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Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.
When using reified fluents, a separate predicate is necessary to tell when a fluent is actually true or not. For example, H o l d s A t ( o n ( b o x , t a b l e ) , t ) {\displaystyle HoldsAt(on(box,table),t)} means that the box is actually on the table at time t {\displaystyle t} , where the predicate H o l d s A t {\displaystyle HoldsAt} is ...
In artificial intelligence, symbolic artificial intelligence (also known as classical artificial intelligence or logic-based artificial intelligence) [1] [2] is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. [3]
Modal predicate logic (a combination of modal logic and predicate logic) is used as the formal method of knowledge representation. The connectives from the language model are logically true (indicated by the "L" modal operator) and connective added at the knowledge elicitation stage are possibility true (indicated by the "M" modal operator ...
First-order logic—also called predicate logic, predicate calculus, quantificational logic—is a collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects, and allows the use of sentences that contain variables.
A frame language is a technology used for knowledge representation in artificial intelligence. They are similar to class hierarchies in object-oriented languages although their fundamental design goals are different. Frames are focused on explicit and intuitive representation of knowledge whereas objects focus on encapsulation and information ...
The semantics of autoepistemic logic is based on the expansions of a theory, which have a role similar to model [broken anchor] s in propositional logic. While a propositional model specifies which atomic propositions are true or false, an expansion specifies which formulae F {\displaystyle \Box F} are true and which ones are false.
DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web: the Web Ontology Language (OWL) and its profiles are based on DLs.