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Knowledge representation and reasoning (KRR, KR&R, or KR²) is a field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks, such as diagnosing a medical condition or having a natural-language dialog.
The Loom project's goal is the development and fielding of advanced tools for knowledge representation and reasoning in artificial intelligence. Specifically to enable code to be generated from provably valid domain models. Loom is a language and environment for constructing intelligent applications.
Approaches for integration are diverse. [10] Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.
Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic . In contrast to the latter, the core reasoning problems for DLs are (usually) decidable , and efficient decision procedures have been designed and implemented for ...
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 most powerful form of knowledge representation is first-order logic. However, it is not possible to implement knowledge representation that provides the complete expressive power of first-order logic. Such a representation will include the capability to represent concepts such as the set of all integers which are impossible to iterate through.
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]
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...