Behavior-Aware Player Selection Using Graph Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional multiplayer game matchmaking systems primarily focus on skill level, leading to players being matched with unfamiliar peers, which can hinder camaraderie and teamwork, as well as fail to account for player preferences regarding behaviors such as profanity or gameplay styles, resulting in an unsatisfactory gaming experience.
Innovation Solution
A behavior-aware player selection method that groups players based on their exhibited behaviors and preferences, using graph analysis to determine affinities between players, allowing for more compatible and enjoyable multiplayer sessions by matching players with similar behavior preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If players are matched based on skill level only, then gameplay competitiveness is improved, but player social connection and camaraderie deteriorate
Solution Approach 1:
The patent combines multiple matching criteria (skill level, behavior compatibility, social connections) into a unified player selection system. The server merges these different factors to select players for game sessions, thereby maintaining gameplay competitiveness while simultaneously improving social connection and camaraderie among players.
Solution Approach 2:
The system changes the parameters used for player matching from solely skill-based to a multi-dimensional approach including behavior analysis and social connection metrics. By analyzing player behaviors and preferences, the system transforms the matching parameters to include compatibility scores that reflect both competitive ability and social compatibility.
2Adaptability or versatility
If players are constantly matched with different players, then skill diversity is improved, but team learning and camaraderie development deteriorate
Solution Approach 1:
The system performs preliminary actions by establishing social connections and analyzing player behaviors before game sessions begin. The server pre-processes player data to identify compatible players and build social networks in advance, allowing players to be matched with both skill-diverse and socially-compatible teammates, thereby enabling team learning while maintaining diversity.
3Ease of operation
If behavior analysis is added to player matching, then player compatibility and enjoyment are improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically analyzing player behaviors and generating compatibility scores without requiring manual intervention. The server autonomously processes player data, tracks behaviors, and makes matching decisions based on analyzed patterns, thereby improving compatibility while managing system complexity through automation rather than manual processes.
Data Source
AI summary
Systems and methods are described for generating a multi-player game session including a set of players with compatible preferences and behaviors. Each player can be associated with a set of behaviors exhibited by that player, as well as a set of preferences for those behaviors. On a request to initiate a game session, a first player can be paired with a set of other players whose behaviors are compatible with the preferences of the first player. Graph analysis may be used to facilitate selection of players. For example, each player may be represented as a node within a graph, and be connected to other nodes via an edge weighted according to a compatibility of preferences of the two players with their respective behaviors. In some instances, behaviors or preferences of players are determined based on analysis of in-game actions.