This document discusses intelligent agents and their environments. It covers:
1) Intelligent agents are entities that perceive their environment through sensors and act upon the environment through actuators. They map percept sequences to actions.
2) A rational agent should select actions that are expected to maximize its performance measure given the percept sequence and its prior knowledge. Performance measures evaluate how well the agent solves its task.
3) Agent environments can have different properties such as being fully or partially observable, deterministic or stochastic, episodic or sequential, static or dynamic, discrete or continuous, and single-agent or multi-agent. The simplest is fully observable, deterministic, etc. but most real environments are more complex.
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.
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
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.
Introduction to agents and multi-agent systemsAntonio Moreno
Multi-agent systems course at University Rovira i Virgili. Slides mostly based on those of Rosenschein, from the content of the book by Wooldridge.
Lecture 1-Introduction to agents and multi-agent systems.
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
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
peas description of task environment with different types of propertiesmonircse2
Ai related Topics: PEAS: A task environment specification that includes Performance measure, Environment, Actuators, and Sensors. Agents can improve their performance through learning. This is a high-level presentation of agent programs.
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
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
MAS course at URV. Lecture 4, agent types (specially interface agents, information agents, hybrid systems, agentification). Based on diverse resources.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
peas description of task environment with different types of propertiesmonircse2
Ai related Topics: PEAS: A task environment specification that includes Performance measure, Environment, Actuators, and Sensors. Agents can improve their performance through learning. This is a high-level presentation of agent programs.
Types of environment, Fully observable vs partially observable, static vs dynamic, deterministic vs stochastic, episodic vs sequential, discrete vs continuous, single agent vs multiagent, single agent vs multiagent,
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
FOLLOW PART #2 OF THE SERIES
https://youtu.be/fM6CQ2Vsdjw
ENJOYYY
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.
www.funFruit.com
Intelligent Agent
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Intelligent Agent
What is Agent?
Agents and Environments
Rational Agents
PEAS Examples:
Agent Types
Table Driven Agents
Reflex agents
Goal-based agents
Utility-based agents
Learning agents
Environment Types
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Intelligent Agent
An agent is anything that can be viewed as
perceiving its environment through sensors
and acting upon that environment through
actuators
www.funFruit.com
Intelligent Agent
Agents and environments
An agent perceives its environment through
sensors.
The complete set of inputs at a given time is
called a percept.
The current percept, or a sequence of
percepts can influence the actions of an agent. The agent can change the
environment through actuators or
effectors.
An operation involving an effectors is
called an action.
Actions can be grouped into action
sequences.
5
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Intelligent Agent
Agents
Human agent:
eyes, ears, and other organs for sensors;
hands, legs, mouth, and other body parts for actuators
Robotic agent:
• cameras and infrared range finders for sensors;
• various motors for actuators..
• Example AIBO entertainment robot from SONY
A software agent:
Keystrokes, file contents, received network packages as sensors
Displays on the screen, files, sent network packets as actuators
6
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Intelligent Agent
Agent Example
A simple example of an agent in a physical environment is a thermostat for a
heater.
The thermostat receives input from a sensor, which is
embedded in the environment, to detect the temperature.
Two states:
temperature too cold
temperature OK are possible.
The first action has the effect of raising the room temperature, but this is not
guaranteed. If cold air continuously comes into the room, the added heat may
not have the desired effect of raising the room temperature.
Each state has an associated action:
too cold turn the heating on
temperature OK turn the heating off.
7
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Intelligent Agent
Agent function and program
Agent function
An agent is completely specified by the agent function
[f: P* -> A]
mapping percept sequences to actions
Agent program
The agent program runs on the physical architecture to produce f
AGENT = ARCHITECTURE + PROGRAM
.
8
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Intelligent Agent
Vacuum-cleaner world
Percepts: location and state of the environment, e.g.,
[A,Dirty], [B,Clean]
Actions: Left, Right,
Suck, NoOp
9
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Intelligent Agent
Agent Performance
How do you define success? Need a performance measure
How the agent does successfully
E.g., 90% or 30% ?
Eg. reward agent with one point for each clean square at each time step
(could penalize for costs and noise)
The success can be.
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
Circuit lab 9 verification of maximum power transfer theorem
.
This lecture is from my class lectures in IIT-JU.
.
Find me:
Website: https://www.tajimiitju.blogspot.com
Linked in: https://www.linkedin.com/in/tajimiitju
Researchgate: https://www.researchgate.net/profile/Tajim_Md_Niamat_Ullah_Akhund
Youtube: https://www.youtube.com/tajimiitju?sub_confirmation=1
Slideshare: https://www.slideshare.net/TajimMdNiamatUllahAk
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Email: tajim.mohammad.3@gmail.com
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
1. CSE 412: Artificial IntelligenceCSE 412: Artificial Intelligence
Topic - 2: Intelligent AgentsTopic - 2: Intelligent Agents
Fall 2018Fall 2018
Tajim Md. Niamat Ullah Akhund
Lecturer
Department of Computer Science and Engineering
Daffodil International University
Email: tajim.cse@diu.edu.bd
2. Agents and EnvironmentsAgents and Environments
Good Behavior: The Concept of RationalityGood Behavior: The Concept of Rationality
The Nature of EnvironmentsThe Nature of Environments
The Structure of AgentsThe Structure of Agents
Topic ContentsTopic Contents
3. Intelligent AgentIntelligent Agent
An agent
perceives its environment with sensors
and
acts upon that environment with its
actuators.
An agent gets percepts one at a time,
and maps this percept sequence to
actions.
3
Majidur RahmanMajidur Rahman
5. Example of an AgentExample of an Agent
Let us consider a vacuum-cleaner agent that is
shown in the figure bellow.
The vacuum-cleaner world has just two locations:
squares A and B.
5
6. Example of an AgentExample of an Agent
The vacuum agent perceives which square it is in
and whether there is dirt in the square.
It can choose to move left, move right, suck up the
dirt, or do nothing.
One very simple agent function is the following: if
the current square is dirty, then suck; otherwise,
move to the other square.
6
7. Example of an AgentExample of an Agent
Environment: squares A and B
Percepts: [location, status], e.g. [A, Dirty]
Actions: left, right, suck, and no-op
7
Majidur RahmanMajidur Rahman
8. function REFLEX-VACUUM-AGENT ([location, status]) return an action
if status == Dirty then return Suck
else if location == A then return Right
else if location == B then return Left
Example of an AgentExample of an Agent
Majidur RahmanMajidur Rahman
9. Rational AgentRational Agent
For each possible percept sequence, a rational agent
should select an action (using an agent function) that is
expected to maximize its performance measure, given
the evidence provided by the percept sequence and
whatever built-in prior knowledge the agent has.
A percept sequence is the complete history of anything
the agent has ever perceived.
A performance measure is a means of calculating how
well the agent has performed based on the sequence of
percepts that it has received.
An agent’s prior knowledge of the environment is the
knowledge that the agent designer has given to the
agent before its introduction to the environment.
9
Majidur RahmanMajidur Rahman
10. Rational AgentRational Agent
An agent function maps percept sequences to actions.
f : seq(P) →A
Agent function for vacuum Cleaner example:
10
Majidur RahmanMajidur Rahman
11. Performance
Measures
Environment
Actuators
Sensors
used by the agent to perform actions
surroundings beyond the control of the agent
used to evaluate how well an agent
solves the task at hand
provide information about the current state of
the environment
To design a rational agent we must specify its task
environment:
Task EnvironmentsTask Environments
11
Majidur RahmanMajidur Rahman
12. ExampleExample
PEAS description of the environment for an
automated taxi:
Performance
Safety, destination, profits, legality, comfort
Environment
Streets/motorways, other traffic, pedestrians, weather
Actuators
Steering, accelerator, brake, horn, speaker/display
Sensors
Video, sonar, speedometer, engine sensors, keyboard,
GPS
12
Majidur RahmanMajidur Rahman
14. Environment TypesEnvironment Types
The environment for an agent may be
Fully or partially observable
Deterministic or stochastic
Episodic or sequential
Static or dynamic
Discrete or continuous
Single or multi-agent
14
Majidur RahmanMajidur Rahman
16. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable??
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when
the sensors can detect all aspects that are relevant to the choice of
action.
Majidur RahmanMajidur Rahman 16
17. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
Deterministic??
Episodic??
Static??
Discrete??
Single-agent??
Fully vs. partially observable: an environment is full observable when
the sensors can detect all aspects that are relevant to the choice of
action.
Majidur RahmanMajidur Rahman 17
18. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
DeterministicDeterministic??
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is
completely determined by the current state the executed action then the
environment is deterministic.
Majidur RahmanMajidur Rahman 18
19. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
DeterministicDeterministic?? YES NO YES NO
Episodic??
Static??
Discrete??
Single-agent??
Deterministic vs. stochastic: if the next environment state is
completely
determined by the current state the executed action then the
environment is deterministic.
Majidur RahmanMajidur Rahman 19
20. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
DeterministicDeterministic?? YES NO YES NO
EpisodicEpisodic??
Static??
Discrete??
Single-agent??
Episodic vs. sequential: In an episodic environment the agent’s
experience can be divided into atomic steps where the agents perceives
and then performs a single action. The choice of action depends only on
the episode itself.
Majidur RahmanMajidur Rahman 20
21. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
EpisodicEpisodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
Episodic vs. sequential: In an episodic environment the agent’s
experience can be divided into atomic steps where the agents perceives
and then performs a single action. The choice of action depends only on
the episode itself
Majidur RahmanMajidur Rahman 21
22. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static??
Discrete??
Single-agent??
Static vs. dynamic: If the environment can change while the agent is
choosing an action, the environment is dynamic. Semi-dynamic if the
agent’s performance changes even when the environment remains the
same.
Majidur RahmanMajidur Rahman 22
23. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
DeterministicDeterministic?? YES NO YES NO
EpisodicEpisodic?? NO NO NO NO
StaticStatic?? YES YES SEMI NO
Discrete??
Single-agent??
Static vs. dynamic: If the environment can change while the agent is choosing
an action, the environment is dynamic. Semi-dynamic if the agent’s performance
changes even when the environment remains the same.
Majidur RahmanMajidur Rahman 23
24. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete??
Single-agent??
Discrete vs. continuous: This distinction can be applied to the state of the
environment, the way time is handled and to the percepts/actions of the agent.
Majidur RahmanMajidur Rahman 24
25. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
Observable?? FULL FULL PARTIAL PARTIAL
Deterministic?? YES NO YES NO
Episodic?? NO NO NO NO
Static?? YES YES SEMI NO
Discrete?? YES YES YES NO
Single-agent??
Discrete vs. continuous: This distinction can be applied to the state of the
environment, the way time is handled and to the percepts/actions of the agent.
Majidur RahmanMajidur Rahman 25
26. Environment TypesEnvironment Types
Solitaire Backgammom Internet shopping Taxi
ObservableObservable?? FULL FULL PARTIAL PARTIAL
DeterministicDeterministic???? YES NO YES NO
EpisodicEpisodic???? NO NO NO NO
StaticStatic???? YES YES SEMI NO
DiscreteDiscrete???? YES YES YES NO
Single-agentSingle-agent???? YES NO NO NO
Single vs. multi-agent: Does the environment contain other agents who are
also maximizing some performance measure that depends on the current
agent’s actions?
Majidur RahmanMajidur Rahman 26
27. Environment TypesEnvironment Types
The simplest environment is
Fully observable, deterministic, episodic,
static, discrete and single-agent.
Most real situations are:
Partially observable, stochastic, sequential,
dynamic, continuous and multi-agent.
Majidur RahmanMajidur Rahman 27
28. Agent TypesAgent Types
Four basic kinds of agent that embody the
principles underlying almost all intelligent systems:
Simple reflex agents;
Model-based reflex agents;
Goal-based agents; and
Utility-based agents.
28
Majidur RahmanMajidur Rahman
29. Agent Types: Simple ReflexAgent Types: Simple Reflex
Select action on the basis of only
the current percept,
e.g. the vacuum-agent
Large reduction in possible
percept/action situations
Implemented through
condition-action rules
if dirty then suck
Example:
– Agent: Mail sorting robot
– Environment: Conveyor belt of
letters
– Rule: e.g.
city = Edin → put Scotland bag
29
Majidur RahmanMajidur Rahman
30. Agent Types: Model-BasedAgent Types: Model-Based
To tackle partially observable
environments
Maintain internal state that
depends on percept history
Over time update state using
world knowledge
How does the world change
independently
How do actions affect the world
Example:
– Agent: Robot vacuum cleaner
– Environment: Dirty room, furniture
– Model: Map of room, which areas
already cleaned 30
Majidur RahmanMajidur Rahman
31. Agent Types: Goal-BasedAgent Types: Goal-Based
The agent needs a goal to know
which situations are desirable
Difficulties arise when long
sequences of actions are required
to find the goal
Typically investigated in search
and planning research
Major difference: future is taken
into account
Example:
– Agent: Robot maid
– Environment: House and people
– Goal: Clean clothes, tidy room,
table laid, etc 31
Majidur RahmanMajidur Rahman
32. 32
Agent Types: Utility-BasedAgent Types: Utility-Based
Certain goals can be reached in
different ways
Some are better: have a higher
utility
Utility function maps a (sequence
of) state(s) onto a real number
Improves on goals:
Selecting between conflicting
goals
Selecting between several goals
based on likelihood of success
and importance of goals
Example:
– Agent: Mars Lander
– Environment: The surface of
mars with obstacles
– Utility: Shortest and obstacle-free
path Majidur RahmanMajidur Rahman
34. ********** ********** If You Need Me ********** **********
Mail: tajim.cse@diu.edu.bd
Website: https://www.tajimiitju.blogspot.com
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Thank you
Tajim Md. Niamat Ullah AkhundTajim Md. Niamat Ullah Akhund
Editor's Notes
Volledige observeerbare omgevingen zijn handig omdat de agent geen
Interne staat moet bijhouden over de omgeving
Partieel observeerbare omgevingen door noise of niet goed afgestelde sensoren of ontbrekende informatie
Als de omgeving gedeeltelijk observeerbaar is kan deze stochastisch lijken.
Dus het is beter om te kijken of de omgebing determinitisch of stochastisch is vanuit het standpunt van de agent.
Als de omgeving deterministisch is behalve de acties van de andere agenten dan is de omgeving strategisch.
If the agent performance score changes ahen time passes, the environment is semi-dynamic.
If the agent performance score changes ahen time passes, the environment is semi-dynamic.
If the agent performance score changes ahen time passes, the environment is semi-dynamic.
If the agent performance score changes ahen time passes, the environment is semi-dynamic.
If the agent performance score changes ahen time passes, the environment is semi-dynamic.