A Star

Most situations in commercial games have very large problem spaces, so it would take a long time to list all the possible AI behaviors… In order for planners to solve such problems in real-time, they must effectively discard as many useless options as possible.
Generally speaking, you can approach the implementation of such planners using a […]

This article was contributed by Gabriel Ware, who works at Pam Development (2K Paris) as a PS3 programmer.

Planners are among the most popular algorithms used in the video game industry, as they solve a broad range of problems ranging from path-finding to action planning. Even though they have their pitfalls, developers regularly use a […]

This demo was contributed by Justin Gilbert, game programming guru at Multimedia Games and Vanguard Games.

This applet was written in Python and compiled with Jython 2.2.1 for using Java 1.5.0.13. Make sure you have a recent version of the JVM installed. The file is 935 KB big, mostly code but including a few […]

This article looks into the latest SDK behind F.E.A.R., the hit first-person shooter acclaimed for its artificial intelligence. A majority of the AI code is available as part of the game source, and notably its famous STRIPS planner written by Jeff Orkin.

This Saturday in the build-up to the festive shopping season, there’s an avalanche of interesting Smart Links about game development and artificial intelligence! Feel free to contact me if you have any news or tips for next week.
Remember there’s a mini-blog (RSS) which gets updated with game AI news from the web as […]

F.E.A.R. advances the state-of-the-art of first-person shooters (FPS); not only is it highly entertaining, but it has been praised for its innovations in artificial intelligence. This article looks at the planning technology in detail, the design behind it, and how the behaviors are pieced together at runtime.

This review explains one of the more recent innovations in path-finding to deal with greater numbers of actors and larger dynamic worlds. In particular, HP-A* (pronounced “A Star”) is capable of reducing memory usage and the cost of searching for an optimal path by automatically generating a hierarchical representation of the navigation grid.

Let’s say your FPS bot needs to go to multiple places to pickup ammo, a weapon, and a health pack. How do you make sure the pathfinding takes into account the location of each object to provide the best overall path, rather than wander around picking up each object individually?