Podcast
Questions and Answers
Which of the following are considered sensors for a human agent?
Which of the following are considered sensors for a human agent?
- Hands
- Ears (correct)
- Mouth
- Eyes (correct)
- Legs
Which of the following are considered actuators for a robotic agent?
Which of the following are considered actuators for a robotic agent?
- Cameras
- Joint angle sensors
- Infrared range finders
- Motors (correct)
What is the purpose of the agent function in an agent-based system?
What is the purpose of the agent function in an agent-based system?
The agent function maps from percepts to actions. It defines the agent's behavior by determining the action to take based on the current perception of the environment.
What are the components of a PEAS model?
What are the components of a PEAS model?
Which of the following is NOT a component of the PEAS model?
Which of the following is NOT a component of the PEAS model?
Which of the following is NOT considered an actuator in an automated Taxi Driver?
Which of the following is NOT considered an actuator in an automated Taxi Driver?
Which of the following is NOT considered a sensor in Part-picking robot?
Which of the following is NOT considered a sensor in Part-picking robot?
A rational agent always chooses the action that will maximize its actual outcome.
A rational agent always chooses the action that will maximize its actual outcome.
According to the lecture, what is the ideal level of autonomy for an agent?
According to the lecture, what is the ideal level of autonomy for an agent?
How many sensors are mentioned for the Interactive English tutor in the PEAS model?
How many sensors are mentioned for the Interactive English tutor in the PEAS model?
Match the following agents with their respective PEAS components:
Match the following agents with their respective PEAS components:
What is the difference between a rational agent and a perfect agent?
What is the difference between a rational agent and a perfect agent?
Flashcards
Agent
Agent
Anything that perceives its environment through sensors and acts upon it using actuators.
Agent Function
Agent Function
Maps percept histories to actions.
Agent Program
Agent Program
The program that runs on the architecture to produce the agent function.
Rational Agent
Rational Agent
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Rationality
Rationality
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PEAS
PEAS
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Percepts
Percepts
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Actions
Actions
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Performance Measure
Performance Measure
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Environment
Environment
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Actuators
Actuators
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Sensors
Sensors
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Autonomous Agent
Autonomous Agent
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Vacuum-cleaner world
Vacuum-cleaner world
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Look-up table
Look-up table
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Percept History
Percept History
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Architecture
Architecture
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Agent-based system
Agent-based system
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Study Notes
Agent-Based Systems Lecture 1
- Lecture presented by Professor Dr. Magdy Zakarya and Dr. Dina Saif
- Topics covered include agents and environments, rationality, PEAS (Performance measure, Environment, Actuators, Sensors), environmental types, and agent types
- Agents are entities that perceive their environment through sensors and act upon it through actuators
- Example human agents have eyes, ears, hands, and legs as sensors and actuators
- Example robotic agents use cameras and infrared range finders as sensors and various motors as actuators
- The agent function maps from percept histories to actions. The notation describes this as [f: P* → A]
- The agent program runs on the physical architecture to produce the function.
- Agent = architecture + program
- Vacuum-cleaner world example with percepts (location and contents), actions (Left, Right, Stuck, NoOp), and the agent using a lookup table.
Rational Agents
- Rationality is measured by success
- Agents' prior knowledge of the environment
- Actions the agent can perform
- The agent's percept sequence to date
- A rational agent selects an action to maximize its performance measure based on percept evidence and built-in knowledge
Rationality and its Differences
- Rationality differs from perfection, as it maximizes expected rather than actual outcomes
- Percepts in situations might not be comprehensive, such as in games of cards or in situations where knowledge of other agents is unknown
- A rational agent chooses the most likely optimal action given the available data and knowledge
Autonomy in Agents
- An agent's autonomy is the extent to which its behavior is determined by experience, not the designer's knowledge.
- Extremes of autonomy include having no autonomy (the environment/information is ignored) and complete autonomy (acting randomly/no program).
- A desirable middle ground for autonomous agents exists.
PEAS
- PEAS (Performance measure, Environment, Actuators, Sensors): a framework to define intelligent agent designs
- Specific examples given include:
- Automated Taxi Driver:
- Performance: Safe, fast, legal, comfortable trip, maximizing profit
- Environment: Roads, other traffic, pedestrians, and customers
- Actuators: Steering wheel, accelerator, brake, signals, and horn
- Sensors: Cameras, sonar, speedometer, GPS, odometer, and engine sensors
- Part-picking Robot:
- Performance: Percentage of parts in correct bins
- Environment: Conveyor belt with parts, and bins
- Actuators: Jointed arm and hand
- Sensors: Camera, and joint angle sensors
- Interactive English Tutor:
- Performance: Maximizing student's test scores
- Environment: Set of students
- Actuators: Screen displays (exercises, suggestions, corrections)
- Sensors: Keyboard and microphone
- Automated Taxi Driver:
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