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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 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...

 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...

 Artificial Intelligence (AI) has developed rapidly from basic automation tools to highly advanced systems that can learn and make decisions. Leading these advancements are AI Agents, which are independent entities that can learn from their environment, decide, and act upon those decisions to accomplish certain goals. As such,h agents become more deeply embedded across different industries, it is necessary to know their capabilities, uses, and implications. AI agents are a great step ahead of how computers interact with the world. Not only are they programmed to follow instructions, but they also observe, learn, change, and act independently in a way that simulates human choice. Whether it’s a virtual assistant answering your questions, a warehouse robot moving through floors, or a virtual teacher teaching lessons for students, AI agents are the 21st century’s invisible workforce. What is so interesting about AI agents is their capacity to execute tasks independently towards ce...

  Artificial Intelligence (AI) is not just a figment of your favourite sci-fi movie; it is here to stay, and an inseparable part of our lives and environment. AI Technologies are being implemented in many aspects of human life, from virtual assistants to predictive algorithms. AI is set to revolutionize how we live and work, and as we find ourselves on the brink of this venture, we must ask ourselves one simple question: how does this affect society? Artificial Intelligence Everywhere in Our Daily Lives AI permeates daily life in deep ways. It is used for facial recognition and voice commands in smartphones. Streaming services tell us what to watch based on viewing patterns; navigation apps calculate the best routes in real-time. In the health sector, AI is used for diagnosing diseases and developing individualized treatment plans. These applications show how AI has the potential to improve efficiency and convenience. As ubiquitous as all of this is, there are also questions...

 Artificial Intelligence is not the same as it looks today, it took many years to bring the AI to this shape as it sees today, and many researchers, scientists, and mathematicians play vital roles in developing AI to the position where it is now. AI utilizes many previous scientific inventions not identified here because AI as a science has only existed since the middle of the 20th century. The information below provides examples of key milestones and major trends in AI. Learn more about the basics of AI in our comprehensive guide: Introduction to AI . 1931: In 1931, an Austrian mathematician, Kurt Gödel, demonstrated that in first-order predicate logic, every true statement can be derived. When it comes to higher-order logic, on the other hand, there are true statements that can not be derived. 1937: In 1937, Alan Turing, an English Mathematician and Computer scientist, pointed out the limits of intelligent machines with the halting problem. 1943: In 1943, McCulloc...