The document discusses expert systems, which are computer programs that use artificial intelligence to solve complex problems that usually require human expertise. An example is a medical diagnosis expert system that allows a user to diagnose a disease without seeing a doctor. The key components of an expert system are the knowledge base, inference engine, and user interface. The knowledge base contains facts and rules acquired from human experts. The inference engine uses the rules to deduce conclusions. It can work forward or backward from the facts. The user interface allows interaction between the user and the system. The document provides examples of code for a medical diagnosis expert system and discusses some limitations of expert 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.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
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
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.
Knowledge representation In Artificial IntelligenceRamla Sheikh
facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject.
Knowledge = information + rules
EXAMPLE
Doctors, managers.
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
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
This presentation discuses the following topics:
What is A-Star (A*) Algorithm in Artificial Intelligence?
A* Algorithm Steps
Why is A* Search Algorithm Preferred?
A* and Its Basic Concepts
What is a Heuristic Function?
Admissibility of the Heuristic Function
Consistency of the Heuristic Function
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medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
This paper details the use of Electroencephalography, a methodology commonly applied for
medical purposes such as in detection of mental disorders and in upcoming technological research areas like BCI
(Brain Computer Interfaces), is now re-purposed to use in the Manufacturing sector to reduce the risk of error
and anomalies. Manufacturing involves many tasks that require mental alertness of an operator who supervises a
particular process, failure to do this, might leave unchecked errors in the finished product. Fatigue could lead to
serious consequences to health of the worker and may also lead to on-job accidents. To minimize possibility of
such instances, a study has been conducted to measure and find ways to tackle issues of mental fatigue. To
quantify the study, we have taken the case study of Pharmaceutical Sector where this kind of study might have
some impact. [3] The study reveals that workers doings tasks that require high alertness develop fatigue earlier
than anticipated, and therefore need frequent rotation from such activities.
Plug In Generator To Produce Variant Outputs For Unique Data.IJRES Journal
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UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
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4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
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Length: 30 minutes
Session Overview
-------------------------------------------
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- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
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https://www.rttsweb.com/jmeter-integration-webinar
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2. What is an EXPERT SYSTEM?
● An expert system is a computer expert that emulates the decision making
ability of a human expert.
● Expert system, a computer program that uses artificial intelligence methods
to solve complex problems within a specialized domain that ordinarily
requires human expertise.
3. Example: Making of a medical diagnosis expert
system
● A medical diagnosis expert system lets the user diagnose his disease without
going to a real doctor (but of course, user has to go to a real doctor for
treatment)
6. Components of an Expert System
1. Knowledge Base
2. Inference Engine
3. User Interface
7. 1. Knowledge Base
● A knowledge base is an organized collection of facts about the system’s
domain.
● Facts for a knowledge base must be acquired from human experts through
interviews and observations.
● This knowledge is then usually represented in the form of “if-then” rules
(production rules): “If some condition is true, then the following inference can
be made (or some action taken).”
● A probability factor is often attached to the conclusion of each production
rule and to the ultimate recommendation, because the conclusion is not a
certainty.
8. Components of Knowledge Base
● Factual Knowledge − It is the information widely accepted by the Knowledge
Engineers and scholars in the task domain.
● Heuristic Knowledge − It is about practice, accurate judgement, one’s ability
of evaluation, and guessing.
9. Knowledge Acquisition
● The knowledge base is formed by readings from various experts, scholars,
and the Knowledge Engineers. The knowledge engineer is a person with the
qualities of empathy, quick learning, and case analyzing skills.
● He acquires information from subject expert by recording, interviewing, and
observing him at work, etc.
● He then categorizes and organizes the information in a meaningful way, in the
form of IF-THEN-ELSE rules, to be used by interference machine. The
knowledge engineer also monitors the development of the ES.
10. Knowledge Base for Medical Diagnosis Expert
system
Symptoms Disease
Fever, cough, conjunctivitis, running nose, rashes Measles
Fever,headache, ache, conjunctivitis, chills,sore
throat,cough,running nose
Flu
Headache, sneezing, sore throat, running nose,
chills
Cold
Fever, chills,ache,rashes Chickenpox
11. 2. The Inference Engine
● An inference engine interprets and evaluates the facts in the knowledge
base in order to provide an answer or to reach a goal state
● For example, if the KB contains the production rules “if x, then y” and “if y, then
z,” the inference engine is able to deduce “if x, then z.”
● In medical diagnosis ES, if a user has Fever, chills,ache,rashes, then user
is suffering from chickenpox
● Strategies used by the inference engine
○ Forward chaining
○ Backward chaining
12. Forward Chaining
● “What can happen next?”
● It is a data-driven strategy.
● The inferencing process moves from the facts of the case to a goal
(conclusion).
● The inference engine attempts to match the condition (IF) part of each rule in
the knowledge base with the facts currently available in the working memory.
● If several rules match, a conflict resolution procedure is invoked;
14. Forward Chaining in Medical Diagnosis ES
● A user has fever, headache, ache, conjunctivitis, chills,sore
throat,cough,running nose.
● In forward chaining, the inference engine will use the symptoms specified to
conclude to the disease the user is suffering from.
● After going through the above symptoms, the inference engine concludes that
the user is suffering from flu by checking the knowledge base.
15. Backward Chaining
● “Why this happened?”
● On the basis of what has already happened, the Inference Engine tries to find
out which conditions could have happened in the past for this result.
● This strategy is followed for finding out cause or reason.
● The inference engine attempts to match the assumed (hypothesized)
conclusion - the goal or subgoal state - with the conclusion (THEN) part of the
rule.
● If such a rule is found, its premise becomes the new subgoal.
● In an ES with few possible goal states, this is a good strategy to pursue.
17. Backward Chaining in Medical Diagnosis ES
● Suppose a user has measles.
● Now, the inference engine will look up in the knowledge base to derive the
symptoms that could have resulted in measles.
● It inferred that the symptoms were fever, cough, conjunctivitis, running nose,
rashes.
● This worked as only few states were present.
18. 3. User Interface
● User interface provides interaction between user of the ES and the ES itself.
● The user of the ES need not be necessarily an expert in Artificial Intelligence.
● It explains how the ES has arrived at a particular recommendation. The
explanation may appear in the following forms −
○ Natural language displayed on screen.
○ Verbal narrations in natural language.
○ Listing of rule numbers displayed on the screen.
19. Code for medical diagnosis ES: 1
#include <iostream>
using namespace std;
void measels(char,char,char,char,char);
void flu(char,char,char,char,char,char,char,char);
void cold(char,char,char,char,char);
void chickenpox(char,char,char,char);
20. Code for medical diagnosis ES: 2
int main()
{
char name[50];
char a,b,c,d,e,f,g,h,i,j,k;
cout << "Please enter your name.. " << endl;
cin>> name;
cout << "Do you have fever? (y/n)"<< endl;
cin>>a;
cout << "Do you have rashes? (y/n)"<< endl;
21. Code for medical diagnosis ES: 3
cin>>b;
cout << "Do you have headache? (y/n)"<< endl;
cin>>c;
cout << "Do you have running nose? (y/n)"<< endl;
cin>>d;
cout << "Do you have conjunctivitis? (y/n)"<< endl;
cin>>e;
cout << "Do you have cough? (y/n)"<< endl;
cin>>f;
cout << "Do you have ache? (y/n)"<< endl;
22. Code for medical diagnosis ES: 4
cin>>g;
cout << "Do you have chills? (y/n)"<< endl;
cin>>h;
cout << "Do you have swollen glands? (y/n)"<< endl;
cin>>i;
cout << "Do you have sneezing? (y/n)"<< endl;
cin>>j;
cout << "Do you have sore throat? (y/n)"<< endl;
cin>>k;
23. Code for medical diagnosis ES: 5
measles(a,f,e,d,b);
flu(a,c,g,e,h,k,f,d);
cold(c,j,k,d,h);
chickenpox(a,h,g,b);
return 0;
}
24. Code for medical diagnosis ES: 6
void measles(char q,char w,char r,char t,char y)
{
if(q=='y'&&w=='y'&& r=='y' && t=='y' && y== 'y')
cout<< "You may have measles"<< endl;
else
cout<< "";
}
25. Code for medical diagnosis ES: 7
void flu(char q,char w,char r,char t,char y,char p,char l,char x)
{
if(q=='y'&&w=='y'&& r=='y' && t=='y' && y== 'y'&& p=='y' && l=='y' && x=='y')
cout<< "You may have flu."<< endl;
else
cout<< "";
}
28. Limitations of Expert System
No technology offers an easy and total solution. Large systems are costly and
require significant development time and computer resources. ESs also have their
limitations which include:
● Limitations of the technology
● Problems with knowledge acquisition
● Maintaining human expertise in organizations