Terms & Conditions

Effective Date: May 08, 2025

Welcome to theailearner.blogspot.com ("the Site"), a personal blog focused on Artificial Intelligence (AI), Knowledge-Based Systems, Logic in AI, and related topics. By accessing or using this website, you agree to comply with and be bound by the following Terms and Conditions. Please read them carefully before using the Site.

1. Intellectual Property Rights

All blog content, including but not limited to articles, text, images, diagrams, and code snippets, is the intellectual property of the admin unless otherwise credited. Some images used are sourced under Creative Commons or open-source licenses and are properly attributed.

2. User Conduct

By using this Site, you agree not to:

Post or transmit any harmful, abusive, or unlawful content.
Attempt to breach the security of the website or gain unauthorized access.
Violate copyright or intellectual property laws.

3. External Links

This website may include links to third-party sites for reference, learning, or attribution (e.g., Creative Commons image sources). We do not control and are not responsible for the content or privacy practices of these external sites.

4. Disclaimer

All content on this blog is provided for informational and educational purposes only. While we strive for accuracy, we make no guarantees regarding the completeness, reliability, or current validity of any information.

The blog does not offer professional or legal advice. Readers should conduct their own research or consult qualified professionals before making decisions based on the content provided here.

5. Changes to Terms

We reserve the right to modify or update these Terms and Conditions at any time without notice. Continued use of the Site after changes have been posted constitutes acceptance of the revised terms.

Comments

How First-Order Predicate Logic Powers AI Reasoning

Artificial Intelligence, Mathematics, and computer science depend heavily on logic for decision-making, knowledge representation, and automated reasoning. Perhaps the most powerful logical system in all three fields is First-Order Predicate Logic (FOPL). While propositional logic is not able to make very precise statements about objects and relationships, FOPL can. In this blog, we will explore the foundations, structure, and application of First-Order Predicate Logic, along with its syntax, semantics, and real-world relevance. Learn more about the foundation of logical reasoning in AI by reading our post on Propositional Logic. What is First-Order Predicate Logic? First-Order Predicate Logic (FOPL), or First-Order Logic (FOL), is a symbolic formal system that extends propositional logic by introducing quantifiers, predicates, functions, and variables. FOPL enables us to make statements like: ‘Each student has a laptop.’ This is more pr...

Propositional Logic Explained Simply: Learn with Easy Examples

Propositional Logic Artificial Intelligence (AI) uses formal logic systems to mimic human reasoning. Of these systems, Propositional Logic is one of the pillars of knowledge representation and reasoning. Although it’s a basic and well-defined type of logic, it provides an entrance point for grasping more sophisticated logical frameworks in AI, such as First-Order Logic, Description Logic, and so forth. This blog post discusses propositional logic’s syntax, semantics, proof systems, resolution, Horn clauses, computability, and complexity, and its applications are limited in AI. What is Propositional Logic? Propositional logic, also referred to as propositional calculus or sentential logic, is concerned with propositions, i.e., declarative sentences that are true or false but not both. It does not include variables and quantifiers, unlike predicate logic. Propositional logic, in the case of AI, is applied to represent basic knowledge and deduce new facts based on current facts with the a...

The Role of Knowledge-Based Systems in Artificial Intelligence

Artificial Intelligence (AI) has made tremendous progress over the past decades, from rule-based systems to powerful machine learning systems. Along the way, Knowledge-Based Systems have led the way in embedding human knowledge into computer systems. Knowledge-based Systems try to mimic the decision-making powers of human experts, providing solutions across different fields, from healthcare to finance. Understanding Knowledge-Based Systems A knowledge-based system is a computer application that uses a knowledge-based regarding a particular subject to address complex problems. Unlike conventional programs that use a sequence of instructions, knowledge-based systems use a knowledge base and an inference engine to mimic human thought processes. Knowledge Base : It contains the domain-specific facts, rules, and heuristics. Inference Engine: Uses logical rules on the knowledge base to derive new information or make decisions. The structure enables a knowledge-bas...

AIHUB

Search This Blog