Symbolic artificial intelligence: Difference between revisions

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{{Short description|Methods in artificial intelligence research}}

{{Artificial intelligence|Approaches}}

[[File:Artificial-Intelligence.jpg|thumb|right|alt=An artistic representation of AI where a cross section of a human head and brain in profile is mixed with a circuit like background and overlay|An artistic representation of AI]]

In [[artificial intelligence]], '''symbolic artificial intelligence''' is the term for the collection of all methods in artificial intelligence research that are based on high-level [[physical symbol systems hypothesis|symbolic]] (human-readable) representations of problems, [[Formal logic|logic]] and [[search algorithm|search]].<ref>{{Cite journal|last1=Garnelo|first1=Marta|last2=Shanahan|first2=Murray|date=2019-10-01|title=Reconciling deep learning with symbolic artificial intelligence: representing objects and relations|journal=Current Opinion in Behavioral Sciences|language=en|volume=29|pages=17–23|doi=10.1016/j.cobeha.2018.12.010|s2cid=72336067 |doi-access=free|hdl=10044/1/67796|hdl-access=free}}</ref> Symbolic AI used tools such as [[logic programming]], [[production (computer science)|production rules]], [[semantic nets]] and [[frame (artificial intelligence)|frames]], and it developed applications such as [[knowledge-based systems]] (in particular, [[expert systems]]), [[symbolic mathematics]], [[automated theorem provers]], [[ontologies]], the [[semantic web]], and [[automated planning and scheduling]] systems. The Symbolic AI paradigm led to seminal ideas in [[Artificial intelligence#Search and optimization|search]], symbolic programming languages, [[Intelligent agent|agents]], [[multi-agent systems]], the [[semantic web]], and the strengths and limitations of formal knowledge and [[automated reasoning|reasoning systems]].