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Concept
Frame (artificial intelligence)
Summary
Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped 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 languages; they are stored as ontologies of 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 objects and event types and arrange the types into a large taxonomic hierarchy analogous to a biological taxonomy".
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:
Facts or Data
Values (called facets)
Procedures (also called procedural attachments)
IF-NEEDED: deferred evaluation
IF-ADDED: updates linked information
Default Values
For Data
For Procedures
Other Frames or Subframes
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.
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