Realize the Basis of Working Before Transforming from Traditional Reasoning to Automated Reasoning in AI ! Design Your Every Step Count !!

Abstract:

Automated reasoning is a computer science field that uses mathematical proof to help determine what a program or system will do. It's a sub-field of artificial intelligence that's also related to theoretical computer science and philosophy. 

 

Here's some more information about automated reasoning: 

 

How it works

Automated reasoning uses a reasoning engine to apply inferential logic to a set of hypothesized relationships between system variables. The engine then determines what the individual truths imply about the overall system. 

 

What it's used for

Automated reasoning is used to check and verify mathematical proofs, solve problems in engineering, and attack open questions in mathematics and logic. 

 

What it's based on

Automated reasoning is based on classical logics and calculi, fuzzy logic, Bayesian inference, and reasoning with maximal entropy. 

 

How it's used in practice

AWS uses automated reasoning to reason about the security, availability, compliance, and correctness of its infrastructure and services. 

 

Related fields

Automated reasoning is related to automated theorem proving, interactive theorem proving, and automated proof checking. 

Keywords;

Automated Reasoning , Mathematical Proof, Logic, Fuzzy Logic, Automation, Intelligent Evaluation, Algorithms for Automated Reasoning 

Learning Outcomes

After undergoing this article you will be able to understand the following:

1. What's Automated Reasoning?

2. How Automated Reasoning  works?

3. Why Automated Reasoning is important?

4. What's the basis of Automated Reasoning ?

5. How Automated Reasoning is used in practice and where?

6. How to find Automated Reasoning Solutions for tasks?

7. What's the algorithm of Automated Reasoning ?

8. What's the elements of Automated Reasoning ?

9. How Automated Reasoning is classified?

10. Common characteristics of Automated Reasoning

11. Benefits of Automated Reasoning 

12. Limitations of Automated Reasoning 

13. Strategies for implementing Automated Reasoning 

14. Conclusions

15. FAQs

References 

1. What's Automated Reasoning?

Automated reasoning is a field of computer science that uses mathematical proof to determine what a program or system can or cannot do. It's a type of artificial intelligence that uses mathematical reasoning to make logical or mathematical statements. 

 

Automated reasoning uses the same tools as mathematicians to solve complex challenges. These tools help determine if a statement or expression is true. 

 

Here are some applications of automated reasoning: Program verification, Testing, Scheduling, and Solving mathematical problems. 

 

Amazon uses automated reasoning to reason about the security, availability, correctness, and compliance of its infrastructure and services. For example, automated reasoning can detect misconfigurations that could expose customer data. 

 

2. How Automated Reasoning  works?

Automated reasoning is a computer science field that uses mathematical proof to help determine what a program or system can or cannot do. It's a subfield of artificial intelligence (AI) that uses logic to make inferences and solve problems. 

 

Here's how automated reasoning works: 

 

Logical rules

Automated reasoning uses logical rules, inference mechanisms, and knowledge representation techniques to help make decisions. 

 

Deduction calculus

Automated reasoning programs use deduction calculus, which is a set of logical axioms and deduction rules, to prove conclusions from assumptions. 

 

Problem domain

Automated reasoning programs are designed to solve a specific class of problems. 

 

Algorithms and techniques

Automated reasoning uses algorithms and computational techniques to enable machines to perform deductive, inductive, or abductive reasoning. 

 

Automated reasoning has many practical applications, including: Detecting misconfigurations that could expose customer data, Improving the user experience of Prime Video, Making operations in AWS easier, and Making systems more secure. 

 

3. Why Automated Reasoning is important?

Automated reasoning is important because it helps create intelligent systems that can solve problems, make decisions, and reason logically. It's a sub-field of artificial intelligence (AI) that uses mathematical and logical methods to verify that systems work as intended. Here are some reasons why automated reasoning is important: 

 

Proves systems work as intended

Automated reasoning can prove that systems work as intended by producing proofs that are supported by mathematical theorems. 

 

Ensures accuracy

Automated reasoning can check mathematical proofs to ensure that calculations are accurate. 

 

Improves AI systems

Automated reasoning can help make AI systems more intelligent and autonomous. 

 

Complements machine learning

Automated reasoning and machine learning can complement each other, and many research groups are trying to integrate the two methods. 

 

Helps identify unwanted behaviors

Automated reasoning can help identify unwanted behaviors in systems and fix them before they happen. 

 

4. What's the basis of Automated Reasoning ?

Automated reasoning is a field of computer science that uses logic and mathematical proof to help computers solve problems and make decisions. It's based on the following principles: 

 

Logic

Automated reasoning uses logical rules and inference mechanisms to represent knowledge and make logical inferences. 

 

Algorithms

Automated reasoning uses algorithms and computational techniques to perform deductive, inductive, or abductive reasoning. 

 

Mathematical proof

Automated reasoning uses mathematical proof to help computers solve complex challenges. 

 

Automated reasoning is used in many applications, including: 

 

Artificial intelligence: Automated reasoning helps AI systems make reasoned judgments and inferences. 

 

Software verification: Automated reasoning can help verify the correctness of software. 

 

Knowledge representation systems: Automated reasoning can help represent knowledge in systems. 

 

Some examples of automated reasoning tools include: ACL2, CVC5, HOL-light's Meson_tac, MiniSat, and Vampire. 

5. How Automated Reasoning is used in practice and where?

Automated reasoning is a computer science discipline that uses mathematical and logical methods to infer the status of a system. It's used in a variety of applications, including: 

 

Mathematics: Automated reasoning can be used to check mathematical proofs, which can help ensure that calculations are correct. 

 

Engineering: Automated reasoning can be used in engineering applications. 

 

Computer science: Automated reasoning can be used to prove that systems work as intended. 

 

Healthcare: Automated reasoning can be used to support medical diagnostic processes. 

 

Network security: Automated reasoning can be used to prove that systems used to configure networks, allow network access, or grant permissions work as intended. 

 

Automated reasoning can be used to: 

 

Prove that a system works as intended: Automated reasoning can be used to prove that a system design or implementation works the way it was intended to. 

 

Develop mathematical statements: Automated reasoning can be used to develop mathematical statements. 

 

Find models: Automated reasoning can be used to find models. 

 

Conclude with unsatisfiability: Automated reasoning can be used to conclude with unsatisfiability. 

 

6. How to find Automated Reasoning Solutions for tasks?

Here are some resources for learning about automated reasoning techniques: 

 

Stanford Encyclopedia of Philosophy

Explains the goal of automated reasoning, which is to use deduction calculus to prove conclusions from assumptions. 

 

AWS

Explains how automated reasoning uses SAT solvers to find satisfying assignments to arguments in propositional logic. 

 

Amazon Science

Discusses how automated reasoning can be applied to both policies and code, and how it can be used to answer questions about the interpretation of configurations. 

 

Lark

Provides a step-by-step guide for using automated reasoning, including how to identify the problem, gather knowledge, and implement reasoning mechanisms. 

 

DeepAI

Explains how automated reasoning frameworks require defining the problem domain, language, deduction calculus, and resolution. 

 

Argonne National Laboratory

Discusses how automated reasoning methods use inferential logic to determine the status of a system based on hypothesized relationships between variables. 

 

7. What's the algorithm of Automated Reasoning ?

Automated reasoning is a field of computer science that uses logic and algorithms to solve problems that typically require human intelligence. Automated reasoning uses a variety of techniques, including: 

 

SAT solving: A technique that uses SAT solvers to find satisfying assignments to arguments in propositional logic. 

 

Deduction calculi: A range of options for the deduction calculus to use, depending on the problem domain. 

 

Symbolic computation: A technique used in automated reasoning. 

 

Constraint satisfaction: A technique used in automated reasoning. 

 

Theorem proving: A technique used in automated reasoning. 

 

Automated reasoning uses a reasoning engine to apply inferential logic to determine what individual truths imply about the overall system. The reasoning engine inspects each relationship between system variables to determine its validity. 

8. What's the elements of Automated systems for Automated Reasoning ?

Automated systems are made up of several elements, including: 

 

Power: The power that runs the system and completes the process 

 

Program: A set of instructions that direct the process 

 

Control system: The system that activates the instructions 

 

Human-machine interface (HMI): A device that allows a person to interact with a machine 

 

Sensors: A common component of automation systems 

 

Autonomous mobile robots: Robots that use sensors to navigate their environment and work around people and instruments 

 

Other elements of automated systems include: 

 

Integrated automation: A type of automation that uses computers or industrial robots to control a production unit 

 

IT automation reporting: A system that generates reports on IT system performance 

 

9. How Automated Reasoning is classified?

There are many different types of reasoning in AI, but some of the most common are Deductive reasoning, 

Inductive reasoning, and Abductive reasoning. 

A brief explanation of each types of Narratives is presented below :

Deductive reasoning

Deductive reasoning is a psychological process that people use to make decisions and solve problems. It's a cognitive function, meaning it's a conscious intellectual activity like thinking and understanding. In deductive reasoning, you use general ideas or premises to come to a specific conclusion.

Deductive reasoning is when you start with a set of premises and then use them to logically derive a conclusion.

Here are some examples of deductive reasoning: 

 

All men are mortal, Socrates is a man, therefore Socrates is a mortal

This is a well-known example of deductive reasoning, attributed to Aristotle. 

 

All raccoons are omnivores, this animal is a raccoon, therefore this animal is an omnivore

This example illustrates that if a group has a quality, then any individual in that group must also have that quality. 

 

All insects have six legs, spiders have eight legs, therefore spiders are not insects

This example illustrates the use of deductive logic arguments, which start with premises and then form a conclusion based on those premises. 

 

Milo has no cake flour, he needs cake flour to bake brownies, therefore he cannot make brownies

This example illustrates how deductive reasoning uses accepted facts to make a decision. 

 

Deductive reasoning is a process that involves using ideas or premises that are believed to be true to develop a conclusion that is also believed to be true. The premises logically support and relate to the conclusion, but the premises don't need to be true for the argument to be valid. 

 

Inductive reasoning 

Inductive reasoning is a logical method of making inferences or conclusions based on observations and patterns. Here are some examples of inductive reasoning: 

 

Shopping for a gift

You might use inductive reasoning to choose a gift for your mom by remembering she likes dark chocolate, but not dark chocolate with coconut flakes. You might then buy a variety of dark chocolate candies with different fillings, and notice that she prefers the caramel-filled ones. 

 

Allergic to strawberries

You might conclude that you are likely allergic to strawberries if your lips swell after eating them multiple times. 

 

Fish with yellow fins

You might conclude that any new fish species in a genus with yellow fins is likely to have yellow fins, based on the fact that all known fish species in that genus have yellow fins. 

 

A volcano that erupts

You might conclude that a volcano will erupt again soon if it has erupted about every 500 years for the last 1 million years, and it last erupted 499 years ago. 

 

A child who enjoys the beach

You might conclude that a child who loves building sand castles, playing in the ocean, and collecting sea shells probably enjoys the beach. 

 

Inductive reasoning can't lead to absolute certainty, and conclusions can be skewed if relevant data is overlooked. 

Abductive reasoning

Abductive reasoning is a form of logical inference that involves finding the most likely explanation for a set of observations. It's also known as "inference to the best explanation". 

 

Here are some characteristics of abductive reasoning: 

 

Starts with an observation

Abductive reasoning begins with an unexpected fact or occurrence and works backward to find the most likely explanation. 

 

Involves iteration

Abductive reasoning involves iterating between current theory and the data to craft and verify a conclusion. 

 

Produces a probable conclusion

The conclusion of an abductive argument is considered to be true or likely true, but it does not have to be. 

 

Common in science

Abductive reasoning is commonplace in the sciences and other situations where we make inferences about the underlying explanations of things in our environment. 

 

Developed by Charles Sanders Peirce

American philosopher and logician Charles Sanders Peirce formulated and advanced abductive reasoning in the latter half of the 19th century.

10. Common characteristics of Automated Reasoning

Some characteristics of reasoning include:

Purpose: All reasoning has a purpose, such as to solve a problem, settle a question, or figure something out.

Assumptions: All reasoning is based on assumptions.

Point of view: All reasoning is done from a certain point of view.

Data: All reasoning is based on data, evidence, and information.

Concepts and ideas: All reasoning is shaped by and expressed through concepts and ideas.

Inferences: All reasoning contains inferences that give meaning to data and draw conclusions.

Implications and consequences: All reasoning has implications and consequences and leads somewhere. 

 

Some types of reasoning include: 

 

Inductive reasoning

This type of reasoning involves generalizing premises into factual statements by analyzing specific observations. It's also known as "bottom-up" reasoning. 

 

Abductive reasoning

This type of reasoning starts with an observation or observations and seeks the most probable explanation or conclusion. 

 

Conditional reasoning

This type of reasoning uses if/then statements and is a form of deductive reasoning. 

 

Problem solving

This type of reasoning involves identifying problems, analyzing information, and developing solutions based on evidence. 

 

Clinical reasoning

This type of reasoning requires memory, critical, creative, and practical reasoning skills. 

 

Numerical reasoning

This type of reasoning provides insights into a job candidate's grasp of numbers, problem-solving, and logical thinking skills. 

 

11. Benefits of Automated Reasoning 

Automated reasoning offers several advantages over traditional manual methods, including 

increased efficiency, 

accuracy, and 

scalability. 

It can handle complex problems that might be too difficult or time-consuming for humans to tackle effectively, leading to faster decision-making and problem-solving.

12. Limitations of Automated Reasoning 

Automated reasoning systems have several limitations, including: 

 

Computational complexity

Some automated reasoning systems can be computationally complex, which can cause performance issues in environments with limited resources. 

 

Knowledge representation

Representing different knowledge domains accurately can be challenging, which can affect the accuracy of inferences and decisions. 

 

Input quality

The quality and relevance of input data can affect the effectiveness of automated reasoning, potentially leading to biased or inaccurate results. 

 

Ambiguity and uncertainty

Automated reasoning systems may have difficulty with problems that involve ambiguity or uncertainty. 

 

Human intuition and creativity

Automated reasoning systems may struggle with problems that require human intuition or creativity. 

 

Output interpretation

The output from automated reasoning systems may not be easy for humans to interpret or act on, which can make communication between humans and machines difficult. 

 

Solver performance

The performance of a solver on a particular problem instance can depend on arbitrary decisions made by the configuration or a random seed. 

 

Heuristic failure

Automated reasoning tools use heuristics to solve intractable problems, but these heuristics can fail, resulting in "Don't know" answers or long execution times. 

 

13. Strategies for implementing Automated Reasoning 

Automated reasoning is a computer science discipline that uses algorithms to prove theorems and solve problems by applying logic and mathematical methods. It can be used in many fields, including mathematics, engineering, and computing science. 

 

To implement automated reasoning, you can follow these steps:

Identify the problem: Define the problem and the key variables, constraints, and parameters.

Gather knowledge: Collect relevant information and knowledge about the problem domain.

Select reasoning mechanisms: Choose algorithms and techniques that are appropriate for the problem.

Implement the reasoning mechanisms: Integrate the reasoning mechanisms into the AI system, along with relevant data sources and knowledge bases.

Validate and verify: Compare the output of the automated reasoning process to known benchmarks or expert judgments.

Refine and improve: Use real-world feedback and performance evaluations to refine the automated reasoning processes.

Deploy and monitor: Integrate the automated reasoning system into the target environment. 

 

14. Conclusions

Automated reasoning can produce conclusions in a variety of ways, including:

Proving theorems

Automated reasoning tools, also known as theorem provers, can prove theorems by using algorithmic descriptions of the calculus being used.

Reasoning about logical formulas

Automated reasoning tools can reason about logical formulas, either fully or partially automatically.

Exploring interpretations

Automated reasoning tools can explore the space of interpretations.

Finding models

Automated reasoning tools can find models or conclude with unsatisfiability.

Providing accuracy of a proof

Automated reasoning programs can provide the accuracy of a proof as output. 

 

Automated reasoning is a key concept in artificial intelligence (AI) that aims to replicate human-like decision-making processes. It can be used to solve complex problems, derive logical conclusions, and make informed decisions in real-time. 

 

Automated reasoning can be applied to both policies and code. For example, it can be used to prove that systems used to configure networks, allow network access, or grant permissions work as intended. 

 

15. FAQs

Q. What's the tools and techniques of Automated Reasoning?

Ans.

Automated reasoning uses a variety of tools and techniques, including: 

 

Classical logics and calculi: Used in automated reasoning 

 

Fuzzy logic: A tool used in automated reasoning 

 

Bayesian inference: A tool used in automated reasoning 

 

Reasoning with maximal entropy: A tool used in automated reasoning 

 

Ad hoc techniques: Less formal techniques used in automated reasoning 

 

Automated theorem proving (ATP): A subfield of automated reasoning that uses computer programs to prove mathematical theorems 

 

Model checking: A technique that automatically generates proofs or counterexamples based on a supplied model and constraints 

 

Theorem proving: A technique that verifies properties based on fundamental theory and assumptions 

 

Term rewriting: An automated deduction method that requires strategies to direct its application 

 

Rippling: A heuristic that uses annotations to selectively restrict the rewriting process 

 

Knowledge-based systems: A technique that uses a knowledge base that contains knowledge used by human experts 

 

Automated reasoning uses mathematical and logic-based algorithmic verification methods to produce proofs of security or correctness. 

References 

Books / Automated reasoning

From sources across the web

Handbook of Automated Reasoning

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2001

Handbook of Practical Logic and Automated Reasoning

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John Harrison, 2009

Automated Reasoning: Introduction and Applications

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Larry Wos, 1984

Automated Reasoning: 11th International Joint Conference, IJCAR 2022, Haifa, Israel, August 8-10, 2022, Proceedings

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2022

Automated reasoning

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Larry Wos

Automated Reasoning: 5th International Joint Conference, IJCAR 2010, Edinburgh, UK, July 16-19, 2010, Proceedings

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2010

Automated Reasoning and Its Applications

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Robert Veroff, 1997

Automated Reasoning: 10th International Joint Conference, IJCAR 2020, Paris, France, July 1–4, 2020, Proceedings, Part II

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2020

Logic for Programming and Automated Reasoning: 7th International Conference, LPAR 2000 Reunion Island, France, November 6-10, 2000 Proceedings

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2000

Automated Reasoning with Analytic Tableaux and Related Methods: 20th International Conference, TABLEAUX 2011, Bern, Switzerland, July 4-8, 2011, Proceedings

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2011

Automated Reasoning in Higher-order Logic: Set Comprehension and Extensionality in Church's Type Theory

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Chad E. Brown, 2007

Automated Reasoning: 7th International Joint Conference, IJCAR 2014, Held as Part of the Vienna Summer of Logic, Vienna, Austria, July 19-22, 2014, Proceedings

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2014

Automation of Reasoning: Classical Papers on Computational Logic 1957–1966

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1983

Automation of Reasoning: 2: Classical Papers on Computational Logic 1967–1970

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1983

Automated Reasoning: 6th International Joint Conference, IJCAR 2012, Manchester, UK, June 26-29, 2012, Proceedings

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2012