Here's a list of the eighteen best, quirkiest, and most informative artificial intelligence bugs available on YouTube we've collected over the past 12 months. In the spirit of AiGameDev.com, you'll find a bunch of tips & tricks to help fix these problems when/if you see them in your own game.

First, a disclaimer. While certainly fun, this is also a difficult feature for me to write; the draft has been waiting to be finished up and published for over 6 months. There are already many video collections of AI bugs on also-ran Game News sites, but the difference here is that I've personally met or worked online with most of the developers behind these games — and many of them read this blog (RSS). I'm hoping the controversial aspect of this article is overshadowed by the useful analysis and the importance of the topic.

The fact is, if you look beyond the sensationalist headline, social media websites are dramatically changing the way games are received by gamers and the community at large. And AI in games is no exception here... In fact, there seems to be increasingly more pressure and scrutiny on the behavior of game characters. Luckily, this new area of social media means there's also lots to learn from!

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Welcome to the AiGameDev.com list of the most influential AI games of all time! You’ll find video and computer games that have used artificial intelligence in innovative ways to critical acclaim, and some of them that have managed to integrate cutting-edge AI technology too…

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Editor’s note: This in-depth article was submitted by Johan Hagelbäck, lecturer and Ph.D. researcher in adaptive game AI. If you’d like to join Johan and myself (Alex J. Champandard) for a live online masterclass (public) on Wednesday, February 4th at 21:00 CET Europe, 15:00 East Coast, then go to live.aigamedev.com for more details. You’ll learn about the key concepts in this tutorial, but also learn how potential fields fit in with high-level AI strategy, dynamic navigation in crowds, and optimizing traditional pathfinding. (These sessions are normally for members only, so don’t miss it!)

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While AI is playing an increasingly important role in the games industry these days, games entirely based on AI are few and far between. One in particular though, is Neuro-Evolving Robotic Operatives (NERO) which was released in 2005 by Kenneth Stanley and his team at the University of Texas at Austin, and totaled over 100,000 downloads. NERO involved evolving AI bots navigating and fighting in virtual battle grounds.

In this exclusive interview, AiGameDev.com caught up with Ken Stanley to find out about his next project, which is also based on neural networks and genetic algorithms, and a technique called Neuro-Evolution of Augmented Topologies, a.k.a. NEAT, which we covered previously on the site.

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In this presentation, Alex Champandard talks about using behavior trees to create responsive yet purposeful decision-making and control systems. In particular, you'll learn how behavior trees combine the best features of linear scripts and reactive state machines, yet provide the goal-directedness of planners. You'll learn about the structure of behavior trees and what you need to implement them, as well as useful patterns for designing scalable AI logic based on them.
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Whilst catching up with recent discussions about game AI on the web, I stumbled on this thread about cover. Implementing good combat behaviors using cover isn’t always straightforward, but game developers often use the same tricks to solve this problem…

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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.

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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.

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Halo caused a bit of a controversy here on AiGameDev.com when it ranked so highly in the Top 10 Most Influential AI Games. But after reading through the record 42 tips in this technical review, you’ll quickly realize how much influence Bungie has over the industry by sharing insights into its development process.

Taking a cue from the presentations at the Game Developer’s Conference from Jaime Griesemer and Chris Butcher in 2002 (Halo’s designers), then Damian Isla talk in 2005 (Halo’s lead AI programmer), this article looks into the artificial intelligence technology behind the best-selling franchise — what to reuse for your game as well as what to avoid.

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One topic that's particularly hot in computer animation these days is motion planning. Think of it as one way to combine pathfinding with animation — which falls under the label of locomotion. Motion planning distinguishes itself by taking a more global approach to generating animation, such that the results are more purposeful and higher quality. Many game developers are looking in this direction for solutions that are better looking and have lower production costs.

In my animation lecture at the AI Summit, I gave a comprehensive introduction and review of all the different ways to create a locomotion system. The most enlightening for me was hearing feedback form people agreeing or disagreeing with particular solutions. On the animation front, no topic was more debated than the idea of motion planning during those few days, so I thought the topic deserves its own article to go through the major benefits and objections.

As a teaser, here's an example we've been working on as part of the AiGameDev.com Sandbox, and that I showed at the GDC last month that. This prototype uses A* as planner that drives a step-based motion model, so the system is searching through a set of foot plants to reach a goal.

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