Artificial Intelligence PDF

KOYA UNIVERSITY FACULTY OF ENGINEERING DEPARTMENT OF SOFTWARE ENGINEERING STAGE:1ST STAGE Artificial intelligence Prepared by: SUPERVISED: Rahand Hemn MR Ari Arif Ahmad Hidayat Darun Dler Zhero Ari Blnd Luqman Armin Rebar  To...

KOYA UNIVERSITY FACULTY OF ENGINEERING DEPARTMENT OF SOFTWARE ENGINEERING STAGE:1ST STAGE Artificial intelligence Prepared by: SUPERVISED: Rahand Hemn MR Ari Arif Ahmad Hidayat Darun Dler Zhero Ari Blnd Luqman Armin Rebar  Topic Description Artificial Intelligence (Al) is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include problem-solving, decision-making, speech recognition, language translation, and even creative activities like composing music or generating images. Al is used in various industries, including healthcare, finance, education, and entertainment, making it one of the most transformative technologies of our time. Definition of Al Al refers to the simulation of human intelligence in machines, enabling them to learn from experience, adapt to new inputs, and execute tasks without explicit human intervention. Al systems are often powered by algorithms, machine learning, and deep learning techniques, allowing them to recognize patterns, process data, and make predictions. How the propositional logic is used in AI Propositional logic is crucial in AI for knowledge representation and reasoning. It uses symbols to represent facts and rules, enabling AI to perform logical inference and make decisions. AI systems employ it for automated reasoning, problem- solving (like planning and diagnosis), and building expert systems. While powerful for representing simple knowledge, its limitations in handling complex scenarios and uncertainty often necessitate the use of more advanced logic like first-order logic in AI applications. Essentially, propositional logic provides a foundational framework for AI to manipulate and reason about information.  Application of discrete mathematics in AI Discrete Mathematics Is A Type Of Math That Deals With Separate Or Distinct Objects, And It Is Very Important For Building Smart Computer Programs. Here Are Two Simple Ways It Is Used In Ai: 1. Combinatorics: Combinatorics Is About Counting And Finding Different Ways Things Can Happen. This Is Useful In Ai When There Are Many Possible Choices, Such As In Game-Playing Or Planning. It Helps The Computer Decide The Best Move Without Checking Every Possibility. 2. Probability: Probability Helps Deal With Uncertainty Or Randomness. In Ai, It Is Used To Predict What Might Happen Next Or To Make Decisions When Not All The Information Is Clear. Models Like Bayesian Networks Use Probability To Handle These Uncertain Situations. Example Graph Theory, a branch of discrete mathematics, studies graphs, which are mathematical structures used to represent relationships by connecting a set of points. These graphs create pairwise relationships between objects, making them essential for understanding complex systems. Discrete mathematics is foundational to many AI methods and algorithms. For instance, graph theory helps model neural networks and the internet, where information is processed through interconnected nodes. Combinatorics plays a key role in improving machine learning, especially in tasks like pattern recognition and solving optimization problems. Logic underpins AI decision-making and automated reasoning. Before exploring specific examples, it’s important to understand how discrete structures—such as sets, graphs, and functions—are used to organize data and build algorithms in AI systems.