Simulated Player Matchmaking for Online Gaming

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Solution Overview

Problem

Online gaming systems face frustration due to delays or failures in finding suitable matches for players due to factors like user location, latency, and connection quality, leading to unsatisfactory matchmaking results such as skill imbalances or extended wait times.

Innovation Solution

The introduction of simulated players operated by machine learned models that mimic human behavior, allowing for matchmaking with human players based on skill scores and playstyles, and the use of behavior simulation models to provide natural and cohesive gameplay experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional matchmaking systems are used to match players online, then players can compete against real human opponents, but delays or failures in finding suitable matches occur due to limited player availability and geographic constraints

Engineering Contradiction:

Improvematchmaking success rateVSAvoidwait time for match

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system creates simulated player copies that mimic human player behavior through machine learned models. These simulated players can be instantly deployed to fill matchmaking slots, eliminating wait times caused by limited human player availability while maintaining realistic gameplay interactions

Inventive Principle:

Principle #26Copying

Solution Approach 2:

Simulated players act as intermediaries between the matchmaking system and human players. They bridge the gap during periods when suitable human opponents are unavailable, ensuring continuous matchmaking functionality without compromising the core human-versus-human gameplay experience

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If simulated players are introduced to fill matchmaking gaps, then matchmaking availability improves, but system complexity increases due to machine learned models and behavior simulation

Engineering Contradiction:

Improvematchmaking flexibilityVSAvoidmatchmaking system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The simulated player system serves multiple functions: it fills matchmaking gaps, provides training opponents, enables gameplay during low-traffic periods, and maintains skill-based matching consistency. This multi-functionality justifies the added complexity by delivering diverse benefits from a single architectural addition

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system replaces manual matchmaking operations with automated machine learned models that simulate player behavior. This substitution reduces operational complexity by eliminating the need for manual intervention in match formation while improving adaptability to varying player populations and skill levels

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Manufacturing precision

If simulated players are used to maintain consistent skill levels, then gameplay quality is preserved, but ensuring natural and cohesive simulated player behavior becomes difficult

Engineering Contradiction:

Improveskill level consistencyVSAvoidbehavior simulation naturalness

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The machine learned models continuously learn from gameplay data and feedback mechanisms, adjusting simulated player behavior to maintain naturalness while preserving consistent skill levels. This feedback loop enables the system to refine behavior patterns over time, making simulated players increasingly indistinguishable from human opponents

Inventive Principle:

Principle #23Feedback

4Productivity

If simulated players fill all available slots, then matchmaking speed increases, but the gaming experience may become less engaging without real human opponents

Engineering Contradiction:

Improvematchmaking speedVSAvoidgameplay quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system applies simulated players partially rather than universally, deploying them only to fill specific slots when human opponents are unavailable. This partial action maintains high matchmaking speed by instantly filling gaps while preserving gameplay quality by prioritizing human-versus-human matches when possible

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11260306B2Matchmaking for online gaming with simulated players

Publication Date: 2022.03.01 ELECTRONIC ARTS INC

AI summary

A matchmaking system matches players for online gaming, when some of the players may be human players and others may be simulated players. The matchmaking system may determine a first skill score associated with a first player for an online game, determine a behavior simulation model for a simulated player is available for the online game, determine a second skill score associated with the behavior simulation model for the online game, and determine the behavior simulation model matches with the first player based at on the first skill score being within a threshold of the second skill score. The matchmaking system may then instantiate a simulated player based on the behavior simulation model and instantiate the online game with the first player and the simulated player.