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Automated theorem proving (also known as ATP or automated deduction) is a subfield of automated reasoning and mathematical logic dealing with proving mathematical theorems by computer programs. Automated reasoning over mathematical proof was a major motivating factor for the development of computer science .
Backward chaining starts with a list of goals (or a hypothesis) and works backwards from the consequent to the antecedent to see if any data supports any of these consequents. [3] An inference engine using backward chaining would search the inference rules until it finds one with a consequent (Then clause) that matches a
John Pollock's OSCAR system [2] is an example of an automated argumentation system that is more specific than being just an automated theorem prover. Tools and techniques of automated reasoning include the classical logics and calculi, fuzzy logic, Bayesian inference, reasoning with maximal entropy and many less formal ad hoc techniques.
Prolog is a logic programming language that has its origins in artificial intelligence, automated theorem proving and computational linguistics. [1] [2] [3]Prolog has its roots in first-order logic, a formal logic, and unlike many other programming languages, Prolog is intended primarily as a declarative programming language: the program is a set of facts and rules, which define relations.
Drools, a forward-chaining inference-based rules engine which uses an enhanced implementation of the Rete algorithm. Evrete, a forward-chaining Java rule engine that uses the Rete algorithm and is compliant with the Java Rule Engine API (JSR 94). D3web, a platform for knowledge-based systems (expert systems).
In the field of artificial intelligence, an inference engine is a software component of an intelligent system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. The typical expert system consisted of a knowledge base and an inference engine.
An inference engine is a computer program that tries to derive answers from a knowledge base. The Cyc inference engine performs general logical deduction. [8] It also performs inductive reasoning, statistical machine learning and symbolic machine learning, and abductive reasoning.
Z3 was open sourced in the beginning of 2015. [3] The source code is licensed under MIT License and hosted on GitHub. [4] The solver can be built using Visual Studio, a makefile or using CMake and runs on Windows, FreeBSD, Linux, and macOS. The default input format for Z3 is SMTLIB2.