An agent is anything that can perceive its environment and act upon that environment. An intelligent agent must sense, act autonomously, and be rational. There are different types of environments and classes of agents. Intelligent agents are applied as automated online assistants to perceive customer needs and perform individualized customer service.
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.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
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.
Problem solving
Problem formulation
Search Techniques for Artificial Intelligence
Classification of AI searching Strategies
What is Search strategy ?
Defining a Search Problem
State Space Graph versus Search Trees
Graph vs. Tree
Problem Solving by Search
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
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
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# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
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What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
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
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
What is Intelligent agent, Abstract Intelligent Agents, Autonomous Intelligent Agents, Classes of intelligent agents, Application of an intelligent agent, Capabilities of an intelligent agent, Limitations of an intelligent agent.
UNIT - I PROBLEM SOLVING AGENTS and EXAMPLES.pptx.pdfJenishaR1
Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.
To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:
Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:
High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
The concept of intelligent system has emerged in information technology as a type of system derived from successful applications of artificial intelligence. The goal of this presentation is to give a general description of an intelligent system, which integrates classical approaches and recent advances in artificial intelligence. The presentation describes an intelligent system
in a generic way, identifying its main properties and functional components.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Assumptions of parametric and non-parametric tests
Testing the assumption of normality
Commonly used non-parametric tests
Applying tests in SPSS
Advantages of non-parametric tests
Limitations
AI Agents, Agents in Artificial IntelligenceKirti Verma
HI guys,
I am starting my very first course on Artificial Intelligence(AI).
if your are interested in the above topic you can follow this course to improve your knowledge.
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you can view the slide presented in the above video https://www.slideshare.net/KirtiVerma4/artificial-intellegence-introduction
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This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
For more topics stay tuned with Learnbay.
Intelligent Agents, A discovery on How A Rational Agent ActsSheetal Jain
Because this concept of developing a smart set of design principles for building successful agents, systems that can reasonably be called intelligent, is Central to artificial intelligence we need to know its thinking and action approach. This PPT covers this topic in detail.
Go and take a look and share your suggestions with me.
Introduction of agents, Structure(configuration) of Intelligent agent,
Properties of Intelligent Agents
2.2. PEAS Description of Agents
2.3. Types of Agents: Simple Reflexive, Model Based, Goal Based, Utility Based,
Learning agent.
2.4. Types of Environments: Deterministic/Stochastic, Static/Dynamic,
Observable/Semi-observable, Single Agent/Multi Agent
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
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Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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Intelligent agent
1.
2. Instructional Objective
Define an agent
Define an Intelligent agent
Define a Rational agent
Discuss different types of environment
Explain classes of intelligent agents
Applications of Intelligent agent
3. Agents
An agent is anything that can be viewed as
perceiving its environment through sensors and
acting upon that environment through effectors.
A human agent has eyes, ears, and other organs
for sensors, and hands, legs, mouth, and other
body parts for effectors.
A robotic agent substitutes cameras and infrared
range finders for the sensors and various motors
for the effectors.
5. Agents
Operate in an environment.
Perceives its environment through
sensors.
Acts upon its environment through
actuators/effectors.
Have goals.
6. Sensors & Effectors
An agent Perceives its environment
through sensors.
The complete set of inputs at a given time
is called percept.
The current percept, or a sequence of
percepts can influence the actions of an
agent.
7. Sensors & Effectors
It can change the environment through
effectors.
An operation involving an actuator is
called an action.
Actions can be grouped in to action
sequences.
So an agent program implement
mapping from percept sequences to
actions.
8. Agents
Autonomous Agent: Decide autonomously
which action to take in the current situation
to maximize progress towards its goals.
Performance measure: An objective
criterion for success of an agent's behavior.
E.g., performance measure of a vacuum-
cleaner agent could be amount of dirt
cleaned up, amount of time taken, amount
of electricity consumed, amount of noise
generated, etc.
9. Examples of agents
Humans
eyes, ears, skin, taste buds, etc. for Sensors.
hands, fingers, legs, mouth for effectors.
Robots
camera, infrared, bumper, etc. for sensors.
grippers, wheels, lights, speakers, etc. for
effectors.
10. Structure of agents
A simple agent program can be defined
mathematically as an agent function which
maps every possible precepts sequence to
a possible action the agent can perform.
F: p*-> A
the term percept is use to the agent's
perceptional inputs at any given instant.
11. Intelligent agents
Fundamental faculties of intelligence
Acting
Sensing
Understanding, Reasoning, learning
In order to act you must sense. Blind actions is not
a characterization of intelligence.
Robotics: sensing and acting. Understanding not
necessary.
Sensing needs understanding to be useful.
13. Rational Agent
AI is about building rational agents.
An agent is something that perceives
and acts.
A rational agent always does the right
thing.
What are the functionalities(goals)?
What are the components?
How do we build them?
14. Rationality
Perfect Rationality:
Assumes that the rational agent
knows all and will take the action that
maximize the utility.
Human beings do not satisfy this
definition of rationality.
15. Agent Environment
Environments in which agents operate
can be defined in different ways.
It is helpful to view the following
definitions as referring to the way the
environment appears from the point of
view of the agent itself.
16. Environment: Observability
Fully Observable: All of the environment
relevant to the action being considered is
observable.
Such environments are convenient, since the
agent is freed from the task of keeping track of
changes in the environment.
Partially Observable: The relevant features of
the environment are only partially observable.
Example: Fully obs: Chess; Partially obs: Poker
17. Environment: Determinism
Deterministic: The next state of the
environment is completely described by the
current state and the agent’s action. e.g. Image
Analysis
Stochastic: If an element of interference or
uncertainty occurs then the environment is
stochastic. A partially observable environment will
appear to be stochastic to the agent. e.g. ludo
Strategic: Environment state wholly determined
by the preceding state and the actions of multiple
agents is called strategic. e.g. Chess
18. Environment: Episodicity
Episodic/Sequential:
An Episodic Environment means that
subsequent episodes do not depend on
what actions occurred in previous
episodes.
In a Sequential Environment, the
agent engages in a series of connected
episodes.
19. Environment: Dynamism
Static Environment: does not change
from one sate to next while the agent is
considering its course of action. The
only changes to the environment as
those caused by the agent itself.
Dynamic Environment: Changes over
time independent of the actions of the
agent-and thus if an agent does not
respond in a timely manner, this counts
as a choice to do nothing.
21. Classes of Intelligent Agents
Intelligent agents are grouped in to five
classes based on their degree of
perceived intelligence and capability.
Simple reflex agents
Model based reflex agents
Goal based agents
Utility based agents
Learning agents
22. Simple reflex agents
Simple reflex agents act only on the basis of the
current percept, ignoring the rest of the percept
history. The agent function is based on the
condition-action rule: if condition then action.
Succeeds when the environment is fully
observable.
Some reflex agents can also contain information
on their current state which allows them to
disregard conditions.
24. Model based reflex agents
A model-based agent can handle a partially
observable environment.
Its current state is stored inside the agent
maintaining some kind of structure which
describes the part of the world which cannot be
seen.
This knowledge about "how the world evolves" is
called a model of the world, hence the name
"model-based agent".
26. Goal based agents
Goal-based agents further expand on the
capabilities of the model-based agents, by using
"goal" information.
Goal information describes situations that are
desirable. This allows the agent a way to choose
among multiple possibilities, selecting the one
which reaches a goal state.
Search and planning are the subfields of artificial
intelligence devoted to finding action sequences
that achieve the agent's goals.
28. Utility based agents
Goal-based agents only distinguish between goal
states and non-goal states.
It is possible to define a measure of how desirable
a particular state is. This measure can be obtained
through the use of a utility function which maps a
state to a measure of the utility of the state.
A more general performance measure should
allow a comparison of different world states
according to exactly how happy they would make
the agent. The term utility, can be used to describe
how "happy" the agent is.
30. Learning agents
Learning has an advantage that it allows the agents
to initially operate in unknown environments and to
become more competent than its initial knowledge
alone might allow.
The most important distinction is between the
"learning element", which is responsible for making
improvements, and the "performance element",
which is responsible for selecting external actions.
The learning element uses feedback from the
"critic" on how the agent is doing and determines
how the performance element should be modified
to do better in the future.
31. Learning agents
The last component of the learning
agent is the "problem generator". It is
responsible for suggesting actions that
will lead to new and informative
experiences.
33. Applications of Intelligent
Agents
Intelligent agents are applied as
automated online assistants, where they
function to perceive the needs of
customers in order to perform
individualized customer service.
Use in smart phones in future.