| This chapter described the various aspects of solutions in general, starting with an information theory study: The solution is a mapping from inputs to outputs, within which there are many types of patterns. Compressing this data leaves us with essential information that indicates the complexity of the mapping. There is a trade-off between finding patterns to approximate data and missing out on information with oversimplifications. We can measure the quality of a representation or instance using the MDL principle, which provides a trade-off between precision and generalization. Although algorithms use these principles, understanding these concepts helps AI developers find better solutions.
Then, the concepts behind the representation were analyzed in more depth: The solution's representation is often different from the representation of the problem. The representation indirectly determines the size of the search space. It's relatively easy to compute the total number of configurations by multiplying the size of internal variables together. The representation chosen has an effect on the smoothness of the search space. We can control the properties of the search space by tweaking parameters of the representation, or even selecting the right representation.
The simulation of the solution (that is, computing the result) depends on the representation. By contrast, there are some common concepts behind the training mechanisms: Human experts can intervene at various levels to provide guidance to find the solution. Other native approaches include exhaustive search and random scanning of the search space. Combinations of these different approaches are very common, if not essential, for any solution to work effectively.
The concepts in this chapter tend to come in handy while applying AI techniques to new problems. Although the ideas may seem somewhat abstract, they provide pertinent insights that guide the design of solutions. The only way to get comfortable with this process is to practice, practice, practice! Practical Demo Guinea is an animat that has modular capabilities for movement and shooting. These are used to collect all the solution data, ready for analysis. By examining the mappings for each of the capabilities, the best AI technique can be chosen. Guinea provides the necessary facilities to apply ideas from information theory, and can be found online at http://AiGameDev.com/. |
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