1..Explain the structure and types of intelligent agents with suitable diagrams and examples.
An intelligent agent is an entity that perceives its environment through sensors and acts upon
it using actuators to achieve specific goals or tasks, often incorporating learning and
adaptation capabilities.
These are the main four rules all AI agents must adhere to:
• Rule 1: An AI agent must be able to perceive the environment.
• Rule 2: The environmental observations must be used to make decisions.
• Rule 3: The decisions should result in action.
• Rule 4: The action taken by the AI agent must be a rational. Rational actions are actions that
maximize performance and yield the best positive outcome.
The Functions of an Artificial Intelligence Agent
• Perceiving dynamic conditions in the environment
• Acting to affect conditions in the environment
• Using reasoning to interpret perceptions
• Problem-solving
• Drawing inferences
• Determining actions and their outcomes
The Number and Types of Agents in Artificial Intelligence
There are five different types of intelligent agents used in AI. They are defined by their range of
capabilities and intelligence level:
, • Reflex Agents: These agents work here and now and ignore the past. They respond using the
event-condition-action rule. The ECA rule applies when a user initiates an event, and the Agent
turns to a list of pre-set conditions and rules, resulting in pre-programmed outcomes.
• Model-based Agents: These agents choose their actions like reflex agents do, but they have a
better comprehensive view of the environment. An environmental model is programmed into the
internal system, incorporating into the Agent's history.
• Goal-based agents: These agents build on the information that a model-based agent stores by
augmenting it with goal information or data regarding desirable outcomes and situations.
• Utility-based agents: These are comparable to the goal-based agents, except they offer an extra
utility measurement. This measurement rates each possible scenario based on the desired result
and selects the action that maximizes the outcome. Rating criteria examples include variables such
as success probability or the number of resources required.
• Learning agents: These agents employ an additional learning element to gradually improve and
become more knowledgeable over time about an environment. The learning element uses feedback
to decide how the performance elements should be gradually changed to show improvement.
• The Structure of Agents in Artificial Intelligence
Agents in Artificial Intelligence follow this simple structural formula:
Architecture + Agent Program = Agent
These are the terms most associated with agent structure:
• Architecture: This is the machinery or platform that executes the agent.
• Agent Function: The agent function maps a precept to the Action, represented by the following
formula: f:P* - A
• Agent Program: The agent program is an implementation of the agent function. The agent program
produces function f by executing on the physical architecture.
Many AI Agents use the PEAS model in their structure. PEAS is an acronym for Performance
Measure, Environment, Actuators, and Sensors. For instance, take a vacuum cleaner.
• Performance: Cleanliness and efficiency
• Environment: Rug, hardwood floor, living room
• Actuator: Brushes, wheels, vacuum bag
• Sensors: Dirt detection sensor, bump sensor
Here’s a diagram that illustrates the structure of a utility-based agent, courtesy of Researchgate.net
2…Describe in detail the Breadth-First Search and Depth-First Search algorithms with
their advantages, disadvantages, and complexity analysis
An intelligent agent is an entity that perceives its environment through sensors and acts upon
it using actuators to achieve specific goals or tasks, often incorporating learning and
adaptation capabilities.
These are the main four rules all AI agents must adhere to:
• Rule 1: An AI agent must be able to perceive the environment.
• Rule 2: The environmental observations must be used to make decisions.
• Rule 3: The decisions should result in action.
• Rule 4: The action taken by the AI agent must be a rational. Rational actions are actions that
maximize performance and yield the best positive outcome.
The Functions of an Artificial Intelligence Agent
• Perceiving dynamic conditions in the environment
• Acting to affect conditions in the environment
• Using reasoning to interpret perceptions
• Problem-solving
• Drawing inferences
• Determining actions and their outcomes
The Number and Types of Agents in Artificial Intelligence
There are five different types of intelligent agents used in AI. They are defined by their range of
capabilities and intelligence level:
, • Reflex Agents: These agents work here and now and ignore the past. They respond using the
event-condition-action rule. The ECA rule applies when a user initiates an event, and the Agent
turns to a list of pre-set conditions and rules, resulting in pre-programmed outcomes.
• Model-based Agents: These agents choose their actions like reflex agents do, but they have a
better comprehensive view of the environment. An environmental model is programmed into the
internal system, incorporating into the Agent's history.
• Goal-based agents: These agents build on the information that a model-based agent stores by
augmenting it with goal information or data regarding desirable outcomes and situations.
• Utility-based agents: These are comparable to the goal-based agents, except they offer an extra
utility measurement. This measurement rates each possible scenario based on the desired result
and selects the action that maximizes the outcome. Rating criteria examples include variables such
as success probability or the number of resources required.
• Learning agents: These agents employ an additional learning element to gradually improve and
become more knowledgeable over time about an environment. The learning element uses feedback
to decide how the performance elements should be gradually changed to show improvement.
• The Structure of Agents in Artificial Intelligence
Agents in Artificial Intelligence follow this simple structural formula:
Architecture + Agent Program = Agent
These are the terms most associated with agent structure:
• Architecture: This is the machinery or platform that executes the agent.
• Agent Function: The agent function maps a precept to the Action, represented by the following
formula: f:P* - A
• Agent Program: The agent program is an implementation of the agent function. The agent program
produces function f by executing on the physical architecture.
Many AI Agents use the PEAS model in their structure. PEAS is an acronym for Performance
Measure, Environment, Actuators, and Sensors. For instance, take a vacuum cleaner.
• Performance: Cleanliness and efficiency
• Environment: Rug, hardwood floor, living room
• Actuator: Brushes, wheels, vacuum bag
• Sensors: Dirt detection sensor, bump sensor
Here’s a diagram that illustrates the structure of a utility-based agent, courtesy of Researchgate.net
2…Describe in detail the Breadth-First Search and Depth-First Search algorithms with
their advantages, disadvantages, and complexity analysis
