Artificial Intelligence

Mon, 01 Jan 0001 00:00:00 +0000

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

Agent
Explore (all possible solutions)
Environment
Sensor → i/p → Percept
Actuator
Action
Initial State: Source
Transition Model
Random-isation
Learned Info → FACT → Knowledge Base
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

Mon, 01 Jan 0001 00:00:00 +0000

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.

Artificial and Computational Intelligence on Arshad Siddiqui

Artificial Intelligence

Artificial Intelligence #

Learning Objectives #

Big Picture #

Intelligent Agents and Environments

Intelligent Agents and Environments #

Learning Objectives #

Big Picture #