Showing: 11 - 20 of 29 RESULTS
From Cloud to Edge: Optimizing Small Language Models for Game Applications on AWS
About the talk
This presentation explores how game companies can leverage AWS to prepare small language models that run directly on phones and desktop computers. We’ll demonstrate practical approaches for fine-tuning open-source models for specific gaming needs, enhancing them through distillation techniques, and optimizing for edge performance. Learn how to enable function calling capabilities that let these models trigger actions within your applications. We’ll showcase an efficient AWS feedback loop that continuously improves model responses by collecting user interactions and eliminating generic “slate” answers through targeted updates. Discover how this approach delivers responsive AI experiences while enhancing privacy, reducing costs, and enabling offline functionality across your customer devices.
Takeaway
– Develop and deploy Small Language Models for Multiple Platform.
– Implementing SLM Optimizations and function calling for operational techniques and triggering in-game actions.
– How to build a Continuous Improvement feedback loop to improve the AI experience.
Experience level needed: Beginner, Intermediate
One Trillion Parameters and No Plans
About the talk
In today’s world of LLMs, is there still a need for planners? I argue yes, maybe more now than ever. This talk will first reflect on the use of GOAP and HTN planners in games over the past 20 years. Next we explore how LLMs appear to be able to plan… yet actually are fundamentally unable to plan with the attention to detail that AI characters in games require. Finally, we discuss where LLMs can add real value for authoring symbolic planning domains, and where there could be opportunities for hybrid solutions combining symbolic planning logic with generative plans in the future.
Takeaway
– Pros & cons of GOAP vs HTN planners.
– How to plan for multiple characters, actions, and dialogue all at once.
– Why LLMs are not a silver bullet replacement for planners.
– How LLMs can democratize designing content for planner – powered NPCs.
Experience level needed: Intermediate, Advanced
Heat, MaxTac, and Blockades: Expanding the Police System in Cyberpunk 2077
About the talk
Take an insider look at the police chases in Cyberpunk 2077: Phantom Liberty and dive into the dynamic spawning of road blockades and MaxTac AV encounters, which add flavor to regular police chases and are designed to keep players on the edge of their seats. Discover how the CD PROJEKT RED team leveraged Night City’s vast, vertical environment with graph-based lane discovery and asynchronous spawn points generation, ensuring seamless and engaging pursuits. With maximizing player’s fun in mind, this talk will also explore the unique technical challenges and solutions required to make these two features work.
Takeaway
Gain insights into the dynamic spawning of police chase add – ons, including road blockades and MaxTac AV encounters, as seen in Cyberpunk 2077: Phantom Liberty. Understand how to architect and implement these features effectively, balancing performance with immersive player experience.
Learn how to creatively leverage tools like:
– Asynchronous batch processing to handle complex logic efficiently without blocking the game thread.
– Graph – based traffic lane discovery for intelligent placement and movement within urban road networks.
– Physics – based overlap checks to validate spawn locations and ensure believable, non – intrusive placements.
Discover what makes a scalable and successful system for reactive world events in an open – world AAA game.
Experience level needed: Intermediate
AI Planning Analytics — From F.E.A.R (2005) to Assassin’s Creed: Shadows (2025)
About this Talk
Goal-Oriented Action Planning is 20 years old in 2025 since it was introduced in F.E.A.R in 2005. This 20th anniversary is the opportunity to visualize how planning works in a game when it is used as the decision procedure for NPC behaviors.
Takeaway
Answers to below questions:
– Why should we limit anything about the planner?
– Does the planner work more for some actions/goals than others?
– When heuristics are used, is a cheap action/goal/plan more frequent than others? Can the action/goal/plan cost influence its frequency?
– How many NPCs call the planner within a given time-frame?
– Is there a loss of control? Does planning take control of the game (instead of GD and LD) ? Could plans built by the planner be unexpected? Could surprises be planned?
– Does HTN Planning make any difference? o How Behaviour Analytics can be achieved?