Artificial Intelligence: Understanding Agents and Environments

Explore the fundamental components of AI systems: agents and their environments. Learn how agents—whether human, robotic, or software—interact with their surroundings using sensors and effectors. Get insights into key AI terminology like performance measures, behavior, and percept sequences.

Artificial Intelligence - Agents & Environments

An AI system is composed of two key components: agents and their environment. The agents operate within their environment, and this environment may include other agents as well.

What are Agents and Environments?

An agent is any entity that can sense its environment through sensors and take actions within that environment using effectors.

Human Agent: A human has sensory organs like eyes, ears, nose, tongue, and skin that act as sensors. Hands, legs, and mouth function as effectors, enabling humans to interact with their surroundings.
Robotic Agent: A robot might use cameras and infrared range finders as sensors, while motors and actuators serve as effectors to move or manipulate objects.
Software Agent: In software, agents use encoded bit strings as programs to sense and act within a digital environment.

Key Terminology in AI Agents

Understanding the terminology related to AI agents is crucial:

Performance Measure of Agent: This is the criterion used to evaluate how successfully an agent performs its tasks.
Behavior of Agent: The behavior refers to the actions that an agent performs based on a given sequence of percepts (inputs).
Percept: A percept is the input that an agent receives at any given moment.
Percept Sequence: This is the complete history of all percepts an agent has received up to the current moment.
Agent Function: The agent function maps a sequence of percepts to an action, guiding the agent's behavior.

Understanding Rationality in AI

Rationality in AI refers to the ability to make decisions that are reasonable, sensible, and well-judged based on what the agent has perceived. Rational actions are those that aim to achieve the best possible outcome based on the available information.

What is an Ideal Rational Agent?

An ideal rational agent is one that can perform the actions most likely to maximize its performance, considering:

The percept sequence it has experienced.
Its built-in knowledge base.

The rationality of an agent depends on several factors:

The performance measures that determine the level of success.
The agent's percept sequence so far.
The agent's prior knowledge about its environment.
The actions available to the agent.

A rational agent always takes the "right" action, where the "right" action is the one that leads to the highest success according to the given percept sequence. The problem that an agent solves is characterized by Performance Measure, Environment, Actuators, and Sensors (PEAS).

The Structure of Intelligent Agents

An intelligent agent's structure can be viewed as:

Agent = Architecture + Agent Program

Architecture: This refers to the physical or computational infrastructure on which the agent runs.
Agent Program: This is the software that implements the agent function.

Types of Agents

Simple Reflex Agents

Simple reflex agents select actions based solely on the current percept. They are rational only if the decision-making process is correct based on the current percept. These agents assume that the environment is fully observable.

Condition-Action Rule: This is a rule that maps a specific state (condition) to an action.

Model-Based Reflex Agents

These agents use a model of the world to make decisions. They maintain an internal state that represents the unobservable aspects of the current situation based on the history of percepts.

Updating the internal state requires knowledge of:

How the world evolves.
How the agent's actions affect the world.

Goal-Based Agents

Goal-based agents choose their actions to achieve specific goals. This approach is more flexible than simple reflex agents because the decision-making process is explicitly modeled, allowing for easier modification.

Goal: A goal is a description of a desired outcome or state.

Utility-Based Agents

Utility-based agents make decisions based on a preference (utility) for each possible state. This approach is useful when:

There are conflicting goals, and only some can be achieved.
There is uncertainty in achieving the goals, requiring a balance between the likelihood of success and the importance of the goal.

The Nature of Environments in AI

AI programs can operate in a wide variety of environments, ranging from entirely artificial, like those confined to keyboard input and screen output, to more complex environments where software agents (softbots) interact in real-time.

For example, a softbot designed to scan online customer preferences and display relevant items operates in both real and artificial environments.

The Turing Test Environment

The most famous artificial environment is the Turing Test environment. In this test, both human and AI agents are tested under the same conditions to determine if the AI can mimic human intelligence convincingly.

In this test:

Two people and one machine participate. One person acts as the tester, while the other two (one human and one machine) answer the tester's questions.
The tester interacts with both the human and the machine without knowing which is which.
If the tester cannot reliably tell the machine's responses from the human's, the machine is considered to exhibit intelligent behavior.

Properties of AI Environments

Environments in which AI agents operate have various properties:

Discrete / Continuous: In a discrete environment, there are a limited number of clearly defined states (e.g., chess). In a continuous environment, the states are not as clearly defined (e.g., driving).
Observable / Partially Observable: If an agent can determine the complete state of the environment at any given time from its percepts, the environment is observable. If not, it is only partially observable.
Static / Dynamic: If the environment remains unchanged while the agent is acting, it is static. If it changes, it is dynamic.
Single Agent / Multiple Agents: The environment may contain one or multiple agents, either of the same or different types.
Accessible / Inaccessible: If an agent can access the complete state of the environment through its sensors, the environment is accessible. Otherwise, it is inaccessible.
Deterministic / Non-Deterministic: If the next state of the environment is completely determined by the current state and the agent's actions, the environment is deterministic. Otherwise, it is non-deterministic.
Episodic / Non-Episodic: In an episodic environment, each episode consists of the agent perceiving and then acting, with the quality of its action depending only on that episode. Subsequent episodes are independent of previous ones. Episodic environments are simpler because the agent does not need to consider future consequences.