Early AI research emphasized the optimization of search algorithms. This approach made a lot of sense because many AI tasks can be solved by effectively by defining state spaces and using search algorithms to define and explore search trees in this state space. Search programs were frequently made tractable by using heuristics to limit areas of search in these search trees. This use of heuristics converts intractable problems to solvable problems by compromising the quality of solutions; this trade off of less computational complexity for less than optimal solutions has become a standard design pattern for AI programming. We will see in this chapter that we trade off memory for faster computation time and better results; often, by storing extra data we can make search time faster, and make future searches in the same search space even more efficient.
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Real-time strategy games are as close as most of us will ever get to assuming the role of a military commander. In RTS games, the player must form and carry out long-term strategies, just like in a game of chess. However, in RTS games, the player must also be able to quickly adapt his strategies to a very dynamic environment. The player does not always have a perfect picture of what is happening in the game world (unlike in a game of chess).
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