Artificial Intelligence - Intelligent Agents Chapter 2

Study Notes

Intelligent Agents are anything that acts on behalf of a user, using sensors to perceive the environment and effectors to act on it.
A key component is their intelligence, ranging from simple rules to complex learning algorithms.
Agents adapt to environmental changes, not only reacting but often proactively.
They can cooperate with other agents and even migrate to different systems.
They can also communicate with the user, system, and other agents.

The term "Intelligent Agent" is sometimes overused.
An agent perceives its environment through sensors.
It acts on the environment using effectors.
Its goal is to maximize progress.
The concept of an agent as a tool to analyze systems.
Agents are not a definitive division of the world; rather, they're like object-oriented vs. imperative approaches
The notion of an Agent encompasses Percepts, Actions, Goals and the Environment.
Task-specific and specialized agents possess well-defined goals and structured environments.

Collision Avoidance Agent (CAA): Avoids obstacles, perceives distance, velocity and trajectory.
Sensors: Vision, proximity sensing.
Effectors: Steering wheel, accelerator, brakes, horn, headlights.
Actions: Steer, speed up, brake, signal (headlights).
Environment: Freeways.
Lane Keeping Agent (LKA): Stays in lane, perceives lane center and boundaries.
Sensors: Vision.
Effectors: Steering wheel, accelerator, brakes.
Actions: Steer, speed up, brake.
Environment: Freeways.

This section details the performance measures, environments, actuators, and sensors for various agent types.
Examples include Medical diagnosis systems, satellite image analysis systems, part-picking robots, refinery controllers, and interactive English tutors.

Perception, followed by action mapping, is fundamental.
Ideal mapping: Specific actions for a given state.
Performance measure: Subjective assessment (e.g., speed, power use, error rate, cost).
Degree of autonomy: Agent's ability to make decisions independently.

Rational action is the best possible action given the agent's knowledge and abilities.
Rational involves recognizing constraints and limitations.
Rationality is not omniscience, clairvoyance, or guaranteed success.

Agents are autonomous.
Agents have some intelligence (from simple rules to learning algorithms), allowing adaptation.
They are not limited to reactive actions, but can also act proactively.
Agents have some social ability, interacting with users, systems, or other agents.

An agent is composed of an architecture and a program.
The agent program defines the perception-action mapping.
The architecture is the device that executes the agent program (e.g., a computer).

Example of how to use a look-up table for collision avoidance in various scenarios.
The size of the table is determined by the number of possible percepts and actions.

A table-driven agent is useful for agents with limited complexity.
For a given sequence of percepts, use a pre-defined table to determine the correct actions.

Intelligent agents perceive and act on their environments to achieve goals.
Rational actions maximize the expected value of performance measures, given current data.