Transition Networks in Artificial Intelligence

Transition Networks:

A transition network is a finite state automaton that is used to represent a part of a grammar. A transition network parser uses a number of these transition networks to represent its entire grammar. Each network represents one non-terminal symbol in the grammar.

A transition network is a method of parsing which represents the grammar as a set of a finite state machine (FSM).

Finite State Machine:

A FSM is a model of computational behavior where each node represents an internal state of the system and the arcs are the means of moving between the states. They are used in automata theory to represent grammar. In the case of parsing of natural language, the arcs in the networks represent either a terminal or a non-terminal symbol.

Types of Transition Networks:

1. Augmented Transition Networks (ATNs): ATN was developed by Wiliam Woods in 1970. The ATN method of parsing sentences integrates many concepts from Chomsky’s (1957) formal grammar theory with a matching process resembling a dynamic semantic network.

2. Recursive Transition Networks (RTNs): RTN is a recursive transition network that permits arc labels to refer to other networks and they, in turn, may refer back to the referring network rather than just permitting word categories used previously.