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AI Agents

AI Agents and Environments_122023A
[AI Agents and Environments - DataDrivenInvestor]
 

 

- Overview

AI agent, short for artificial intelligence agent, refers to an entity embedded in a computer system that simulates human-like intelligence to perform specific tasks. These agents use algorithms, data, and predefined rules to analyze information, make decisions, and perform actions. 

The overall goal is to replicate human cognitive processes and problem-solving abilities, enabling machines to perform tasks autonomously and adapt to changing environments.

Here are some characteristics of AI agents:

  • Autonomy: AI agents can operate without constant human intervention.
  • Reactivity: AI agents can perceive their environment and respond to changes in a timely manner.
  • Proactivity: AI agents can take initiative and perform tasks to achieve their objectives.
  • Social ability: AI agents can communicate with other agents or humans when necessary.

 

Here are some types of agents: 

  • Human agents: Use their senses to perceive their environment and use their limbs and voice to effect changes in the world. Sensory organs include eyes, ears, nose, tongue, and skin, and effector organs include hands, legs, and mouth.
  • Robotic agents: Autonomous machines designed to perform tasks in an environment. They have various types of sensors to collect data from their surroundings, such as cameras and infrared range finders.
  • Learning agents: Can learn from their past experiences or have learning capabilities. They start to act with basic knowledge and then are able to act and adapt automatically through learning.
  • Software agents: Have encoded bit strings as their percepts and actions.
  • Generic agents: Have a general structure of an agent who interacts with the environment.

 

- The Four Rules of AI Agents

An AI agent is anything that can be seen as perceiving its environment through sensors and acting upon it through actuators. It will run in cycles of perceiving, thinking and acting. Agents include humans, robots, softbots, thermostats, etc.

Here are the main four rules that all AI agents must abide by:

  • Rule 1: AI agents must be able to perceive their environment.
  • Rule 2: Environmental observations must be used to make decisions.
  • Rule 3: Decisions should lead to actions.
  • Rule 4: Actions taken by AI agents must be rational. Rational action is action that maximizes performance and produces the best positive outcome.

 

AI agent continuously performs these functions:

  • 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

 

- Intelligent Assistance

Intelligent assistance (IA) refers to the use of intelligent agents to help individuals perform tasks or services. While intelligent agents may look like any other software application that accomplishes a set of coordinated functions, tasks, or activities, they can actually do one or more of the following:

  • Work autonomously 
  • Meet Goals
  • Maintain historical data 
  • Perceive and assist

 

AI machines imitate humans, while IA (intelligent Assistance) machines assist humans. Due to the breadth of research involving both techniques, it is easy to confuse the two. 

AI (Artificial intelligence) is the practice or study of intelligent agents and determining the limits of their intelligence. IA (Intelligent Assistance) is the use of intelligent agents to serve humans, which means that such agents exist within predefined parameters. 

Their intelligence is measured by many factors, including but not limited to: Problem solving, learning, language processing.

 

- Intelligent Agents

In AI, an intelligent agent perceives its environment via sensors and acts rationally upon that environment with its effectors. 

The Properties of an Intelligent Agent: autonomous, reactive to the environment, pro-active (goal-directed), and interacts with other agents via the environment. 

An Intelligent agent (or rational agent) is an agent which acts in a way that is expected to maximize to its performance measure, given the evidence provided by what it perceived and whatever built-in knowledge it has. The performance measure defines the criterion of success for an agent. The above properties of the intelligent agents are often grouped in the term PEAS, which stands for Performance, Environment, Actuators and Sensors.

An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot. 

The concept of intelligent agent is actually central in AI. AI aims to design intelligent agents that are useful, reactive, autonomous, and even social and pro-active.

An ideal agent always chooses the action which maximizes its expected performance, given its percept sequence so far. An autonomous agent uses its own experience rather than built-in knowledge of the environment by the designer.

 

- Rational Agents

In artificial intelligence (AI), a rational agent is a computer program that uses logical reasoning and decision-making to determine its next action. A rational agent is goal-based, assessing its environment and determining how each available action will affect it. The agent then chooses the action that will help it achieve its goal and be most successful. 

Rational agents are closely related to intelligent agents, which are autonomous software programs that display intelligence. Some examples of intelligent agents include: 

  • Online assistants: Siri, Alexa, Google, and Cortana
  • Autonomous driving: Cars, buses, trucks, and drones

A rational agent is different from an omniscient agent. An omniscient agent knows the outcome of its actions and can act accordingly, but perfection is impossible in reality.

 

- Three Forms of Intelligent Agent

AI-enabled agents gather input from the environment by using sensors such as cameras, microphones, or other sensing devices. The agent then performs some real-time computation on the input and provides the output using actuators like screens or speakers. These agents have capabilities such as real-time problem solving, error or success rate analysis, and information retrieval.

Intelligent Agent can come in any of the three forms, such as:-

  • Human-Agent: A Human-Agent use Eyes, Nose, Tongue and other sensory organs as sensors to percept information from the environment and uses limbs and vocal-tract as actuators to perform an action based on the information. 
  • Robotic Agent: Robotics Agent uses cameras and infrared radars as sensors to record information from the Environment and it uses reflex motors as actuators to deliver output back to the environment. 
  • Software Agent: Software Agent use keypad strokes, audio commands as input sensors and display screen as actuators.

 

For Example, AI-based smart assistants like Siri, Alexa. They use voice sensors to receive a request from the user and search for the relevant information in secondary sources without human intervention and actuators like its voice or text module relay information to the environment.  

 

- Rationality

Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment. Rationality is concerned with expected actions and results depending upon what the agent has perceived. 

Performing actions with the aim of obtaining useful information is an important part of rationality. The rationality of the agent is measured by its performance measure, the prior knowledge it has, the environment it can perceive and actions it can perform.  

Rationality of an agent depends on the following four factors: 

  • The performance measures, which determine the degree of success
  • Agent’s Percept Sequence (the history of all that an agent has perceived till date) till now. 
  • The agent’s prior knowledge about the environment. 
  • The actions that the agent can carry out.

 

To satisfy real world use cases, AI itself needs to have a wide spectrum of intelligent agents. This introduces diversity in the types of agents and the environments we have.

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[Chicago, USA]


- Agent Autonomy

An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future. 

  • An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice.
  • An environment can sometimes be completely known in advance.
  • Exploration: sometimes an agent must perform an action to gather information (to increase perception).
  • Autonomy: the capacity to compensate for partial or incorrect prior knowledge (usually by learning).

 

The next generation of computing involves a degree of agency on the behalf of computers. With machine learning, computers have the capacity to take in large amounts of information, make decisions of some sort and act on that; what is called agency. Autonomous systems use information gathered from sensors to make independent decisions and then act on them using corresponding actuators. 

An autonomous agent is a “smart” agent operating on an owner’s behalf but without any interference of that ownership entity. A thermostat is an example of a very simple autonomous agent in that it senses the environment and acts to change the heater. 

An autonomous agent has the capacity to process information within a restricted domain giving it autonomy and then take an action based upon the rules it has been given or learned. 

 

- Single-agent Vs Multu-agent Systems

The main difference between single-agent and multi-agent systems is that single-agent systems are better for well-defined tasks, while multi-agent systems are better for complex tasks that require collaboration:

  • Single-agent systems: These systems are good for cognitive tasks and work well independently. They are like a generalist who can handle multiple duties. Single-agent systems are best for environments where tasks are well-defined and don't require interaction with other agents.
  • Multi-agent systems: These systems are made up of multiple agents that work together to solve problems. Each agent has unique problem-solving skills and communicates with the others to achieve common goals. Multi-agent systems are better for complex tasks that require collaboration, adaptability, and resource sharing.
 
Here are some other differences between single-agent and multi-agent systems:
  • Fault tolerance: Multi-agent systems have built-in redundancy and fault tolerance, so if one agent goes down, the others can keep things running.
  • Efficiency: Multi-agent systems can be more efficient than single-agent systems because they can share learned experiences to optimize time complexity.
  • Scalability: Multi-agent systems can be more scalable than single-agent systems.

 

This collaboration allows multi-agent systems to solve more complex problems and tasks than single-agent systems. Single agent systems require one agent to perform tasks in various domains, whereas each agent in a multi-agent system can hold specific domain expertise.



 

 

 

 

 

 

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