Frame (artificial intelligence): Difference between revisions

Revision as of 02:22, 11 October 2021

Revision as of 01:12, 7 February 2022

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'''Frames''' are an [[artificial intelligence]] [[data structure]] used to divide [[knowledge]] into substructures by representing "[[stereotype]]d situations". They were proposed by [[Marvin Minsky]] in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence [[frame language]]; they are stored as [[Ontology (information science)|ontologies]] of [[Set theory|sets]].

Frames are also an extensive part of [[knowledge representation and reasoning]] schemes. They were originally derived from semantic networks and are therefore part of structure -based knowledge representations. According to Russell and Norvig's "Artificial Intelligence: A Modern Approach", structural representations assemble "[...]facts about particular objectobjects and event types and arrange the types into a large taxonomic hierarchy analogous to a biological taxonomy".

== Frame structure ==

The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met. Some information in the frame is generally unchanged while other information, stored in "terminals", usually change. Terminals can be considered as variables. Top -level frames carry information, that is always true about the problem in hand, however, terminals do not have to be true. Their value might change with the new information encountered. Different frames may share the same terminals.

Each piece of information about a particular frame is held in a slot. The information can contain:

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** Values (called facets)

* Procedures (also called procedural attachments)

** IF-NEEDED : deferred evaluation

** IF-ADDED : updates linked information

* Default Values

** For Data

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A frame's terminals are already filled with default values, which is based on how the human mind works. For example, when a person is told "a boy kicks a ball", most people will visualize a particular ball (such as a familiar [[soccer ball]]) rather than imagining some abstract ball with no attributes.

One particular strength of frame -based knowledge representations is that, unlike semantic networks, they allow for exceptions in particular

instances. This gives frames an amount of flexibility that allowallows representations of real -world phenomena to be reflected more accurately.

Like [[semantic networks]], frames can be queried using spreading activation. Following the rules of inheritance, any value given to a slot that is inherited by subframes will be updated (IF-ADDED) to the corresponding slots in the subframes and any new instances of a particular frame will feature that new value as the default.