Frame (artificial intelligence): Difference between revisions

Revision as of 08:05, 25 June 2025

Latest revision as of 22:59, 31 July 2025

(4 intermediate revisions by 4 users not shown)

Line 6:

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 ==

== What Are Frames in Artificial Intelligence ==

{{Unreferenced section|date=July 2025}}

In AI, frames are data structures used for representing stereotyped situations. They allow systems to understand, interpret, and respond like humans by organizing knowledge into slots and facets—like a mental blueprint.

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.

=== Examples of Frames in AI: ===

Frames represent a disease (e.g., “diabetes”) with slots like symptoms, treatments, risk factors. AI uses this to match patient data.

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.

Chatbots use frames to recognize conversation types (complaint, inquiry) and respond using pre-filled responses.

Each piece of information about a particular frame is held in a slot.

Frames define traffic objects (e.g., pedestrian, stop sign) with rules on behavior and context.

The information can contain:

Frames define user shopping habits with slots for item type, time, and frequency, enabling personalized offers.

* Facts or Data

Frames help AI systems organize and retrieve information efficiently, making them crucial in expert systems, robotics, and natural language processing (NLP).<ref>{{Cite web |last=khan |first=hammad |date=June 24, 2025 |title=The Best Artificial Intelligence Opportunities in Pakistan 2025 |url=https://smarttoolblog.com/the-best-artificial-intelligence-opportunitiesin-in-pakistan-2025/ |url-status=live |access-date=June 25, 2025 |website=smart tool blog}}</ref>

** 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

== Features and advantages ==

{{Unreferenced section|date=July 2025}}

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

Line 37 ⟶ 45:

== Example ==

{{Unreferenced section|date=July 2025}}

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.

Line 150 ⟶ 158:

===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:

Line 217 ⟶ 225:

* 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.