Chart (left) shows more projects now include a full-time AI developer. The Sims (right) are an example of A-Life.
Among the trends that Woodcock discusses are:
- An increase of dedicated AI coders on a commercial game project (see chart above).
- A-Life. “The power of A-Life techniques stems from its roots in the study of real-world living organisms. A-Life seeks to emulate that behavior through a variety of methods that can use hard-coded rules, genetic algorithms, flocking algorithms, and so on.
- Formations; both military and sports. According to Woodcock, developers are using Finite and Fuzzy State Machines for implementation of formations. Also, “hierarchal AI”, “Interplay's Starfleet Command and Red Storm's Force 21 take such an approach, using higher-level strategic "admirals" or "generals" to issue general movement and attack orders to tactical groups of units under their command.”
- Visibility Graphs. “They work as follows: Assume you are looking down at a map that has a hill in the center and a pasture with clumps of trees all around it. Let appropriately shaped polygons represent the hill and the trees. The visibility graph for this scene uses the vertices of the polygons for the vertices in the graph, and builds the edges of the graph between the vertices wherever there is a clear (unobstructed) path between the corresponding polygon vertices. The weight of each connecting line equals the distance between the two corresponding polygon vertices. This gives you a simplified map against which you can run a pathfinding algorithm to traverse the map while avoiding the obstacles.”
Pottinger (who is closely involved with Ensemble Studios) in part 2 discusses many of the same trends as Woodcock in part 1 with a special emphasis on Hierarchal AI. The Hierarchal AI that is being discussed appears to be a much simpler version (two or three level) of the four-level AI I designed for UMS II: Nations at War (1992).
John Laird (of the University of Michigan) writes in Bridging the Gap Between Developers & Researchers, “When game developers look at AI research, they find little work on the problems that interest them, such as nontrivial pathfinding, simple resource management and strategic decision-making, bot control, behavior-scripting languages, and variable levels of skill and personality -- all using minimal processing and memory resources. Game developers are looking for example "gems": AI code that they can use or adapt to their specific problems. Unfortunately, most AI research systems are big hunks of code that require a significant investment of time to understand and use effectively.”
Laird concludes, “Although there is currently a significant gap between game developers and AI researchers, that gap is starting to close. The inevitable march of Moore's law is starting to free up significant processing power for AI, especially with the advent of graphics cards that move the graphics processing off the CPU. The added CPU power will make more complex game AI possible. …A second, equally powerful force that is closing the gap is sociological. Students who grew up loving computer games are getting advanced degrees in AI. This has the dual effect of bringing game research to universities and university research to game companies— already there are at least five AI Ph.D.s at game companies.”
Pottinger’s 2003 Article in Computer Game Developer explains in great detail the methods behind the AI in Ensemble’s Age of Mythology. The system is based on a Knowledge Base (KB). The KB keeps track of all enemy units encountered as well as enemy buildings and structures (a key part to the game).
Above the KB are “AI Plans” — or “complex state machines that know how to do tasks such as building structures, attacking a variety of target types, gathering and the like.” Above the AI Plans are “AI Goals” that “are super-high-level constructs such as ‘Attack Player 4’ of ‘Build a forward base in Player 3’s direction’.”
