Introduction to Artificial Intelligence Notes

Introduction to Artificial Intelligence 1. What is Artificial Intelligence? Artificial Intelligence (AI) is a branch of computer science that enables machines and computer systems to perform tasks that normally require human intelligence. These tasks include learning, reasoning, problem-solving, decision-making, understanding language, and recognizing images. 2. Types of AI 1. Narrow AI / Weak AI AI designed to perform one specific task or a limited set of tasks. Examples include face recognition, Google Maps, voice assistants, and recommendation systems. Most AI systems available today are Narrow AI. 2. General AI / Strong AI A theoretical AI system that can understand, learn, reason, and perform many different tasks like a human. It does not currently exist as a fully realized system. 3. Super AI A hypothetical form of AI that would have intelligence beyond human capabilities in almost every area. 3. Types of AI Agents An AI agent is a system that perceives its environment through sensors and performs actions using actuators. 1. Simple Reflex Agent Makes decisions based only on the current situation using predefined condition-action rules. Example: an automatic door opens when its sensor detects a person. It is simple and fast, but cannot remember past situations or learn. 2. Model-Based Agent Maintains an internal model or memory of the environment and uses current and previous information to make decisions. Example: a robot vacuum remembers which areas have already been cleaned. 3. Goal-Based Agent Takes actions to achieve a specific goal and considers possible actions that help reach the desired result. Example: Google Maps finding a route to a destination. 4. Utility-Based Agent Chooses the action that provides the best possible outcome according to a utility or preference measure. Example: a navigation system selecting a route using time, traffic, distance, and fuel consumption. 5. Learning Agent Learns from experience and feedback and improves its performance over time. A learning agent commonly contains a learning element, performance element, critic, and problem generator. Example: a recommendation system learning from a user's choices. Agent Main Feature Example Simple Reflex Uses current situation Automatic door Model-Based Uses memory/internal model Robot vacuum Goal-Based Works toward a goal Google Maps Utility-Based Chooses best outcome Smart navigation Learning Agent Learns from experience Recommendation system Introduction to Artificial Intelligence • Page 1 4. AI Environment The environment is everything around an AI agent that the agent can perceive and interact with. • Fully Observable: The agent has access to all necessary information. Example: chess board. • Partially Observable: The agent cannot access all information. Example: self-driving car. • Deterministic: The result of an action is predictable. Example: calculator. • Stochastic: The result of an action involves uncertainty. Example: real-world traffic. • Static: The environment does not change while the agent makes a decision. Example: crossword puzzle. • Dynamic: The environment can change while the agent operates. Example: road traffic. • Discrete: States and actions are countable. Example: chess. • Continuous: States can change continuously. Example: self-driving car. • Single-Agent: Only one agent is involved. Example: crossword puzzle. • Multi-Agent: Multiple agents interact. Example: multiplayer games. A self-driving car operates in a partially observable, stochastic, dynamic, continuous, and multi-agent environment. 5. PEAS Framework PEAS is a framework used to describe the task environment of an intelligent agent. PEAS Meaning Self-Driving Car Example P Performance Measure Safety, speed, comfort, fuel efficiency E Environment Roads, traffic, pedestrians, weather A Actuators Steering, brakes, accelerator S Sensors Cameras, GPS, radar, LiDAR 6. Problem-Solving Agent A problem-solving agent identifies a goal and searches for a sequence of actions that transforms the initial state into the goal state. 1. Initial State: The starting point of the problem. 2. Goal State: The desired final state or objective. 3. Actions: The possible actions the agent can perform. 4. Transition Model: Describes how an action changes one state into another. 5. Evaluation: Measures and compares possible solutions to select the most suitable solution. Example – Route Finding: Initial state = current location; Goal state = destination; Actions = available roads; Transition model = movement between locations; Evaluation = shortest, fastest, or least expensive route. 7. Types of Searching Uninformed Search does not use additional knowledge about which state is closer to the goal. • Breadth-First Search (BFS): Explores nodes level by level. • Depth-First Search (DFS): Explores one branch as deeply as possible before backtracking. • Uniform Cost Search: Expands the path with the lowest total cost. • Depth-Limited Search: DFS with a fixed depth limit. Introduction to Artificial Intelligence • Page 2 • Iterative Deepening Search: Repeatedly performs depth-limited searches with increasing depth limits. Informed Search uses additional knowledge called a heuristic to guide the search. • Greedy Best-First Search: Selects the state that appears closest to the goal. • A* Search: Uses both the cost already travelled and the estimated cost to the goal. A* formula: f(n) = g(n) + h(n), where g(n) is the cost from the initial state, h(n) is the estimated cost to the goal, and f(n) is the total estimated cost. 8. Real-Life Applications of AI • Healthcare: Disease detection, medical image analysis, patient monitoring, and drug discovery. • Education: AI tutors, personalized learning, and automatic assessment. • Finance: Fraud detection, risk analysis, algorithmic trading, and credit assessment. • Transportation: Self-driving vehicles, traffic prediction, and route optimization. • E-Commerce: Product recommendations, chatbots, and personalized advertising. • Entertainment: Movie, music, and video recommendations. • Security: Facial recognition, intrusion detection, and threat detection. • Virtual Assistants: Siri, Alexa, Google Assistant, and AI chatbots. Quick Revision Topic Key Point AI Enables machines to perform intelligent tasks AI Agents Perceive and act in an environment Environment Surroundings of an agent PEAS Performance, Environment, Actuators, Sensors Problem Solving Finds actions from initial state to goal state Uninformed Search BFS, DFS, UCS, DLS, IDS Informed Search Greedy Best-First, A* Applications Healthcare, education, finance, transport, security, etc. Introduction to Artificial Intelligence • Page 3