Representational Learning

              A simple example of AI [artificial intelligence]. In this particular case, we present a simple AI based on Valve's "Half-Life" engine. This is actually a MOD [modification for a game] that uses another utility called HLB_bot, which is an AI of a character in the game. We use the character to demonstrate a model of navigating through space. It coordinates itself placing waypoint marks on the way, every 200 units away from the previous waypoint. [a specific parameter measure of the engine] In addition the BOT is able to perform random rotational moves, so change of trajectory could be possible. The algorithm behind the BOT's logic is extremely simple and limits the BOT to making rather stupid move choices sometimes. When running the simulation for quite a while you can see the progression of the BOT, enabling it to build up a network of connected nodes, or waypoints. Using these waypoints the BOT can navigate throughout the space. After an inordinate amount of time we can assume that the BOT will be able to cover the whole map, without getting stuck in any dead ends. We can see the storing mechanism graphically in real time via the placement of these waypoints and the network they create. The waypoints are represented by blue rods and the white lines between them represent the shortest path calculation from one point to the next. The result is a self-learning mechanism created by the BOT's ability to store environmental data and then use this data for navigation. The resulting web of lines is a graphical representation of what space the BOT knows to exist. In short the BOTs are mapping out their surroundings. All the processes are happening in real time in an OpenGL graphics environment.

- John Bruneau and Michael Chernobrod