Frame (artificial intelligence): Difference between revisions

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{{Short description|Artificial intelligence data structure}}

'''Frames''' are an [[artificial intelligence]] [[data structure]] used to divide [[knowledge]] into substructures by representing "[[stereotype]]d situations".

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Frames are also an extensive part of [[knowledge representation and reasoning]] schemes. They were originally derived from [[semantic network]]s and are therefore part of structure-based [[Knowledge representation and reasoning|knowledge representations]].

According to [[Stuart J. Russell|Russell]] and [[Peter Norvig|Norvig]]'s ''[[Artificial Intelligence: A Modern Approach]]'', structural representations assemble "[...]facts about particular objectsobject and event types and [arrange] the types into a large [[taxonomy|taxonomic]] hierarchy analogous to a [[biological taxonomy]]".

== Frame structure ==

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The frame contains information on how to use the frame, what to expect next, and what to do when these expectations are not met.

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== Features and advantages ==

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A frame's terminals are already filled with default values, which is based on how the [[Mind|human mind]] works.

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== Example ==

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Worth noticing here is the easy analogical reasoning (comparison) that can be done between a boy and a monkey just by having similarly named slots.

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===Comparison of frames and objects===

Frame languages have a significant overlap with [[Object-oriented programming|object-oriented]] languages. The terminologies and goals of the two communities were different but as they moved from the academic world and labs to the commercial world developers tended to not care about philosophical issues and focused primarily on specific capabilities, taking the best from either camp regardless of where the idea began. What both paradigms have in common is a desire to reduce the distance between concepts in the real world and their implementation in software. As such both [[paradigm]]s arrived at the idea of representing the primary software objects in [[Taxonomy|taxonomies]] starting with very general types and progressing to more specific types.

The following table illustrates the [[correlation]] between standard terminology from the object-oriented and frame language communities:

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The most notable of the more formal approaches was the [[KL-ONE]] language.<ref>{{cite journal|last=Brachman|first=Ron|title=A Structural Paradigm for Representing Knowledge|journal=Bolt, Beranek, and Neumann Technical Report|year=1978|issue=3605|url=http://www.dtic.mil/docs/citations/ADA056524}}{{dead link|date=June 2022|bot=medic}}{{cbignore|bot=medic}}</ref> KL-ONE later went on to spawn several subsequent Frame languages. The formal semantics of languages such as KL-ONE gave these frame languages a new type of automated reasoning capability known as the [[Deductive classifier|classifier]]. The classifier is an engine that analyzes the various declarations in the frame language: the definition of sets, subsets, relations, etc. The classifier can then automatically deduce various additional relations and can detect when some parts of a model are inconsistent with each other. In this way many of the tasks that would normally be executed by forward or backward chaining in an inference engine can instead be performed by the classifier.<ref>{{cite journal|last=MacGregor|first=Robert|title=Using a description classifier to enhance knowledge representation|journal=IEEE Expert|date=June 1991|volume=6|issue=3|doi=10.1109/64.87683|pages=41–46|s2cid=29575443 }}</ref>

This technology is especially valuable in dealing with the Internet. It is an interesting result that the formalism of languages such as KL-ONE can be most useful dealing with the highly informal and unstructured data found on the Internet. On the Internet it is simply not feasible to require all systems to standardize on one data model. It is inevitable that terminology will be used in multiple inconsistent forms. The automatic classification capability of the classifier engine provides AI developers with a powerful toolbox to help bring order and consistency to a very inconsistent collection of data (i.e., the Internet). The vision for an enhanced Internet, where pages are ordered not just by text keywords but by classification of concepts is known as the [[Semantic Web]]. Classification technology originally developed for Frame languages is a key enabler of the Semantic Web.<ref>{{cite journal |last=Berners-Lee |first=Tim |author2=James Hendler |author3=Ora Lassila |title=The Semantic Web A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities |journal=Scientific American |date=May 17, 2001 |url=http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html |doi=10.1038/scientificamerican0501-34 |volume=284 |issue=5 |pages=34–43 |url-status=dead |archive-url=https://web.archive.org/web/20130424071228/http://www.cs.umd.edu/~golbeck/LBSC690/SemanticWeb.html |archive-date=2013-04-24 |url-access=subscription }}</ref><ref>{{cite web|last=Horridge|first=Mathew|title=Protégé OWL Tutorial A step-by-step guide to modelling in OWL using the popular Protégé OWL tools.|url=http://130.88.198.11/tutorials/protegeowltutorial/|work=Manchester University|access-date=9 December 2013|archive-url=https://web.archive.org/web/20131213081905/http://130.88.198.11/tutorials/protegeowltutorial/|archive-date=13 December 2013|url-status=dead}}</ref> The "neats vs. scruffies" divide also emerged in Semantic Web research, culminating in the creation of the [[Linking Open Data]] community—their focus was on exposing data on the Web rather than modeling.

== See also ==

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* Russell, Stuart J.; Norvig, Peter (2010), ''Artificial Intelligence: A Modern Approach'' (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, {{ISBN|0-13-604259-7}}, ch. 1.

* Marvin Minsky, [http://web.media.mit.edu/~minsky/papers/Frames/frames.html A Framework for Representing Knowledge], MIT-AI Laboratory Memo 306, June, 1974.

* Daniel G. Bobrow, Terry Winograd, [ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/76/581/CS-TR-76-581.pdf An Overview of KRL, A Knowledge Representation Language]{{dead link|date=May 2025|bot=medic}}{{cbignore|bot=medic}}

* R. Bruce Roberts and Ira P. Goldstein, [https://web.archive.org/web/20170706131617/ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-408.pdf The FRL Primer], 1977

* R. Bruce Roberts and Ira P. Goldstein, [https://web.archive.org/web/20170706131620/ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-409.pdf The FRL Manual], 1977

* {{cite journal | last1 = Brachman | first1 = R. | last2 = Schmolze | first2 = J. | year = 1985 | title = An overview of the KL-ONE Knowledge Representation System | journal = Cognitive Science | volume = 9 | issue = 2| pages = 171–216 | doi=10.1016/s0364-0213(85)80014-8| doi-broken-date = 8 December 2024 | doi-access = free }}

* {{cite journal | last1 = Fikes | first1 = R. E. | last2 = Kehler | first2 = T. | year = 1985 | title = The role of frame-based representation in knowledge representation and reasoning | journal = Communications of the ACM | volume = 28 | issue = 9| pages = 904–920 | doi=10.1145/4284.4285| s2cid = 9868560 | doi-access = free }}

* Peter Clark & Bruce Porter: KM - The Knowledge Machine 2.0: Users Manual, http://www.cs.utexas.edu/users/mfkb/RKF/km.html.