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{{Distinguish|soft microprocessor}}
{{Expert needed|computer science|date=July 2009}}

In [[computer science]], '''s'''Soft computing''' is the idea oft computing''' (sometimes referred to as [[computational intelligence]], though CI does not have an agreed definition) is the use of inexac like people.  It was conceived by [[Lotfi Zadeh]], pioneer of a mathematical concept known as [[fuzzy sets]] which lead to many new fields such as [[fuzzy control systems]], fuzzy graph theory<ref> http://researchmathsci.org/apamart/apam-v4n1-3.pdf</ref>, [[fuzzy systems]], and so on.   Zadeh observed that people are good at 'solutions to computationally ft' thinking while computers typically are 'hard' tasks such as the solution of [[NP-complete]] problemshinking.<ref>http://soft-computing.de/def.html</ref>  People use concepts like 'some', 'most', for which there is no known algorithm that can compute an exact solution in [[polynomial time]]. Soft computing differs from conventional ('very' rather than 'hard' or precise concepts of 3.5 or 102. People want a 'warm' glass of milk, not one that is 102 degrees. In general, people at good at learning, finding patterns, adapting and are rather unpredictable.  In 'hard)' computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and [[approximation]]. In effect the role model for soft computing is the [[human]] [[mind]].⏎
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, by contrast, machines need precision, determinism and  measures, and although pattern recognition happens, there is a 'brittleness' if things change - it cannot easily adapt. 'Soft' computing by contrast embraces chaotic, neural models of computing that are more pliable.  Because there is no known single method that lets us compute like people,  soft computing involves using a combination of methods that each bring something helpful to achieve this goal. The principal constituents of Soft Computing (SC) are [[Fuzzy logic|Fuzzy Logic]] (FL), [[Evolutionary computation|Evolutionary Computation]] (EC), [[Machine learning|Machine Learning]] (ML) and [[Probabilistic logic|Probabilistic Reasoning]] (PR), with the latter subsuming [[Bayesian network|belief networks]] and parts of learning theory.

==Introduction==
(contracted; show full)* https://web.archive.org/web/20160310135547/http://dspace.nitrkl.ac.in:8080/dspace/bitstream/2080/1136/1/subudhi.pdf

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[[Category:Scientific modeling]]
[[Category:Artificial intelligence]]
[[Category:Semantic Web]]
[[Category:Soft computing]]