Artificial Intelligence in RTS/War/Strategy games roundtable (Friday)

Hierarchy! (many) levels of entities; important to address communication between levels; resolve conflicting desires from above and self-preservation.
Mommentum to keep working on a goal instead of keep switching.
Command level can be in a different thread; pathfinding in a third thread (handled > 1000 units in "Enemy Nations").
In Moo3/turn-based game: randomized decisions with weighting of choices; current goal is given a big bonus.
Add cost of abandoning current choice to cost of all other choices. However distance factors are relevant: if going towards point A with some forces, may not cost much to switch target to B if B is close to A.
Have to weight distance, time, cost, etc. Importance of each factor changes with time.

Ex: sports game, must satisfy many constraints in addition to trying to further several goals.
State machines are a poor fit to this situation, but fuzzy state machines address the problem.
In Moo3, many factors go into whether to colonize (quality of planet but also strategic position), but here ship only has a binary decision
Alternatively, can use a complicated state selection method based on analysis of desires.
For hierarchy/fuzzy AI see Game Programming Gems 2.

Moo3: What enemy forces do we know about? How quickly can they arrive? Are they headed this way? How advanced technologically are they?
Can use influence maps: terrain modifies the effect, can add up several influence maps to achieve a final score.
Game programming gem on using influence maps to find fronts.
Compute how many units are in range.
Connectivity affects whether geography important, so influence maps may not apply (if you can get from any point to any other in a turn for example).

People use A*, Dijkstra , Hierarchical A*, cheap methods
Dijkstra said to be good for non-Euclidean search spaces, but another claims that you should use the minimum of outgoing edges with A* instead.
Cheap methods (follow left wall) used to get someone out of the way of a car in a hurry.
Steering: Problem with steering is you can't find a small path through a dense set of obstacles.
Steering: Good for moving a group together towards a target.
Steering: Look up Robin Greene and Craig Reynolds (red3d.com) on the web
Adaptive force field: what I would normally do + (some weight times) learned behavior (from what has failed in the past).
Enemy nations: 1 thread per CPU opponent, +1 for pathfinding, +1 for game itself
Another model uses one thread for control, one for pathfinding, one for game.

Look up "Operation planning" -- planning is not that exotic. Chess techniques apply
AI Wisdom 2 has an article on insect AI
NN and GA have a place before shipping: GAs at least are easy to code.

MOO3 does not use a cheating AI (unless accidentally). Seeing fewer units means faster processing! Cheating creates player frustration.
For knowledge that gets out of date (I saw a large group of forces at x,y) should keep a certainty that goes down with time
Moo3 does not estimate forces it can't see.
"Rely on the player's imagination -- use simple techniques"
"Create the illusion of intelligence"
"Players want to feel bad-ass"
Gamedev.net has a forum & links; Gameai.com is a gateway to other resources
"Robocup": team play for soccer