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.

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.

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. Agent
2. Explore (all possible solutions)
3. Environment
4. Sensor → i/p → Percept
5. Actuator
6. Action
7. Initial State: Source
8. Transition Model
9. Random-isation
10. Learned Info → FACT → Knowledge Base
11. Backtracking

• State Space Transition Diagram / Search Tree
• Performance Measure → Numerical Measure (Minimise or Maximise)

M2 → Search M4 → Game M5 → Knowledge → Fact → KB → Inference

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.

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

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
• 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.