Symbolic reasoning

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Symbolic reasoning, also known as logic-based reasoning or deductive reasoning, is a type of reasoning in which conclusions are drawn from a set of facts or premises using logical rules of inference. In symbolic reasoning, knowledge is represented in the form of symbols and relationships among them, such as predicates, variables, and logical operators.

Symbolic reasoning involves a set of rules for manipulating symbols to derive new conclusions from existing knowledge. This process of reasoning can be thought of as a step-by-step deduction of new knowledge from old knowledge. The rules of inference in symbolic reasoning are based on the principles of formal logic, which are based on a set of axioms and rules for deriving new statements from the existing ones.

Symbolic reasoning is widely used in artificial intelligence for various tasks such as expert systems, natural language processing, and theorem proving. Expert systems use symbolic reasoning to make inferences from a set of rules and facts to solve complex problems in a specific domain. Natural language processing systems use symbolic reasoning to understand and generate natural language text. Theorem proving is an application of symbolic reasoning that involves proving mathematical theorems using logical inference.

One of the advantages of symbolic reasoning is that it can provide a clear and transparent representation of knowledge, which can be easily understood and interpreted by humans. However, symbolic reasoning is limited by its inability to handle uncertainty and incomplete information. It also requires a significant amount of human effort to create and maintain the knowledge base and the rules of inference.

In contrast, statistical reasoning, which is another type of reasoning used in artificial intelligence, can handle uncertainty and incomplete information more effectively. However, it is often criticized for being less transparent and less interpretable than symbolic reasoning

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