Multi agent Systems MCQ Quiz - Objective Question with Answer for Multi agent Systems - Download Free PDF

The correct answer is Between 0 and 1.

Key Points

• In fuzzy set theory, the membership value (or degree of membership) of an element in a fuzzy set is always a number between 0 and 1, inclusive.
• A membership value of 0 indicates no membership, while a value of 1 indicates full membership.
• Values between 0 and 1 represent partial membership, reflecting the element's degree of belonging to the set.

Additional Information

• Option 1: Always 0 - This is incorrect because a membership value of 0 indicates that the element does not belong to the fuzzy set at all, but membership values in a fuzzy set can vary.
• Option 2: Always 1 - This is incorrect because a membership value of 1 indicates that the element fully belongs to the fuzzy set, but membership values in a fuzzy set can vary.
• Option 4: Greater than 1 - This is incorrect as membership values in fuzzy sets are defined to be within the range [0, 1].

The correct answer is Learning

Key Points

• Learning is a crucial aspect of AI, and it involves the ability of an AI agent to adapt and improve its performance over time by learning from previous states, experiences, and data.
• By saving information from past interactions, an AI agent can make better-informed decisions when faced with similar situations in the future.
• This capability allows the AI agent to continuously refine its understanding and enhance its performance over time, making it more effective in addressing various tasks or challenges.

Explanation:

Simple-Based Agent:

• These agents make judgements based on current perceptions while ignoring previous perceptions. These agents only succeed in creating a fully visible world.
• During their decision and action process, the Simple reflex agent does not evaluate any aspect of perceptual history.
• This agent follows the Condition-action rule, which means that it maps the current state to action.
• It chooses an action purely based on the current condition, ignoring the perceptual past.

 

Model-Based Agent:

• Model-based reflex agents are designed to deal with partial accessibility by keeping track of the area of the world that can be seen right now.
• It accomplishes this by maintaining an internal state that is dependent on what it has seen previously, allowing it to store information about the unseen features of the current state.

Goal-Based Agent:

• A goal-based agent is an AI system that is programmed to accomplish a specified task.
• The goal can range from exploring a complex to playing a game. A goal-based agent, given a plan, strives to select the optimal approach to achieve it depending on the environment.

Utility-Based Agent:

• A utility-based agent is one that acts not just on what the goal is, but also on the optimal approach to achieve that goal.
• In short, the agent's usefulness (or utility) distinguishes it from its equivalents.

Explanation:

Simple-Based Agent:

• These agents make judgements based on current perceptions while ignoring previous perceptions. These agents only succeed in creating a fully visible world.
• During their decision and action process, the Simple reflex agent does not evaluate any aspect of perceptual history.
• This agent follows the Condition-action rule, which means that it maps the current state to action.
• It chooses an action purely based on the current condition, ignoring the perceptual past.

 

Model-Based Agent:

• Model-based reflex agents are designed to deal with partial accessibility by keeping track of the area of the world that can be seen right now.
• It accomplishes this by maintaining an internal state that is dependent on what it has seen previously, allowing it to store information about the unseen features of the current state.

Goal-Based Agent:

• A goal-based agent is an AI system that is programmed to accomplish a specified task.
• The goal can range from exploring a complex to playing a game. A goal-based agent, given a plan, strives to select the optimal approach to achieve it depending on the environment.

Utility-Based Agent:

• A utility-based agent is one that acts not just on what the goal is, but also on the optimal approach to achieve that goal.
• In short, the agent's usefulness (or utility) distinguishes it from its equivalents.

The correct answer is option 1.

Artificial Intelligence system is the composed of agent and its environment. Agent takes input from its Environment through sensor and sends its reaction to environment through actuator.

 

Simple Reflex agent : take decisions on the basis of the current environment and ignore the history.

Model-based reflex agent : work partially in the observable environment, and track the situation.

Goal-based agents :  needs to know its goal that describes advisable situations.

Utility-based agents : acts based not only goal but also the best possible way to achieve it.

Learning Agents : learn from its past experiences, or it has learning capabilities.

The correct answer is option 1.

Artificial Intelligence system is the composed of agent and its environment. Agent takes input from its Environment through sensor and sends its reaction to environment through actuator.

 

Simple Reflex agent : take decisions on the basis of the current environment and ignore the history.

Model-based reflex agent : work partially in the observable environment, and track the situation.

Goal-based agents :  needs to know its goal that describes advisable situations.

Utility-based agents : acts based not only goal but also the best possible way to achieve it.

Learning Agents : learn from its past experiences, or it has learning capabilities.

Concept:

Agent: An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Example: human agents, robotic agents etc.

Agent is also equal to architecture and program combination.

Explanation:

Factors that are considered in agents are its performance measure, environment, actuators, sensors.

• Performance: safe, fast, legal, maximize profits.
• Environment: Roads, pedestrians, customers
• Actuators: steering wheel, accelerator, brake.
• Sensors: Cameras, speedometer, GPS, keyboard.

Environment types: fully observable, deterministic, static, dynamic, single agent, semi dynamic.

Agents observes through sensor and directs activity towards achieving goals. Agents improves its performance by learning to achieve its goals. A reflex machine is an example of an intelligent agent. AI agent must have ability to perceive the environment.

Knowbots: (Knowledge Bases Object Technology) It is a kind of agent that collects information by automatically gathering specified information from websites.

Softbots: (Software Bots) It is a kind of agent that acts and instructs on behalf of a user or a software.

• There is no such thing as transagents or blizzards.

Concept:

Agent: An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Example: human agents, robotic agents etc.

Agent is also equal to architecture and program combination.

Explanation:

Factors that are considered in agents are its performance measure, environment, actuators, sensors.

• Performance: safe, fast, legal, maximize profits.
• Environment: Roads, pedestrians, customers
• Actuators: steering wheel, accelerator, brake.
• Sensors: Cameras, speedometer, GPS, keyboard.

Environment types: fully observable, deterministic, static, dynamic, single agent, semi dynamic.

• Fully observable: It means to check everything is available to it via its sensors.
• Deterministic: It depends only on current state and agent’s action.
• Static: agent is deliberating over what to do
• Dynamic: agent should consult the world when choosing actions

Semi-dynamic: If the environment itself does not change with the passage of time but the agent’s performance does.

The correct answer is Learning

Key Points

• Learning is a crucial aspect of AI, and it involves the ability of an AI agent to adapt and improve its performance over time by learning from previous states, experiences, and data.
• By saving information from past interactions, an AI agent can make better-informed decisions when faced with similar situations in the future.
• This capability allows the AI agent to continuously refine its understanding and enhance its performance over time, making it more effective in addressing various tasks or challenges.

The correct answer is option 1.

Artificial Intelligence system is the composed of agent and its environment. Agent takes input from its Environment through sensor and sends its reaction to environment through actuator.

 

Simple Reflex agent : take decisions on the basis of the current environment and ignore the history.

Model-based reflex agent : work partially in the observable environment, and track the situation.

Goal-based agents :  needs to know its goal that describes advisable situations.

Utility-based agents : acts based not only goal but also the best possible way to achieve it.

Learning Agents : learn from its past experiences, or it has learning capabilities.

Concept:

Agent: An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Example: human agents, robotic agents etc.

Agent is also equal to architecture and program combination.

Explanation:

Factors that are considered in agents are its performance measure, environment, actuators, sensors.

• Performance: safe, fast, legal, maximize profits.
• Environment: Roads, pedestrians, customers
• Actuators: steering wheel, accelerator, brake.
• Sensors: Cameras, speedometer, GPS, keyboard.

Environment types: fully observable, deterministic, static, dynamic, single agent, semi dynamic.

Agents observes through sensor and directs activity towards achieving goals. Agents improves its performance by learning to achieve its goals. A reflex machine is an example of an intelligent agent. AI agent must have ability to perceive the environment.

Explanation:

Simple-Based Agent:

• These agents make judgements based on current perceptions while ignoring previous perceptions. These agents only succeed in creating a fully visible world.
• During their decision and action process, the Simple reflex agent does not evaluate any aspect of perceptual history.
• This agent follows the Condition-action rule, which means that it maps the current state to action.
• It chooses an action purely based on the current condition, ignoring the perceptual past.

 

Model-Based Agent:

• Model-based reflex agents are designed to deal with partial accessibility by keeping track of the area of the world that can be seen right now.
• It accomplishes this by maintaining an internal state that is dependent on what it has seen previously, allowing it to store information about the unseen features of the current state.

Goal-Based Agent:

• A goal-based agent is an AI system that is programmed to accomplish a specified task.
• The goal can range from exploring a complex to playing a game. A goal-based agent, given a plan, strives to select the optimal approach to achieve it depending on the environment.

Utility-Based Agent:

• A utility-based agent is one that acts not just on what the goal is, but also on the optimal approach to achieve that goal.
• In short, the agent's usefulness (or utility) distinguishes it from its equivalents.

Soft computing is an emerging approach to computing that gives the remarkable ability of the human mind to argue and learn in the atmosphere of uncertainty and distrust.

Soft computing is based on some biological induced methods such as genetics, development, ant behaviour, the warm of particles, the human nervous system, etc.

Core of soft computing is Fuzzy Computing, Neural Computing, Genetic Algorithms

Fuzzy logic:

• Fuzzy logic is a concept of certain degree. Boolean logic is a subset of fuzzy logic.
• Fuzzy logic is a form of many-valued logic which deals with reasoning that is approximate rather than fixed and exact.
• Compared to traditional binary sets (where variables may take on true or false values), fuzzy logic variables may have a truth value that ranges in degree between 0 and 1.
• It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.

 

Following is the sequence for the designing a fuzzy logic machine:

Fuzzification → Rule evaluation → Defuzzification

When designing a fuzzy logic, we first have to define the fuzzy sets and make appropriate member function. The rule evaluation comes in which matches the sets to its corresponding rules.