Reasoning Algorithms

- Overview 

Reasoning algorithms are a logical method used in artificial intelligence (AI) systems to apply rules to knowledge graphs to reveal new facts and relationships. It can be achieved inductively or deductively, with rule-based reasoning algorithms being a well-known example of expert systems.

Artificial intelligence (AI) uses algorithms and techniques to simulate human reasoning. These algorithms include: Greedy algorithms, Dynamic programming, Graph algorithms, Pattern searching, Recursion, Divide and conquer, Mathematical algorithms, Geometric algorithms. 

The core algorithms used in automated reasoning systems are based on mathematical logic. These algorithms include: Resolution, Model checking, Satisfiability checking.

AI also uses various types of reasoning, including: Deductive reasoning, Inductive reasoning, Abductive reasoning, Common sense reasoning, and Monotonic reasoning
Non-monotonic reasoning

Inductive and deductive reasoning are two fundamental approaches AI uses to make sense of data, detect patterns, and derive insights. The main difference between these forms of reasoning is that in deductive reasoning, the truth of the premises guarantees the truth of the conclusion. In inductive reasoning, the truth of the premise lends support to the conclusion without giving absolute assurance. 

Microsoft has also unveiled a new AI training method called the "Algorithm of Thoughts" (AoT). AoT trains large language models (LLMs) to go through the problem-solving steps like a traditional pathfinding algorithm.

- Knowledge Representation Languages

Here are some knowledge representation languages: 

• Semantic network: A network built by linguists to represent the semantic relations between words. Semantic networks are often used as a form of knowledge representation. 
• Frame representation: A technology used for knowledge representation in artificial intelligence. Frames are stored as ontologies of sets and subsets of the frame concepts. 
• Logical representation: The primary form of knowledge representation to AI machines with a well-defined syntax and semantics. 
• Description logics (DLs): A family of knowledge representation languages that can be used to represent the terminological knowledge of an application domain. 
• Web Ontology Language (OWL): A knowledge representation language for authoring ontologies. Ontologies are a formal way to describe knowledge for various domains. 
• Conceptual graphs: A knowledge representation language designed as a synthesis of several different traditions. 
• Natural language: One of the methods of knowledge representation. The fundamental unit of knowledge in such languages is often a sentence that consists of a set of words arranged according to grammatical rules. 

Other knowledge representation languages include: Production rules, Syntactic inference. 

[More to come ...]