Modern AAA video games feature huge game levels and maps which are increasingly hard for level testers to cover exhaustively. As a result, games often ship with catastrophic bugs such as the player falling through the floor or being stuck in walls. We propose an approach specifically targeted at reachability bugs in simulated 3D environments based on the powerful exploration algorithm, Go-Explore, which saves unique checkpoints across the map and then identifies promising ones to explore from. We show that when coupled with simple heuristics derived from the game's navigation mesh, Go-Explore finds challenging bugs and comprehensively explores complex environments without the need for human demonstration or knowledge of the game dynamics. Go-Explore vastly outperforms more complicated baselines including reinforcement learning with intrinsic curiosity in both covering the navigation mesh and number of unique positions across the map discovered. Finally, due to our use of parallel agents, our algorithm can fully cover a vast 1.5km x 1.5km game world within 10 hours on a single machine making it extremely promising for continuous testing suites.

AI and Games is a crowdfunded series about research and applications of artificial intelligence in video games. If you like my work please consider supporting the show over on Patreon for early-access and behind-the-scenes updates. 'The AI of Sea of Thieves' is released in association with the UKIE's '30 Years of Play' programme: celebrating the past, present and future of the UK interactive entertainment industry. Welcome to part 3 of the AI of Sea of Thieves here on AI and Games. In parts 1 & 2 I looked at how Rare's online pirate game balances the AI systems at play across each server plus how skeleton and shark AI are built to keep players on their toes both on land and in the water.

AI and Games is a crowdfunded series about research and applications of artificial intelligence in video games. If you like my work please consider supporting the show over on Patreon for early-access and behind-the-scenes updates. In part 1 of my case study on Horizon Zero Dawn - Guerilla Games 2017 Playstation exclusive - I explored how the game is built to create herds of AI-controlled machine animals. This requires a complex agent hierarchy system where each machine can make decisions about how to behave using a Hierarchical Task Network planner, but also groups agents together to dictate their roles and responsibilities as part of a herd. This is all part of a system known as'The Collective', which maintains the ecosystem of all machines in the world as you are playing it.

All chapters are available to download as of Sept 2015.

This work presents an algorithm to provide a better represen- tation of space to artificially intelligent characters (i.e., agents or bots) in game and simulation environments by providing a more accurate breakdown of the traversable space present in the game environment. Such representations are generally constructed by decomposing the walkable space present in a game environment into a series of convex regions to form a data structure called a navigation mesh. We extend the basic concept of a navigation mesh by the introduction of an understanding of the textures that are attached to the underlying geometry creating what we refer to as a texture-aware navigation mesh. This does result in a more complex navigation mesh (more regions and a larger search space). However, since the textures of walkable geometry can be used to determine the appropriate traversal method for that terrain, a game character can determine valid paths for their traversal methods using just the navigation mesh (e.g., characters in cars can generate paths containing just roads or walking characters can create paths containing just sidewalks). We also present a use case that shows how such a system of texture aware naviga- tion meshes might benefit character path planning and search in virtual environments. In this use case, we examine a Real Time Strategy game style game environment, which shows it is possible to generate a navigation mesh such that each region is composed of a single terrain type.

We present a modified navigation mesh generation algorithm that allows the mesh to be dynamically altered at runtime. We accomplish this using an extension to the existing spatial decomposition algorithm ASFV (Adaptive Space Filling Volumes) that will allow the algorithm to dynamically adapt to changes to the underlying world geometry without having to rebuild the entire spatial decomposition. This is accomplished by providing two algorithms to deal with alterations to the world. The ability is provided to add arbitrary obstructions into what was negative space and then to build a new correct spatial decomposition around the new obstruction. Functionality is also provided to remove existing obstructions and then to build up new decompositions to fill in the newly created negative space. Finally, we show via an experiment that our dynamic extensions to ASFV reduces the cost of correcting an invalidated decomposition by 90% or more.