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Opening comments

As far as I understand, FIPA SL is also an example for a knowledge representation language. If nobody objects, I will add it to the list of example languages.

Revised introduction I added an introduction and attempted to answer some of the questions on this page, and give a clear overview of the topic. The topic of KR is broad, so I think we should split into parts, and I propose using the question system to direct links (on the other hand, this may be inconsistent with the rest of the site). There are philisophical, cognitive science (particular developmental), and artificial intelligent paths to take.

In terms of AI, there are thousands of different directions for the topic as well. We should at least make the distinction between those were are trying to model the entire world (e.g., common sense reasoning), and those who model just one specialized subset. Of those groups, there lots of different approaches: logical (situation/event calculus, non-monotonic reasoning), statistical/connectionist, another type of symbolic that I'm having trouble classifying (script, frame, semantic nets, etc)....

--- Dustin

Further work: Consult [1], [2] and [3]

As an epistemologist and someone who has studied cognitive psychology a bit, I am very interested in having a much fuller, clearer explanation of what "the problem of how to store and manipulate knowledge" means. It's not self-evident what the problem even is supposed to be.

If this article is to be concerned entirely with this problem, then it should no doubt be located at the problem of knowledge representation (if that's the best name for the problem). --LMS

What is the difference between knowledge representation and just keeping data in a computer memory? -- User:Hirzel

Computers don't know anything; they are not conscious.
Why do you think Computers don't know anything, but humans do? I mean, what exactly constitutes the difference? --denny vrandečić 10:51, Aug 3, 2004 (UTC)

What about our species knowledge ? That's called Taxonomy and might be included in this article :0) Hashar

I would like to see a greater variety of representations listed e.g. patterns, distinctions, concepts, stories, activities, events, cases, rules, objects. More attention to ephemeral (audio) and visual forms.

This article needs an almost complete rewrite. It is very poor as it is. Any textbook on Artificial Intelligence is way better than this.

This article is about Knowledge representation and reasoning as used in the field of Symbolic Artificial Intelligence. The question of how humans represent and reason about knowledge is a question for psychology and philosophy. If someone wants to create such an article we could change the title of this article to make clear it is for computer science. Although my guess is there are good articles on that topic already (e.g., Epistemology) although they may not have the exact name of this article, please check before you create a new one. --MadScientistX11 (talk) 20:29, 20 March 2020 (UTC)[reply]

Remarks on the History section

The history section is kind of longish and expecially the beginning is too far fetched. In many fields of study people have a tendency to extend the history of a field beyond the actual beginning. Of course one can say that DNA is way to represent knowledge. However most of the interesting things how this is done are not know (yet). So it does not actually give a good account of what people think what knowledge represenation is. It is a term which has the roots in the context of data analysis and general computing and it probably about 20..30 years old. Who has some more details on the first uses of the term?

I propose to just delete the first three paragaphs.

The history of KR can be said to begin with DNA and memory molecules,...

Mathematics and related logical notations such as predicate ....the Big Bang ...

In philosophy knowledge is most commonly defined as "justified true belief". Hirzel 13:21, 20 October 2005 (UTC)[reply]

As there was no reaction yet I think I may move the three paragraphs to here for the time beeing. Hirzel 23:24, 6 November 2005 (UTC)[reply]
The history of KR can be said to begin with DNA and memory molecules, which represent information about how to construct various organisms. This may be considered a knowledge representation. Spoken and written language also represent knowledge. The sum total of all books used to pass knowledge from one generation to the next amount to an extensive KR, with the pace of change increasing exponentially since perhaps 1600.
Mathematics and related logical notations such as predicate calculus are more formal and precise representations used for certain kinds of knowledge. Computer models and simulations also amount to representations of knowledge, from the Big Bang to society and culture.
In philosophy knowledge is most commonly defined as "justified true belief". However, knowledge representation uses the term much more broadly: there need be no belief for DNA to function, and language can easily represent incorrect beliefs, as well as things not believed at all.
Hirzel, thank you for your interest in this article. Clearly, the history of the subject stems from the AI days, which waned in the late '80s and which has become part of the stable infrastructure of the field, when it became clear just how difficult the problem of KR is. You can look in the AI books of the period (Nilsson, Winston, etc) to find a sentence whose canonical statement is something like If we can just choose the right representation for the problem at hand, then it becomes easy to solve. Unfortunately I do not have the time to dig up the books and search for the quote right now. But I agree that the timing was about 1975 - 1980 for the first statement. Ancheta Wis 00:54, 7 November 2005 (UTC)[reply]

Moving Advertisement Reference

Currently at the end of the introduction someone added this sentence: "The KR conference series was established to share ideas and progress on this challenging field." and there is a link to some little known scientific foundation devoted to knowledge representation: http://www.kr.org/ I don't think this group is important enough to justify having them at the beginning of the article. There are much more prestigious organizations such as AAAI or IEEE but there is really no need to talk about conferences or specific groups in the introduction to the article. I'm going to remove that sentence and reference and move the link to the external links section of the article. --MadScientistX11 (talk) 20:41, 20 March 2020 (UTC)[reply]

Missing History

The second paragraph of the History section is problematic in several respects:

In these early days of AI, general search algorithms such as A* were also developed. However, the amorphous problem definitions for systems such as GPS meant that they worked only for very constrained toy domains (e.g. the "blocks world"). In order to tackle non-toy problems, AI researchers such as Ed Feigenbaum and Frederick Hayes-Roth realized that it was necessary to focus systems on more constrained problems.[citation needed]

The transition from systems, such as GPS, that worked only for very constrained domains to systems that focussed on more constrained problems sounds like a step backwards rather than forwards.

There is no mention, moreover, of the great debates of the late 1960s and early 1970s about logical versus procedural representations of knowledge. No mention either of the emergence of logic programming and of such notions as Algorithm = Logic + Control, which emerged from those debates. Robert Kowalski (talk) 11:57, 7 November 2023 (UTC)[reply]

These problems have now been addressed. Robert Kowalski (talk) 21:48, 17 November 2023 (UTC)[reply]

Missing alternative points of view

The article takes the position that the choice between full FOL and alternative representations such as frames, semantic networks and rules is primarily a choice between the greater expressibility of FOL and the greater efficiency, for example, of rules. Paul Thagard, in his popular Introduction to Cognitive Science,[1] takes a very different point of view, arguing, for example, that rules are more expressive than logical conditionals. See the section Logic programming#Relationship with the Computational-representational understanding of mind in the article on logic programming, which presents yet another view. Robert Kowalski (talk) 09:19, 18 November 2023 (UTC)[reply]

The article states "The ultimate knowledge representation formalism in terms of expressive power and compactness is First Order Logic (FOL)." This is false. The article on Higher-order logic correctly states that "Higher-order logics with their standard semantics are more expressive." This false assumption undermines the rest of the paragraph concerning the desirability of identifying a less expressive, but more tractable sublanguage of FOL.

On the other hand, Thagard, in his book cited above, correctly states that rules "are very similar to the conditionals discussed in chapter 2, but they have different representational and computational properties." (The conditionals in chapter 2 are if-then sentences in FOL.)

Russell and Norvig[2] (page 282) identify the nature of the differences between FOL and if-then rules by distinguishing between the standard semantics of FOL and the "database semantics" of FOL, which is "very popular in database systems" and "also used in logic programming". The database semantics includes the closed-world, unique names and domain closure assumptions.

I am prepared to correct these and other mistakes in this article, but it will be necessary to replace a significant portion of it. Robert Kowalski (talk) 14:23, 13 January 2024 (UTC)[reply]

I corrected the mistakes and minimised the changes.Robert Kowalski (talk) 11:45, 19 January 2024 (UTC)[reply]

References

  1. ^ Thagard, Paul (2005). Mind: Introduction to Cognitive Science. The MIT Press. p. 11. ISBN 9780262701099.https://www.google.co.uk/books/edition/Mind_second_edition/gjcR1U2HT7kC?hl=en&gbpv=1&pg=PP11&printsec=frontcover
  2. ^ Russell, Stuart J.; Norvig, Peter. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Hoboken: Pearson. ISBN 978-0134610993. LCCN 20190474.
source: https://en.wikipedia.org/wiki/Talk:Knowledge_representation_and_reasoning