I have worked a little towards off-mesh links (a.k.a jump-links). That is, extra annotation other than polygons which act as links in the navigation graph. My long term plan is to support point-to-point and segment-to-segment off-mesh links. The first iteration will only bring you the point-to-point links.

This is a guest post from Phil Carlisle, best known for his work on the Worms series. He appears from time to time in Wolfire IRC and I finally coaxed him into writing a guest blog post! He will introduce himself below.

After a small hiatus, I’ve been grinding off my to-do list. The first item was to finish the local navigation proto.

Part 2: Perceptions and Path Finding

Perception equals reality. And in games, the more that can be perceived, the more realistic the experience is… both for the player and for the game entities.

This article discusses the challenges an AI faces in perceiving and navigating a complex environment, and how those challenges can be addressed. Included are examinations of using ray tracing to emulate sight, and how your AI can perceive temporary entities (such as bullet holes, pools of blood and footprints). It then discusses how your AI can navigate the environment, including the use of multi-threading for crashing, turning, and remembering. Includes code samples.

Part 1: Design and Implementation

Raising the bar of software development has also raised consumers’ expectations. Gamers expect to see each new game become more complex and intelligent, challenging developers to push the envelope. Computer-controlled AI has evolved to accomplish this mammoth task.

In this article, get an architectural overview of the new API for the Intel Media SDK and how it can be used to incorporate AI into game design. Includes the various types of AI systems; the roles AI can play in games; and decision making in simple rules-based systems, finite state machines (FMS), and adaptive AI, which attempts to predict behavior and is key in fighting and strategy games.

Today’s experiment was to solve of unfortunate side effect of the sampling method I chose—namely initial collision. If two agents are overlapping, the sampling will always return blocked and the two agent will get stuck. I previously side stepped this problem by just ignoring the initial collision.

XNA game in C# for a Game AI course at PUC (Rio de Janeiro). A small clip of the game is at: http://www.youtube.com/watch?v=YZMxgLtnzvc. It’s a 2D game that uses simple the concept of Navigation Mesh with AStar for pathfinding. Steering behaviors were also implemented. The game is based on the starter kits and forum from the XNA’s Creator Club.

Xaviant, the young development studio based in the US city of Atlanta, is using a number of tools from AI group Xaitment for its debut project.

After fiddling around with the different methods to implement velocity obstacles (especially the directive circle style approaches), I noticed that there are two parameters which can be used to tune to get out of the cases where the agent is completely surrounded by the velocity obstacle. The parameters are: desired movement speed and time horizon. Time horizon means the latest potential collision we are interested in avoiding (or reversely, how early something should be avoided).