This document discusses different types of agents in artificial intelligence. It defines an agent as anything that can perceive its environment through sensors and act upon the environment through actuators. The document outlines 5 types of agents: 1) Simple reflex agents that act only based on current percepts; 2) Model-based reflex agents that maintain an internal model of the world; 3) Goal-based agents that take actions to reduce distance from a goal; 4) Utility-based agents that choose actions to maximize expected utility; and 5) Learning agents that can improve through learning from experiences.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Operates in an environment
Perceive its environment through sensors
Acts upon its environment through actuators/ effectors
Has Goals
Abstract: This workship introduces basic concepts of Bayes Theorem. Concepts covered are difference between independent and conditional probabilities, Bayes formulaes and examples.
Level: Fundamental
Requirements: No prior programming or statistics knowledge is required.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators
Operates in an environment
Perceive its environment through sensors
Acts upon its environment through actuators/ effectors
Has Goals
Abstract: This workship introduces basic concepts of Bayes Theorem. Concepts covered are difference between independent and conditional probabilities, Bayes formulaes and examples.
Level: Fundamental
Requirements: No prior programming or statistics knowledge is required.
An intelligent agent perceives its environment via sensors and acts upon that environment with its effectors.
A discrete agent receives percepts one at a time, and maps this percept sequence to a sequence of discrete actions.
Properties
Autonomous
Reactive to the environment
Pro-active (goal-directed)
Interacts with other agents
via the environment
Humans
Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction), neuromuscular system (proprioception)
Percepts:
At the lowest level – electrical signals from these sensors
After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …
Effectors: limbs, digits, eyes, tongue, …
Actions: lift a finger, turn left, walk, run, carry an object, …
The Point: percepts and actions need to be carefully defined, possibly at different levels of abstraction
Artificial Intelligence (AI) is the buzzword there days, wherever we go. However some of the fundamentals / foundations required to program AI remains same as in Embedded Systems. The purpose of this talk is to introduce participants what an Artificial System is, how is it different from conventional system programming. It will provide a basic view of AI architecture and introduce audience with technologies / languages / tools. By the end of the talk audience will get basic knowledge of how AI system can be implemented.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).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.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
An intelligent agent perceives its environment via sensors and acts upon that environment with its effectors.
A discrete agent receives percepts one at a time, and maps this percept sequence to a sequence of discrete actions.
Properties
Autonomous
Reactive to the environment
Pro-active (goal-directed)
Interacts with other agents
via the environment
Humans
Sensors: Eyes (vision), ears (hearing), skin (touch), tongue (gustation), nose (olfaction), neuromuscular system (proprioception)
Percepts:
At the lowest level – electrical signals from these sensors
After preprocessing – objects in the visual field (location, textures, colors, …), auditory streams (pitch, loudness, direction), …
Effectors: limbs, digits, eyes, tongue, …
Actions: lift a finger, turn left, walk, run, carry an object, …
The Point: percepts and actions need to be carefully defined, possibly at different levels of abstraction
Artificial Intelligence (AI) is the buzzword there days, wherever we go. However some of the fundamentals / foundations required to program AI remains same as in Embedded Systems. The purpose of this talk is to introduce participants what an Artificial System is, how is it different from conventional system programming. It will provide a basic view of AI architecture and introduce audience with technologies / languages / tools. By the end of the talk audience will get basic knowledge of how AI system can be implemented.
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).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.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
3. Agents in Artificial Intelligence are the associated concepts that the AI
technologies work upon.
The AI software or AI-enabled devices with sensors generally captures the
information from the enviromant
setup and process the data for further actions.
Defination:
Artificial intelligence is defined as a study of rational agents.
A rational agent could be anything which makes decision, as a person, firm,
machine, or software. It carries after considering past and current
percepts(agent's perceptual inputs at a given instance).
An AI system is composed of an agent and its environment. The agents act in
their enviroment. The environment may contain other agents.
4. An agent is anything that can be viewed as :
perceiving its environment through sensors and
acting upon that environment through actuators
6. 1. Simplex reflex agent
• ignore the rest of the percept history and act only on the basis of the current
percept.
• Percept history is the history of all that an agent has perceived till date.
• The agent function is based on the condition-action rule.
• A condition-action rule is a rule that maps a state i.e, condition to an action. If
the condition is true, then the action is taken, else not.
• Limitations:-
• Very limited intelligence.
• No knowledge of non-perceptual parts of state.
• Usually too big to generate and store.
7.
8. 2.Model-based reflex agents
• It works by finding a rule whose condition matches the current situation.
• Can handle partially observable environments by use of model about the
world.
• keep track of internal state which is adjusted by each percept and that
depends on the percept history.
• The current state is stored inside the agent which maintains some kind of
structure describing the part of the world which cannot be seen.
• Updating the state requires information about :-
1. How the world evolves in-dependently from the agent, and
2.How the agent actions affects the world.
9.
10. 3.Goal-based agents
• Take decision based on how far they are currently from their goal.
• Their every action is intended to reduce its distance from the goal.
• its decisions is represented explicitly and can be modified, which makes
these agents more flexible.
• They usually require search and planning.
• The goal-based agent’s behavior can easily be changed.
11.
12. 4.Utility-based agents
• The agents which are developed having their end uses as building blocks
are called utility based agents.
• There are multiple possible alternatives, then to decide which one is
best.
• They choose actions based on a preference (utility) for each state.
• Utility describes how “happy” the agent is. Because of the uncertainty
in the world, a utility agent chooses the action that maximizes the
expected utility.
• A utility function maps a state onto a real number which describes the
associated degree of happiness.
13.
14. 5.Learning Agent
• The type of agent which can learn from its past experiences or it has learning
capabilities.
• It starts to act with basic knowledge and then able to act and adapt
automatically through learning.
• mainly four conceptual components, which are:
• Learning element :It is responsible for making improvements by learning from
the environment
• Critic: Learning element takes feedback from critic which describes how well
the agent is doing with respect to a fixed performance standard.
• Performance element: It is responsile for selecting external action
• Problem Generator: This component is responsible for suggesting actions that
will lead to new and informative experiences.
Editor's Notes
PRESENTATION
ON
DETAIL ABOUT AGENT WITH IT´S TYPES
Created by-
Bhupendra bohara
BSC CSIT -5TH SEM , FWU