Starcraft II requires complex strategy decisions to be made in real-time. Planning resources, building the optimal structures and units, when to upgrade and when to defend or attack will all be critical to the success in a game. Large language models, such as GPT-4, have shown impressive performance on various natural language tasks, such as text summarization, question answering, and text generation. But how well can they make strategic decisions in a dynamic and competitive environment?
In this demo intensive session Alan will explore the capabilities of large-language models for strategic decision making. He will explain the strategy decisions that need to be made in a Starcraft game, and what makes it an ideal scenario for exploring and evaluating the capabilities of GPT-4. Alan will then focus on the techniques for leveraging GPT models for strategic decision making, including prompt engineering and state description as well as parsing and understanding the response messages. He will also discuss different scenarios where large language models can be leveraged in strategic decision making.
Join me for this session if you want to learn more about using GPT models in strategic decision-making processes, or just sit back and watch GPT-4 destroy the Zerg.
Alan Smith is an AI and ML developer, trainer, mentor and evangelist at Active Solution in Stockholm. He has a strong hands-on philosophy and focusses on embracing the power and flexibility of cloud computing to deliver engaging and exciting demos and training courses.
Alan has held the MVP title since 2005, and is currently an AI MVP. He is in the organization team for the CloudBurst and AI Burst Conferences.
