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SummaryHFSMs improve upon standard state machines by adding the ability to cluster state, to express general transitions, to refine states that are to complex to handle with one procedure, and even to express independent components. In practice, this can be achieved using different concepts:
To implement the system, it's necessary to define the interaction semantics. This specifies how control is passed from one state to another:
HFSMs are an incredibly powerful concept, ideally suited to game AI development. They extend finite-state machines—one of the most popular game AI techniques—and make them easier to design. Because pure finite-state machines are not appropriate for every problem, the concept of heterogeneous hierarchies (or AI architectures) can be used interchangeably. The next chapter uses the concept of a hierarchy along with extensions to finite-state machines to create realistic emotions in animats.
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