Most AI systems in the games industry focus on short-term reactive memory and combat behaviors applying techniques such as behavior trees.  However, there seems to be little support for long-term memory, for example remembering locations in space that mark events that occurred.  In our daily lives, we internally map, model and remember our surroundings and experiences.  This begs the question, how can we similarly organize and leverage longer-term memory within modern AI?

This article looks into the concepts and implementation that went into creating the winning Bot Prize entry this year.  In particular, it shows how a SQL database was used for long term memories of hotspots which were analyzed to improve the AI.  It also discusses the performance and benefits of such an approach and possible future improvements.

How do you turn an idea for a game into something that's fun to play? What do you do when those ideas rely heavily on AI? How do you balance the apparent complexity that AI brings to a simulation with the desire to keep gameplay simple? Find out in this interview with veteran game developer, Borut Pfeifer, formerly a Lead AI Programmer at Electronic Arts LA, currently working on an independent game.

Borut's current project is called The Unconcerned, taking place during the riots in Iran. You play the role of two parents that have lost their child in the crowd. Learn how Borut is approaching the design and in which way he's prototyping the ideas in practice. You'll also see what kind of things he's considering for the AI.

Successfully implementing a multi-threaded system for a game requires a good understanding of the theory. But there are also many "tricks of the trade" that experienced developers use in practice. This is particularly the case for AI since it interacts with many other sub-systems...

In this panel from the Paris Game AI Conference '09, you'll learn about the practical sides of parallel programming. What can you do to build a cross platform system? How does parallelization fit with modular code? Where's a good place to get started when scaling up to multiple threads?

Participants: Julien Hamaide (Fishing Cactus), Markus Mohr (Crytek GmbH), Björn Knafla (University of Kassel).

Since Valve announced Left 4 Dead 2, the feature of the AI Director 2.0 that grabbed the most attention has to be the dynamically changing level geometry. Apparently the game can not only control the weather patterns, but it can also change the route the survivors take while moving through the world.

In this article, you'll find out exactly how the AI Director 2.0 adapts the geometry procedurally. There's also proof in the form of exclusive screenshots from the demo of L4D2, which was released to the general public a few days ago. (The screenshots are exclusive in the sense that I took them myself, and nobody else has these!)

Halo 3: ODST made numerous bold design changes compared to its predecessors, in particular its open hub-world that's populated with enemy NPCs. During the promotion of the game, Bungie released a video documentary with insights from behind the scenes. For approximately 30 seconds during the video, you can see footage of the AI technology applied to this large city. And, yes, some of the debug rendering for the waypoints seems to be pink!

In particular, during that short section, Bungie designer Alex Pfeiffer talks about the squad patrol behaviors that are used to bring life to the city. Since Halo 3: ODST is a partly open-world game, new technology was needed to make sure there's always enemy presence within the hub — which could be adjusted up and down by the designers. You can watch the AI patrolling in the highlight below...

As games become more ubiquitous, two of the most important design challenges we face as developers are difficulty adaptation and experience management. Pure, a dirt racing title from Disney Interactive, received critical acclaim by addressing both of these issues.

In this presentation from the Paris Game AI Conference 2009, you'll hear from Eduardo Jiménez about how Black Rock designed their own "race script" solution as an alternative to rubber banding that increases the amount of interactions between the riders. You'll also see how this avoids the visible effects of speed-adjustment, and finally you'll discover how this was implemented in practice, both with videos from the game and a prototype mini-game.

Of all the game modes in Left 4 Dead, Survival has some of the most interesting dynamics from an AI perspective. From the initial concept, to survive as long as you can with help of your four buddies, the game mode quickly turns into a process of finding optimal locations in the map to defend...

Obviously, when there's a medal or achievement involved, this optimization process becomes particularly creative! What makes this interesting for the Zombie AI, is that the best locations are those that the infected horde has a hard time reaching... Cue unexpected results.

INVITATION: What are the next big challenges for Zombie AI? Join resident expert Phil Carlisle and your regular host Alex Champandard in a Halloween Special this weekend. The online presentation is open for anyone signed-up (free) to AiGameDev.com and will take place live on Sunday, November 1st. Details here.

AI is only as good as the information it gets; if garbage goes in, garbage comes out. Unfortunately, acquiring good information about the physical world around an actor is not a trivial task, and calculating things like collisions and line of sight queries can take a fair bit of processing power. Providing such an interface between the low-level collision representation is typically the role of a sensory system.

In this 1h30 masterclass, you'll discover the big picture that a programmer needs to know about sensory systems — using examples from Thief: Deadly Shadows. In particular, what information do they provide for the AI to reason with, and how do they create an interface between the AI and other systems? Then, focusing on the low-level, you'll find out what it takes to build a good sensory system that can scale up and down depending on the computation power available on your target platform, and most importantly how it can manage sensory queries efficiently.

As military-style games become bigger in scope and more realistic in their simulations, there's an increasing need for using AI to help generate mission scripts automatically under the supervision of designers. Furthermore, online services can be used to extend the lifespan of games by providing procedural missions for players on either standard maps or user-generated content.

In this presentation from the Paris Game AI Conference 2009, William van der Sterren presents his most recent project involving HTN planners based on A*. You'll discover how his web-server based implementation can plan intricate coordination for missions in Armed Assault (a.k.a. ArmA 1). You'll also learn the challenges of applying HTN and A* to making sure units behave in a sensible & tactical manner. You'll also find out what it takes to make military units of many different type coordinate together to achieve objectives.

Editor's Note: This coverage from IEEE Symposium on Computational Intelligence and Games 2009 was written by Luke Dicken. In particular, his article looks at some of the key research from the conference, and discusses the relationship between industry and academia as it came across during the event.

The IEEE Symposium on Computational Intelligence and Games (CIG) is a refreshing conference to my mind because you can put any three attendees in a room and get four different takes on what the conference is about. There are the Game Theorists, talking Nash Equilibria and mixed strategies (Hingston, 2009), the Serious Games people dealing with a more simulated what-if approach to the world — in scenarios such as Disaster Relief or Naval Strategy (Avery, Louis, & Avery, 2009). There are researchers working on a whole range of more classical board and card games such as Poker, Diplomacy or Risk (Kemmerling et al., 2009). And of course, there are the Video Game researchers — but even within this group there are further classifications to be made between those using games as a controlled environment in which to develop better AI techniques (Thompson & Levine, 2009), those using AI to push forward the use of technology in contemporary games (Galli, Loiacono, & Lanzi, 2009), and those using the marriage of advanced AI methodology with video games for some other end, such as behavioural modelling of players (Drachen, Canossa, & Yannakakis, 2009) or automated content creation (Tanimoto, Robison, & Fan, 2009).

Even within our own specific niche, our community remains very diverse. Not only diverse but also of a very high quality — it's hard to single out specific pieces of research to point to as being worth mentioning, because it almost certainly means excluding something else that deserves to be here. You can see the full proceedings here to make sure you don't miss anything. (Also see the bottom of this article for the full references from the previous paragraph!)

Your archers are engaging enemy cavalry from a distance using their ranged attack. The opposing forces harass you, the archers get a bit too complacent and move away from the protection of your infantry. Seeing the opportunity, the enemy cavalry charges suddenly! How do you make sure your archers or crossbowmen switch to their hand weapon to try to limit the damage?

Such problems come up very frequently in game AI. What do you need to do to make sure the AI can deal with changing its mind, for instance to switch between an attack that makes sense in one situation (e.g. ranged attack) vs. another attack mode that needs to be used otherwise (e.g. close combat)? How should you structure your behaviors to support such situations? This masterclass using examples from the indie game Mount & Blade to explain the solutions.

As more games strive for replayability and unique experiences, AI becomes increasingly important to help synthesize the gameplay and adapt to what the players are doing. This can help significantly increase the lifespan of single player games or story-based games, even if they are mostly linear like Left 4 Dead. With large open sandbox worlds, technologies like the AI Director become even more important to help make the experience a little less mediocre during the worst moments, since emergence is rarely good enough on its own!

This in-depth feature article looks into Left 4 Dead's procedural gameplay systems and the famous AI Director that helped Valve solve such problems, and secure the "Best Game AI of the Year" in the AiGameDev.com 2008 Awards. You'll learn how the zombie population is generated using a layered approach that's similar to Perlin noise, and how the results are adapted to what the players are doing. Dig into the a simple four-state system monitors and controls the whole experience, and discover what tricks Valve used to balance and tune the results.