Agents and Environments

An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents.

What are Agent and Environment?

An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.

• A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors.
• A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.
• A software agent has encoded bit strings as its programs and actions.

Agent Terminology

• Performance Measure of Agent − It is the criteria, which determines how successful an agent is.
• Behavior of Agent − It is the action that agent performs after any given sequence of percepts.
• Percept − It is agents' perceptual inputs at a given instance.
• Percept Sequence − It is the history of all that an agent has perceived till date.
• Agent Function − It is a map from the precept sequence to an action.

Rationality

Rationality is nothing but the status of being reasonable, sensible, and having a good sense of judgment.

Rationality is concerned with expected actions and results depending upon what the agent has perceived. Performing actions with the aim of obtaining useful information is an important part of rationality.

What is an Ideal Rational Agent?

An ideal rational agent is the one, which is capable of doing expected actions to maximize its performance measure, on the basis of −

• Its percept sequence
• Its built-in knowledge base

Rationality of an agent depends on the following −

• The performance measures, which determine the degree of success.
• Agents Percept Sequence till now.
• The agent's prior knowledge about the environment.
• The actions that the agent can carry out.

A rational agent always performs the right action, where the right action means the action that causes the agent to be most successful in the given percept sequence. The problem the agent solves is characterized by Performance Measure, Environment, Actuators, and Sensors (PEAS).

The Structure of Intelligent Agents

Agents structure can be viewed as −

• Agent = Architecture + Agent Program
• Architecture = the machinery that an agent executes on.
• Agent Program = an implementation of an agent function.

Simple Reflex Agents

• They choose actions only based on the current percept.
• They are rational only if a correct decision is made only on the basis of current precept.
• Their environment is completely observable.

Condition-Action Rule − It is a rule that maps a state (condition) to an action.

Model Based Reflex Agents

They use a model of the world to choose their actions. They maintain an internal state.

Model − knowledge about how things happen in the world.

Internal State − It is a representation of unobserved aspects of current state depending on percept history.

Updating the state requires the information about −

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

Goal Based Agents

They choose their actions in order to achieve goals. Goal-based approach is more flexible than reflex agent since the knowledge supporting a decision is explicitly modeled, thereby allowing for modifications.

Goal − It is the description of desirable situations.

Utility Based Agents

They choose actions based on a preference (utility) for each state.

Goals are inadequate when −

• There are conflicting goals, out of which only few can be achieved.
• Goals have some uncertainty of being achieved and you need to weigh the likelihood of success against the importance of a goal.