Showing: 11 - 20 of 29 RESULTS
Build Your Own Navigation Stack!
About the talk
Recast’s out-of-the-box navigation features are a good baseline and suitable for many games. However, most companies need to make significant modifications to make Recast’s navigation solution work for their game. While these modifications might work for one project, it might not be suitable for the next. In this talk we showcase how we built an extendable and reusable custom navigation stack that leverages Recast.We explore how to utilise Recast as part of a larger framework by breaking the generation process into individual plugins that form a tile generation pipeline. Each plugin lists the plugins it is dependent on, which forms an ordering. More importantly, this allows us to add additional plugin extensions leveraging the data that Recast produces.Using this framework, we show how it can be extended to implement features like hierarchical pathfinding, multimodal navigation (e.g. traversing multiple navigation meshes), and how to synchronise custom data. This naturally extends to custom extensions that automatically generate navigation links and cover locations.We showcase how this framework can combine multiple navigation meshes into a single system that constructs a unified navigation stack that works for all actor types. Furthermore, we show how it can encode additional constraints such as wading depth and affect pathfinding to avoid areas that are in line of sight of enemies.We showcase our framework in Unreal Engine and demonstrate that this custom navigation solution vastly outperforms Unreal in time, memory, and quality.
Takeaway
– How to build a reusable navigation system;
– The benefits of building a custom navigation stack
– The merits of building a navigation system with extensibility as a first-class citizen
Experience level needed: Beginner
Press A for Assistance: Making Games Accessible with AI
About the talk
As video games continue to evolve in complexity and scope, accessibility, approachability, and learnability are no longer niche considerations—they’re essential. In this talk, we will explore the differences between accessibility, approachability, and learnability, as well as the overlap between them. We will discuss the difficulties in developing accessible, approachable, and learnable games, why the industry lacks clear benchmarks, and the best resources and solutions for game developers. We’ll dive into some examples of modern games, the challenges they pose to players, and the current assistive techniques used in industry. Then, we’ll see how various AI techniques (including Utility AI, Goal-Oriented Action Planning, and Large Language Models) can dynamically assist players without compromising gameplay. This presentation will not only explain the concepts of accessibility, approachability, and learnability, but encourage creative thinking about how emerging technologies can make games more inclusive, intuitive, and fun for everyone.
Takeaway
– What accessibility, approachability, and learnability are, and how they intersect.
– How putting careful thought and consideration into accessibility, approachability, and learnability benefits the community, game, and studio.
– Why there is a lack of industry standards and research in academia.
– The limitations of current strategies/implementations of accessibility, approachability, and learnability, both in terms of assistance, and difficulties of development.
– How we can implement traditional AI – into games to increase accessibility, approachability, and learnability in games.
Experience level needed: Beginner, Intermediate
From Metrics to Meaning: Effort-Aware and Explainable AI for Game Pathfinding
About the talk
This talk presents a practical, explainable framework that bridges the gap between raw AI pathfinding metrics and human-centered game design. Using Sokoban as a case study, we show how unsupervised clustering and visual analytics can decode difficulty, uncover hidden structural archetypes, and guide adaptive solver selection. Our results demonstrate how obstacle density, deadlock potential, and other features drive both algorithmic and human difficulty, enabling designers to make faster, more informed decisions about procedural content.
Takeaway
– How to translate opaque AI solver metrics into designer – relevant difficulty insights.
– Evidence – based design heuristics: why obstacle density and deadlock potential are critical levers in puzzle complexity.
– How clustering reveals hidden level archetypes and balances procedural content.
– Practical workflow for integrating explainable AI tools into game design pipelines.
– The value of adaptive solver selection for efficiency and player experience.
Experience level needed: Intermediate
No API. No Problem: Deploying tiny, fast, fine-tuned models offline.
About the talk
Game developers, especially those working on single-player or mobile titles, are often reluctant to integrate online APIs due to latency, cost, instability or platform constraints. Yet, the promise of generative AI remains strong, provided it can be fast, private, cheap, and seamlessly embedded into game experiences.
This talk explores a production-ready approach to deploying small, fine-tuned, dynamic language models inside games — without needing any internet connectivity. While many LLM-based use cases in games focus on low-hanging fruit like quest dialogue, we want users to take control of any text. We strive to bridge the gap between loose ideas or raw knowledge bases and structured data pipelines — the foundation for generating what we call Diamonds. Ultimately, you’ll understand how to generate reliable results with dramatically reduced memory usage and power consumption, even on the smallest of devices.
Takeaway
– Examine the operational and UX challenges of API – bound systems in videogames and how offline language models unlock new possibilities
– How is it that strategic templating governing input/output behaviours can be paired with strong alignment to your domain for reliable, adaptable outputs in a compact package
– Performance wins of embedded language models: tiny, fast, green and offline a. Side – by – side results comparing models across loading time, RAM use, etc.
– Lessons from building a toolchain for developers: what has and hasn’t worked
– What comes next: a peek into our ongoing R&D and Dynamic AI Platform features.
Experience level needed: Intermediate, Advanced