Artificial Intelligence

Artificial Intelligence #

Artificial Intelligence (AI) is concerned with building systems that can perceive, reason, learn, decide and act in ways that achieve useful goals.

This page introduces the foundations of AI, its major application areas, four classic ways of thinking about intelligence, important milestones in its development, and some of the risks that accompany increasingly capable AI systems.

• Artificial Intelligence: Foundations, Overview of Modern AI & Application Domains.
• What is Artificial Intelligence: Acting Humanly, Thinking humanly, Thinking rationally, Acting Rationally
• Foundations of AI , Brief Overview of Modern AI & Application Domains

Learning Objectives #

• explain what intelligence and artificial intelligence mean in practical terms
• identify the major disciplines that contributed to AI
• distinguish thinking humanly, acting humanly, thinking rationally and acting rationally
• explain the idea behind the Turing Test
• describe why the rational-agent view is central to modern AI
• recognise important application areas and risks of AI

Big Picture #

flowchart TD
    A["Artificial Intelligence"] --> B["Human-like"]
    A --> C["Rational"]
    B --> D["Think like humans"]
    B --> E["Act like humans"]
    C --> F["Think rationally"]
    C --> G["Act rationally"]

    style A fill:#C8E6C9
    style B fill:#E1F5FE
    style C fill:#FFF9C4
    style D fill:#EDE7F6
    style E fill:#EDE7F6
    style F fill:#C8E6C9
    style G fill:#C8E6C9

1. What Is Intelligence? #

Intelligence is not a single ability. It involves several capabilities working together, including:

Intelligent Agents and Environments

Intelligent Agents and Environments #

An intelligent agent connects perception with action. It observes an environment through sensors, uses the information it receives to decide what to do, and affects the environment through actuators.

This page develops the core vocabulary for reasoning about intelligent agents: percepts, actions, rationality, performance measures, PEAS and the different properties an environment can have.

• Intelligent Agents: Notion of Agents and Environments, Rationality
• Nature of Environments, Structure of Agents

Learning Objectives #

• explain the relationship between an agent and its environment
• distinguish sensors, percepts, actions and actuators
• explain percept sequences and the agent function
• describe what makes an agent rational
• distinguish rationality from omniscience
• compare the five basic agent architectures
• specify a task environment using PEAS
• classify environments using standard AI properties
• explain why environment properties affect agent design

Big Picture #

flowchart TD
    E["Environment"] -->|Percepts| S["Sensors"]
    S --> A["Agent"]
    A --> C["Actuators"]
    C -->|Actions| E

    style E fill:#E1F5FE
    style S fill:#FFF9C4
    style A fill:#C8E6C9
    style C fill:#EDE7F6

Think of an agent as a continuous loop: observe → decide → act → observe again. An action may change the environment, so the next observation may be different.