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Intelligent Agent images, Intelligent Agent - Coggle Diagram
Intelligent Agent 
Rational Agent
A rational agent in AI is an agent that performs actions to achieve the best possible outcome based on its perceptions and knowledge. It operates under the premise of rationality, where it consistently makes decisions that maximize its expected utility or performance measure.
Rationality
- Information gathering
- Exploration
- learning
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Omniscience
- Known the actual outcomes of its action
- Can make optimal decision in any solution
Agent function
Agent program
- It is a real world implementation of the agent function
- It define the behavior of the agent
- It refers to the theoretical, abstract functions
Actuator
Software sensor
- Algorithms
- Computational models
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- Physical devices
- Receive electrical signals and translate them in to physical action
- Act as the output or effector of a system
Ex: Robotic arms, robot, electrical motors
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Environment
Fully observable
- Agent's sensors give it access to the complete state of the environment
Partially observable
- Where an agent's sensors provide only partial information about the environment states
(Unobservable) - relevance
- Agent can make decision based on a complete understanding of the world
- The agent needs to maintain some internal state to keep track
Single agent
- System consists single agent
- Operate autonomously to solve problems
Multi Agent
- Agents that interact and coordinate with each other to achieve common goals
Competitive
- Agent have non aligned goals
Ex: play games like poker
Cooperative
- Work together to achieve a shared objective
Ex: team of AI agents work together to solve problems
Deterministic
-Operate with fixed input values
-The future can be precisely determined based on the current stateStochastic
- incorporate randomness
- Use random values
Uncertain outcomes
- Single definitive outcome
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Episodic
-Series of one short actionSequential
- The current action can affect all future actions
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Static
-The environment doesn't change while the agent is deciding on an action
Ex: cleaning a room by a dry cleaner robotDynamic
- The environment changes while the agent is talking action
Semi dynamic
- The environment may not change with time, but the agent's performance score does
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Discreate
- There are a limited numbers of possible actions, states
-variables are measured only specific points
Continuous
- Variables can take any values within a given range
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Known
- Agent can understand of the environment work include the potential actions
- Agent known the rules of the environment
Unknown
- Agent has limited knowledge of environment works
- Agent doesn't know the rules of the environment
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P.E.A.S
Measure performance
- It defines the success of an agent. It evaluates the criteria that determines whether the system performs well.
Ex: Safe, fast, comfortable
Environment
- It refers to the external context in which an AI system operates. It encapsulates the physical and virtual surroundings, including other agents, objects, and conditions.
Ex: Roads, customers
Actuators
- They are responsible for executing actions based on the decisions made. They interact with the environment to bring about desired changes.
Ex: Brake, signal, hone
Sensors
- An agent observes and perceives its environment through sensors. Sensors provide input data to the system, enabling it to make informed decisions.
Ex: Engine sensors, cameras, GPS, sonar
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An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals.
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