Advancing Metaverse's Experience Through Optimization Of Players' Decisions

Abstract

This paper presents a Decision Support System (DSS) for navigating a dynamic Metaverse environment, where individual characters are represented as aggregated-NFTs. The DSS utilizes genetic algorithms (GAs) for optimizing players’ decision-making. Specifically, addressing the challenge of maximizing character rarity within budget constraints and metaverse rules. The optimization problem is formulated as a Knapsack problem, and the GA heuristic is employed due to its effectiveness in dealing with NP-hard problems. To investigate the uncoordinated repeated games scenario, a versatile simulation framework was developed, in which multiple artificial players compete to maximize the rarity of their individual character in a Metaverse world called Sunflower land. The simulation results demonstrate that players utilizing the DSS outperform Greedy players.

Stav Cohen

PhD Candidate

I’m a DataScience PhD candidate at the Technion – Israel Institute of Technology