The macro for automata enables a programmer to ignore this distinction, while still leaving both implementations a possibility.
This enables client programmers to choose the algorithm and the type of automata they wish to use.
It can be exactly the same as with deterministic finite automata if all possible percepts can be collected.
The resulting graphs may be employed in various ways, for example as neural networks, as automata, or as knowledge-base queries.
The operational semantics is given in terms of probabilistic automata.
We give the language an operational semantics in terms of a simplified version of probabilistic automata.
Extended patterns such as (2) are stripped of actions and compiled into automata whose union yields level recognizers, just as before.
One natural implementation of this language of automata is to create a vector or other random-access data structure to represent the states.
