Contextual Matchmaking Using Player Archetypes and Dynamic Objectives
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Solution Overview
Problem
Conventional video game matchmaking primarily focuses on skill-based matching, which may not cater to players seeking a fun and social gaming experience, leading to dissatisfaction and potential dropout from matches.
Innovation Solution
Contextually aware active social matchmaking system that leverages player information to typify affinities and tendencies, matching players with complementary traits and generating dynamic in-game objectives to enhance enjoyment, using machine learning to refine matchmaking and objective generation based on player feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If skill-based matching is used to ensure competitive balance, then match competitiveness is improved, but player enjoyment and satisfaction deteriorate
Solution Approach 1:
The system changes the parameters used for matchmaking from purely skill-based metrics to a multi-dimensional parameter set that includes play style, social preferences, and contextual factors. This allows the system to optimize for both competitiveness and enjoyment by adjusting matching criteria based on player archetype compatibility rather than just skill level.
Solution Approach 2:
The system applies different matching criteria to different player archetypes and contexts. Instead of a universal skill-based matching approach, it tailors the matching parameters locally to each player's preferences and play style, creating customized match compositions that balance competitiveness with individual player enjoyment needs.
2Ease of operation
If dynamic in-game objectives are generated based on player archetypes, then player engagement is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of player information and archetype classification before matches are formed. By pre-processing player data and determining archetypes in advance, the system can generate contextualized objectives more efficiently during gameplay without adding excessive real-time complexity to the matching process.
Solution Approach 2:
The player archetype system serves multiple functions simultaneously: it drives matchmaking decisions, generates contextualized in-game objectives, and provides a framework for evaluating match outcomes. This multi-functionality reduces overall system complexity by using a single unified framework rather than separate systems for each function.
3Reliability
If machine learning models are used to tune matchmaking rules, then match quality is improved, but computational resources increase
Solution Approach 1:
The system applies machine learning selectively to tune specific aspects of matchmaking rules rather than using ML for all matching decisions. By applying ML partially to optimize key parameters while using deterministic rules for other aspects, the system achieves improved match quality without requiring excessive computational resources for full ML-based matching.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for contextual matchmaking. The method includes receiving player information for a plurality of players, the player information for each player comprising at least one of player statistics, player tendencies, and player preferences. The method also includes generating player archetypes for each of the plurality of players based on the player information, each of the player archetypes comprising at least one premier scenario. The method also includes matching the players based on the player archetypes. The method also includes generating in-game objectives based on the player archetypes.