second influence range.