Teamwork under extreme uncertainty: AI for Pokemon ranks 33rd in the world
Nicholas R. Sarantinos

TL;DR
This paper presents an AI agent for Pokemon, addressing challenges of teamwork and uncertainty, achieving 33rd place globally, and offering insights applicable to complex real-world team management under uncertainty.
Contribution
The paper introduces novel AI algorithms tailored for Pokemon, focusing on team balance and uncertainty, advancing AI capabilities in complex, uncertain environments.
Findings
AI agent outperformed previous attempts
Achieved 33rd place globally in Pokemon battles
Operated efficiently on limited hardware
Abstract
The highest grossing media franchise of all times, with over $90 billion in total revenue, is Pokemon. The video games belong to the class of Japanese Role Playing Games (J-RPG). Developing a powerful AI agent for these games is very hard because they present big challenges to MinMax, Monte Carlo Tree Search and statistical Machine Learning, as they are vastly different from the well explored in AI literature games. An AI agent for one of these games means significant progress in AI agents for the entire class. Further, the key principles of such work can hopefully inspire approaches to several domains that require excellent teamwork under conditions of extreme uncertainty, including managing a team of doctors, robots or employees in an ever changing environment, like a pandemic stricken region or a war-zone. In this paper we first explain the mechanics of the game and we perform a…
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Taxonomy
TopicsBig Data and Business Intelligence
