The transition function is a fundamental concept in automata theory, particularly within the study of finite automata (FAs). It is the set of rules that determines how an automaton moves from one state to another in response to an input symbol.
The transition function, often denoted by $\delta$, is formally defined as a function that takes the current state and an input symbol and returns the next state. For a deterministic finite automaton (DFA), this mapping is unique:
$\delta: Q \times \Sigma \rightarrow Q$
Where:
In a non-deterministic finite automaton (NFA), the transition function can map to a set of states:
$\delta: Q \times \Sigma \rightarrow 2^Q$
The transition function is often visualized and implemented using an action table, also known as a transition table. This table lists all possible states and input symbols, showing the resulting state for each combination.
State | Input '0' | Input '1'
------|-----------|-----------
q0 | q1 | q0
q1 | q0 | q1
This table represents a simple DFA where, if in state $q0$ and the input is ‘0’, the automaton transitions to $q1$. If the input is ‘1’, it stays in $q0$. This precise definition is crucial for recognizing patterns in languages.
Transition functions are vital in various computational fields:
A common misconception is that the transition function is always deterministic. However, NFAs utilize a non-deterministic transition function, which offers greater flexibility in defining languages but requires conversion to a DFA for practical implementation.
What is the role of the transition function?
It dictates the state changes of an automaton based on current state and input.
How is the transition function represented?
Often visualized as an action table or transition table, or defined mathematically.
Difference between DFA and NFA transition functions?
DFA transitions are to a single state, while NFA transitions can be to multiple states or the empty set.
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