The Paris Game AI Conference 2009 took place earlier this month on June 10th and 11th, co-organised by AiGameDev.com and the CNAM where it was held. In total, almost 200 people from Europe and beyond attended this two day standalone event — with around 170 attendees per session on average. The event was based on last year's innovative workshop that we held for the first time after another conference in Paris, although this year was an complete upgrade in every way: the presenters, content and location were better in all aspects!

The whole event was made possible thanks to Stephane Natkin, director of the CEDRIC research lab, and Axel Buendia, CEO of the middleware company Spir.Ops. The rest of the organisational committee was pretty much only myself (Alex Champandard) and Petra (Mrs AiGameDev.com) — which explains the last few quiet weeks as we recover from the hectic months of organisation...

In the following post, you'll find the original slides for the presentations, my personal takeaway and executive summary from each session, and some references as appropriate. The recordings will take time to prepare edit, but all audio and video will be published over the next four months in the AiGameDev.com Premium area. (Sign-up here if you'd like to see this again next year!)

The coffee breaks on Wednesday were sponsored by Havok. We'd like to thank Havok for their continuing support of AiGameDev.com. Their company has offices in Dublin and San Francisco, and recently released their Havok AI™ solution to complement the existing packages like Havok Behavior™. For details, don't hesitate to contact them!

The game AI & navigation middleware industry is moving very fast these days; even we're struggling to keep up! Today, PathEngine released its latest SDK and here at AiGameDev.com we've been sinking our teeth into it to check out the latest developments. In this article, you'll find a behind the scenes description of what's inside the release and how the features are implemented. On top of that, you can find the demo binary and the source code for samples within the "testbed" release available from their download page.

The key features of this released, discussed in this page, are the collapsible formations (including a video), support for both automatically generating navigation meshes based on BSP-geometry processing or voxelization (with a comparison), and finally support for run-time off mesh connections to allow agents to jump between arbitrary points in the world.

NOTE: Thanks to Thomas Young for giving us access to a pre-release build of the latest SDK, and for letting us post some exclusive details about the technology. PathEngine is a sponsor of AiGameDev.com.

This masterclass ties together all the sessions on design patterns to show you how to build a complete behavior tree for enemy NPCs in action / combat games. You'll learn how to assemble a set of relaxed, suspicious and alert behaviors into three subtrees. At each stage, you'll find out what kinds of decisions should be made, how the tree should be structured, and what you need to do to make sure the whole behavior is responsive to changes in the environment.

The eighth release of the Sandbox brings a host of new technology and systems together, including components within the AI actors, whole modules of the MVC framework and even additional external libraries. The result is a mini-simulation that includes a race on-foot between multiple characters, each avoiding boxes and locomoting between markers in the world. This ties together game logic, simple character AI and the motion planning based on the terrain representation.

One approach to machine learning that's getting more attention these days is learning by example (also known as imitation). Not only are there more academic papers on the subject, but parts of industry and middleware companies are also turning towards this kind of ML for answers. Will Wright recently said that the "Holy Grail is crowd-sourcing the algorithms, AI, and procedures in the game" and Jeff Orkin retired from the comfort of his AI job at Monolith to research data-mining gameplay sessions.

Imitation learning is appealing because it puts more control into the hands of the designers, by considering them as trainers who provide example behaviors for a system to learn from. Compared with other solutions, the AI can make fewer assumptions about the kind of results desired, so because there's less room for error it seems to be a good fit for the games industry.

Going beyond academic prototypes, there aren't many developers applying these ideas into commercial games — with the notable exception of TruSoft, makers of behavior capture middleware (and sponsors of AiGameDev.com). What follows in the article below are video highlights from their Artificial Contender solution (see this page for more), and two blog posts that I previously wrote about the system, along with my most recent thoughts.

NOTE: This weekend we'll be running an public session about behavior capture with Iskander Umarov, Technical Director on Artificial Contender (a behavior capture middleware), going over the general principles of learning by example, comparing the different solutions available as well as related technology, and discussing how it can be applied to a variety of games ranging from sports to combat training.

The online presentation this weekend and this article are technically sponsored by TruSoft. However, the open session will cover the behavior capture technology and its applications in general. I have to admit I spent 1h43 talking to Iskander in the pre-interview not only because it's a fascinating topic, but because he's willing to answer all questions on the subject!

Continuing the series of masterclasses about behavior tree design, learn how to build modular building blocks for plugging into a more complex tree, at what level you should model behaviors, and the important things to consider when building a goal-directed AI using a behavior tree. Find out what's a good granularity to help your designers interact with purposeful behaviors, and where you can start programming to reduce the lines of code required.

Over the past couple years, Mikko Mononen has been working on a personal R&D project for automatically generating navigation meshes based on a polygon soup. His project was born out of a need for better data for pathfinding; navigation meshes can never be good enough! Previously, Mikko worked on Crysis as a Lead AI Programmer and recently started in the same role at Recoil Games, although he has a demomaking background and experience with independent games too.

Mikko experimented with a variety of different techniques based on rasterization and voxelization, which treat space as a discrete grid and post-process these voxels to retrieve polygons on the output. This approach is one of the most promising for generating robust and reliable navigation meshes, and research projects and commercial solutions are increasingly relying on this approach too. (See the references below in the article.)

In the following 11:30 minute HD video, you'll get a guided tour of the navigation generation (known as Recast) as well as a demo of the runtime pathfinding and navigation queries (called Detour). You'll get an idea of how this kind of technology works, as well as a demonstration of how it works on a variety of indoor and outdoor levels.

In this interview with independent developer Andy Schatz, you'll learn about the A-Life behind his series of games including Venture Africa and Venture Arctic, and the upcoming Venture Dinosauria. Find out how the game's AI was designed, how the creature behaviors were balanced, and how the predator / prey simulation was inspired by the behavior in nature. Andy also talks about opportunities for independent developers looking into A-Life games.

Over the last 8-10 months we've been incrementally building up The AI Sandbox an part of AiGameDev.com Premium. It's built on top of an open-source stack, but with key parts built from scratch (e.g. the MVC framework) or heavily customized (e.g. animation system, test framework).

We designed and approach most components based on practices used in industry, but also prototyping various ideas we feel have lots of promise. In the process, we've encountered lots of important lessons — some old and some new.

First, I'd like to extend a warm welcome to new readers who just discovered the site via recent features and subscribed to the feed. (You did find the RSS didn't you? :) There are even more cool articles already lined up for the next few days as we go into launch week...

Unless you've been stuck in the same finite state for the last month, you probably know we're re-opening the AiGameDev.com Premium for new Members in about 12h. You've also no doubt noticed all the extra content on the site since we relaunched — possibly even wondering who put those red crowns everywhere, and what's hidden behind those pages.

Here's the answer, watch the video below and find out everything now. It's the smartest move!

In this tutorial written by Daniel Harabor, you'll find out how to deal with different sized units when pathfinding on a grid, for example in a typical real-time strategy (RTS) game with tanks and foot soldiers. Learn how you can deal with this problem elegantly by using the concept of clearance at each point in the map. Daniel also presents his HAA* algorithm, which helps deal with clearance in a hierarchical manner such that the search remains both efficient and accurate.

The AI Summit earlier this year featured a discussion panel about the tensions between design and programming — and particularly AI. Since AI is such an integral part of gameplay, innovation on the technical front must be matched in equal measures in design and methodology. This session looked into prototyping workflows, methodology and interaction, as well as tips and tricks from experienced designers and coders.

Participants: Alex Hutchinson, Soren Johnson (moderator), Joshua Mosqueira, Adam Russell, and Tara Teich.