Agent Overview:
Rational Agents:
AI Agent Structure:
PEAS Representation:
In a broader context, an "agent" denotes a versatile entity or system, embodying the capacity to utilize sensors for comprehensive environmental observation and actuators for purposeful action. This overarching concept transcends specific domains, encompassing diverse scenarios where entities, whether human, animal, or mechanical, engage in a perpetual cycle of perceiving, processing, and responding to information in their surroundings. The term "agent" serves as a dynamic descriptor, applicable to a spectrum of situations where active entities play pivotal roles, emphasizing the active involvement in their environment. This inclusive perspective underscores the adaptability of the term, recognizing its relevance across various contexts and fields.
As a result, the environment around us is replete of agents, including thermostats, cellphones, cameras, and even ourselves.
Before we proceed, we must first understand sensors, effectors, and actuators.
An AI agent is essentially a self-contained entity equipped with sensors and actuators, allowing it to interact autonomously with its environment in pursuit of predefined objectives. This concept draws parallels with the functionality of a thermostat, where the agent continually assesses its surroundings and takes actions to achieve its goals.
The fundamental rules governing an AI agent's behavior can be summarized as follows:
In essence, an AI agent operates as an autonomous, goal-oriented entity that continuously learns from its surroundings, making informed decisions and taking rational actions to fulfill its objectives. This framework provides a structured approach to understanding and designing intelligent agents for various applications.
A rational agent is one who has defined preferences, models uncertainty, and acts in such a way that its performance measure is maximized using all available actions. The proper things are stated to be done by a rational agent. AI is concerned with the development of rational agents for application in game theory and decision theory in a variety of real-world contexts. The rational action is the most crucial for an AI agent because in an AI reinforcement learning algorithm, an agent receives a positive reward for each best feasible action and a negative reward for each incorrect action.
Note: Rational agents in AI are a lot similar to intelligent agents.
The performance metric of an agent is used to determine its rationality. The following criteria can be used to assess rationality:
Note: Rationality varies from Omniscience in that an Omniscient agent understands the actual outcome of its actions and acts appropriately, which is impossible to achieve in reality.
AI's objective is to create an agent program that performs the agent function. The architecture and agent program combine to form the framework of an intelligent agent. It can be summed up as follows:
Agent = Architecture + Agent program
The main three terms involved in the structure of an AI agent are as follows:
f:P* → A
PEAS is a form of model that an AI agent uses to work. We can organize the properties of an AI agent or rational agent under the PEAS representation model when we define it. It consists of four words:
Here performance measure is the objective for the success of an agent's behavior.
If we consider a self-driving car, the PEAS representation will be as follows:
Agent | Performance measure | Environment | Actuators | Sensors |
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1. Medical Diagnose |
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Keyboard (Entry of symptoms) |
2. Vacuum Cleaner |
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3. Part -picking Robot |
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