Updated: Aug 1
Automated reasoning (AR) involves using artificial intelligence techniques to reason about knowledge represented in a formal language. Automated reasoning applications (ARAs) use AR to solve problems in specific domains.
AR uses a formal language, such as logic, to represent knowledge and employs inference rules, such as resolution and tableau, to reason about it. It can solve problems like deduction, abduction, and induction.
AR has moderate scalability, with some limitations on the size and complexity of problems it can solve.
ARAs solve problems specific to certain domains by using AR. They use domain-specific inference rules and problem types tailored to the domain's specific needs. ARAs can also use ontologies, which are formal representations of knowledge that specify the concepts and relationships between them. ARAs have moderate to high scalability, depending on the complexity of the problems they solve and the domain.
AR and ARAs are useful in software engineering, formal verification, and natural language processing. Examples of ARAs include theorem provers, which use AR to prove mathematical theorems, and expert systems, which use AR to solve problems in specific domains.
In conclusion, AR and ARAs are powerful tools for reasoning about knowledge and solving problems in specific domains. AR uses a formal language and inference rules, while ARAs use domain-specific inference rules and problem types to solve problems in specific domains.
Baader, F., Calvanese, D., McGuinness, D., Nardi, D., & Patel-Schneider, P. (2017). The Description Logic Handbook: Theory, Implementation and Applications (2nd Ed.). Cambridge University Press.
Ginsberg, M. L. (2017). AI and Expert Systems: Principles and Applications. McGraw-Hill.