Artificial Intelligence Fundamentals: Concepts, History, and Agent Types
Understanding Artificial Intelligence (AI)
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are programmed to think, learn, and make decisions. AI systems can perform tasks that typically require human intelligence, such as recognizing speech, understanding language, solving problems, and making predictions.
Main Categories of AI
AI can be categorized in two primary ways, often based on their capabilities:
I. AI Based on Capabilities
Narrow AI (Weak AI)
Designed for a specific task, excelling in one area without broader cognitive abilities.
Examples: Siri, Google Assistant, facial recognition systems, recommendation engines.
General AI (Strong AI)
Can perform any intellectual task a human can, possessing human-like cognitive abilities across various domains.
Still theoretical and not yet achieved.
Super AI
A hypothetical AI that surpasses human intelligence in virtually every field, including creativity, general wisdom, and problem-solving.
Not developed yet; often seen in science fiction.
A Brief History of Artificial Intelligence
The history of Artificial Intelligence (AI) dates back to the mid-20th century. Here’s a brief timeline of its evolution:
Early Concepts (Before 1950s)
- Philosophers and mathematicians like Alan Turing discussed machines that could simulate human reasoning.
- In 1950, Turing introduced the famous Turing Test to assess machine intelligence.
Birth of AI (1956)
- The term “Artificial Intelligence” was first coined by John McCarthy at the Dartmouth Conference.
- This event marked the official beginning of AI as a dedicated research field.
Growth and Hype (1950s–1970s)
- Early AI programs could solve math problems and play games like chess.
- Expectations were high, but limited computing power and data led to slow progress.
AI Winters (1970s & 1980s)
- Funding and interest significantly dropped due to unmet expectations and over-promising.
- These periods of low AI development and reduced investment are known as “AI winters.”
Revival and Expert Systems (1980s)
- AI experienced a resurgence with the development of expert systems, which mimicked human decision-making processes.
- These systems found applications in medicine, engineering, and business.
Modern AI Boom (2000s–Present)
- Major advances in machine learning, deep learning, and the availability of big data fueled rapid progress.
- AI now powers technologies like smartphones, virtual assistants, self-driving cars, and advanced analytics.
Understanding the PEAS Framework in AI
The PEAS framework is a model used to define the structure and function of an intelligent agent. It helps in designing AI systems by identifying what they need to operate successfully.
PEAS stands for:
- P – Performance Measure
- E – Environment
- A – Actuators
- S – Sensors
Explanation of Each PEAS Term
- Performance Measure
- The standard for evaluating the agent’s success. This defines the goal or objective the agent aims to achieve.
- Environment
- The surrounding world in which the agent operates and interacts.
- Actuators
- The components of the agent that carry out actions and affect the environment.
- Sensors
- The tools the agent uses to receive information (percepts) from its environment.
PEAS Example: Self-Driving Car
- Performance Measure
- Safe driving, fast travel, obeying traffic laws, passenger comfort.
- Environment
- Roads, traffic, pedestrians, weather conditions, road signs, other vehicles.
- Actuators
- Steering wheel, accelerator, brakes, signal lights, horn.
- Sensors
- Cameras, GPS, radar, lidar, speedometer, odometers, ultrasonic sensors.
Types of AI Agent Programs
Intelligent agents can be designed with different levels of complexity and reasoning capabilities. Here are various types of agent programs with examples:
Simple Reflex Agent
Definition:
This agent selects actions based only on the current percept (input from the environment), ignoring the rest of the percept history. It operates using a set of condition-action rules (if-then rules).
Example: Automatic Room Light System
- If the room is dark → Turn on the light
- If the room is bright → Turn off the light
This agent does not consider what happened before or what might happen next; it simply reacts to the immediate situation.
Model-Based Reflex Agent
Definition:
This agent uses an internal model of the world to keep track of the parts of the environment it cannot currently perceive. It can handle more complex environments by maintaining an internal state based on percept history.
Example: Vacuum Cleaner Robot
- Keeps track of which parts of the floor are clean or dirty in its internal map.
- Uses sensors and memory to decide where to go next, not just reacting to immediate dirt.
It doesn’t just react but also remembers past actions and percepts to make more informed decisions.
Goal-Based Agent
Definition:
This agent acts to achieve a specific goal. It uses search and planning algorithms to find sequences of actions that lead to the desired outcome.
Example: GPS Navigation System
- Goal: Get from point A to point B efficiently.
- Uses map data and algorithms to plan the best route.
- Adjusts the plan dynamically if traffic conditions or other environmental factors change.
AI Agents and Their Environments
What is an AI Agent?
An agent is anything that can perceive its environment through sensors and act upon that environment through actuators.
Formal Definition: An agent is a system that perceives its environment and takes actions to achieve a specific goal.
What is an Environment in AI?
The environment is everything that surrounds the agent and with which the agent interacts. It provides inputs (percepts) to the agent and is affected by the agent’s actions.
Agent–Environment Interaction Loop
The agent operates in a continuous loop, interacting with its environment:
- Perceive: The agent receives information (percepts) from the environment through its sensors.
- Decide: Based on its internal program and current percepts, the agent determines what action to take.
- Act: The agent performs an action on the environment through its actuators.
- Repeat: This cycle continuously repeats, allowing the agent to adapt and achieve its goals.
Example: Vacuum Cleaner Agent
- Agent:
- The vacuum cleaner robot
- Environment:
- A room with dust, obstacles, and furniture
- Sensors:
- Dirt sensors, bump sensors, cliff sensors, proximity sensors
- Actuators:
- Wheels (for movement), vacuum motor (for cleaning), brushes
- Percept:
- “I detect dirt in front of me” or “I’ve bumped into an obstacle”
- Action:
- “Move forward and clean the spot” or “Turn and move away from the obstacle”