Player Action Scoring for Decentralized Metaverse Decisions
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Solution Overview
Problem
Existing systems in metaverses lack an autonomous decentralized system for determining matters related to player actions, advertisement placement, and electronic commerce transactions, which are crucial for enhancing user engagement and transaction efficiency.
Innovation Solution
A system that calculates scores based on player and field features, using machine learning models and database units, to determine actions, advertisement placement, and transaction conditions, while allowing stakeholder voting to change rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a centralized administrator manages player actions, advertisement placement, and transactions in a metaverse, then system control and decision-making are simplified, but system autonomy and decentralized management are reduced
Solution Approach 1:
The system enables autonomous decision-making through machine learning models that automatically evaluate player actions, determine advertisement placements, and manage transactions without centralized administrator intervention. The ML models self-adjust based on accumulated data, achieving system self-service and autonomy.
Solution Approach 2:
Machine learning models serve as intermediary components between players, advertisers, and the metaverse environment. These ML models process information and make decisions autonomously, mediating complex interactions without requiring direct centralized control while maintaining system coherence.
2Productivity
If machine learning models are used to calculate scores and determine actions autonomously, then decision-making speed and personalization are improved, but system complexity and computational requirements increase
Solution Approach 1:
Machine learning models are trained in advance with extensive data before deployment in the metaverse. This preliminary training phase enables the models to make rapid, accurate decisions during actual operation without requiring complex real-time computations, thus improving decision-making efficiency while managing computational complexity.
Solution Approach 2:
The decision-making process is divided into separate machine learning models for different functions (player action evaluation, advertisement placement, transaction management). This segmentation allows each model to specialize in specific tasks, improving overall efficiency while distributing computational complexity across multiple manageable components.
3Adaptability or versatility
If stakeholder voting mechanisms are implemented to change rules, then system adaptability and stakeholder engagement are improved, but decision-making time and system complexity increase
Solution Approach 1:
The system implements periodic voting cycles where stakeholders can propose and vote on rule changes at predetermined intervals. This periodic mechanism balances adaptability with efficiency, allowing rule flexibility through stakeholder input while preventing continuous disruptions to system operation and maintaining reasonable decision-making timelines.
Data Source
AI summary
The present invention provides a system for determining matters needing to be determined when a player performs an action. The system is configured so as to: acquire data and/or information pertaining to a player and pertaining to an action (S801); calculate a first score representative of a characteristic of the player on the basis of the acquired data and/or information pertaining to the player (S802); acquire data pertaining to a field where the action is performed (S803); calculate a second score representative of a characteristic of the field, on the basis of the acquired data pertaining to the field (S804); and determine the matters needing to be determined on the basis of the first score and the second score (S805).