An expert system is an interactive computer-based decision tool that uses both facts and heuristics to solve difficult decision problems based on knowledge acquired from an expert.
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.
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.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
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.
This presentation educates you about Applications of Expert System, Expert System Technology, Development of Expert Systems: General Steps and Benefits of Expert Systems.
For more topics stay tuned with Learnbay.
Application of Expert Systems in System Analysis & Design faiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
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.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Artificial Intelligence lecture notes. AI summarized notes for expert systems, inference mechanisms and so on, this is reading and may be for self-learning, I think.
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
---TABLE OF CONTENT---
Introduction
Differences between crisp sets & Fuzzy sets
Operations on Fuzzy Sets
Properties
MF formulation and parameterization
Fuzzy rules and Fuzzy reasoning
Fuzzy interface systems
Introduction to genetic algorithm
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.
This presentation educates you about Applications of Expert System, Expert System Technology, Development of Expert Systems: General Steps and Benefits of Expert Systems.
For more topics stay tuned with Learnbay.
Application of Expert Systems in System Analysis & Design faiza nahin
Design is a field in which a large part of the processes involved is knowledge-based rather than computation-based. Much of this knowledge is experiential and as such lends itself to be encapsulated in an expert system. An analogy is made between analysis and interpretation and between evaluation and comparison of interpretations. Three examples of expert systems carrying out design analysis and evaluation in different domains are described. It is argued that a graphical interface and a model of the elements within the domain are essential parts of any design system.
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.
Part of Lecture series on EE646, Fuzzy Theory & Applications delivered by me during First Semester of M.Tech. Instrumentation & Control, 2012
Z H College of Engg. & Technology, Aligarh Muslim University, Aligarh
Reference Books:
1. T. J. Ross, "Fuzzy Logic with Engineering Applications", 2/e, John Wiley & Sons,England, 2004.
2. Lee, K. H., "First Course on Fuzzy Theory & Applications", Springer-Verlag,Berlin, Heidelberg, 2005.
3. D. Driankov, H. Hellendoorn, M. Reinfrank, "An Introduction to Fuzzy Control", Narosa, 2012.
Please comment and feel free to ask anything related. Thanks!
Artificial Intelligence lecture notes. AI summarized notes for expert systems, inference mechanisms and so on, this is reading and may be for self-learning, I think.
Artificial Intelligence: The Nine Phases of the Expert System Development Lif...The Integral Worm
This presentation is an introduction to Artificial Intelligence: The Nine Phases of the Expert System Development Lifecycle (ESDLC). Topics covered are the following: problem identification phase, feasibility study phase, project planning phase, knowledge acquisition phase, knowledge representation phase, knowledge implementation phase, verification and validation, installation/transition/ training, operation/evaluation/maintenance.
A tutorial on applying artificial neural networks and geometric brownian moti...eSAT Journals
Abstract Several challenges in the engineering or financial world can be resolved with a proper handle on data. Amongst other applications in engineering, system identification and parameter estimation are widely used in developing control strategies for automation. In this domain, there would be requirements to design an adaptive control system. In order to design an adaptive control system, an adaptive model needs to be estimated or identified. This is generally done by studying the data and creating a transfer function. In the process, regression, artificial neural networks (ANN), random walk theory and Markov chain estimates are used to understand a time series and create a model. While some of these processes are stationary, some are non-stationary. These methods are chosen based on the nature and availability of historical data. One of the issues that always remain is which method is appropriate for a certain application. The objective of this tutorial is to illustrate how artificial neural network and Geometric Brownian motion can be used in this regard. An attempt is made to predict the future price of a stock of a corporation. Stock prices are an example for a stochastic time series. Initially, an artificial neural network is used to predict the stock price. The network is designed as a Multi layer Back propagation type network. Profit over earnings and S&P are used as inputs. Thereafter, Geometric Brownian motion is explained and used on the same dataset to come up with its predictions. The results from both neural network and geometric Brownian motion are compared. Key Words: Artificial Neural Network, Geometric Brownian Motion, Stochastic time series, and stock price prediction
Slide deck from 2008 Symposium "Developing an Expert-System for Health Promotion: An Experimental E-Learning Platform" from the APA-NIOSH International Conference on Work, Stress, and Health
A Security Analysis Framework Powered by an Expert SystemCSCJournals
Today\'s IT systems are facing a major challenge in confronting the fast rate of emerging security threats. Although many security tools are being employed within organizations in order to standup to these threats, the information revealed is very inferior in providing a rich understanding to the consequences of the discovered vulnerabilities. We believe expert systems can play an important role in capturing any security expertise from various sources in order to provide the informative deductions we are looking for from the supplied inputs. Throughout this research effort, we have built the Open Security Knowledge Engineered (OpenSKE) framework (http://code.google.com/p/openske), which is a security analysis framework built around an expert system in order to reason over the security information collected from external sources. Our implementation has been published online in order to facilitate and encourage online collaboration to increase the practical research within the field of security analysis.
Iceei2013 expert system in detecting coffee plant diseasesWahyu Nugraha
Coffee is an important commodity in the world economy. But unfortunately, productivity and quality of those commodities results are still quite low. This is caused by the disease in coffee plants.
The research objective is to create an application that can help researchers or observers working in coffee plantation to diagnose diseases of coffee plants.
The method used is fuzzy logic-based expert systems, and decision tree using a hierarchical classification. Knowledge about coffee, its symptoms, and its disease is extracted from human expert and then is converted into a decision tree. It will result on the fuzzy logic-based expert systems.
From the experiments, accuracy calculation of the system is about 85%. Based on the accuracy, it can be concluded that this application can be a bit much to help researchers or observers of the coffee plants in diagnosing coffee plants diseases earlier.
Index Terms— Expert System, Fuzzy, Decision Tree, Coffee, Disease
The Good, the bad, and the ugly of Thin Client/Server ComputingThe Integral Worm
This presentation discusses the features, benefits, advantages and disadvantages of using Thin Client/Server technology as an IT Security strategy. The full discussion of Thin Client/Server technology as a security solution may be found at http://www.theintegralworm.com/security.html .
Ідентифікація багатофакторних залежностей на основі нечіткої бази знань з рі...Роман Тилець
Розглядається ідентифікація багатофакторних залежностей за допомогою гібридної нечіткої бази знань, в яку входять правила різних форматів – Мамдані, Сугено, Ларсена тощо. Такий формат дозволяє описати досліджувану залежність в різних зонах факторного простору за допомогою нечітких правил найбільш релевантного формату.
Concept of Expert Systems .
Appearing of Expert System.
Areas of Success and Failure.
Examples.
Advantages and disadvantages.
What have been planned for it in the future
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKSjares jares
Expert systems are no longer just a technology, but they have entered many fields of decision-making from these medical fields, for example, as they help in diagnosing the disease and giving treatment, and also in the field of administration, where they give the manager a rational decision to solve a problem and other fields, DSS is an interactive information system that provides information, models, and data processing tools to assist decision-making. Islamic banks such as commercial banks offer products and services to customers, but these banks face many problems and the most important ones are the problems financing where Islamic banks seek to participate in money rather than lending and interest the participatory financing system is one of the most important sources of financing within Islamic banks This system is based on the agreement between the Bank and the customer to participate in a new project or project already in place in the proportions that agree to by the bank and the client but this funding takes a long time and many actions so the researcher has built an expert system to reduce the time it takes to award Funding and also to reduce procedures as the expert systems have the ability to help the human element in making decisions. This paper presents expert systems in Islamic banks in the system of co-financing in order to save time and effort and maximize profit.
A PROPOSED EXPERT SYSTEM FOR EVALUATING THE PARTNERSHIP IN BANKSJaresJournal
Expert systems are no longer just a technology, but they have entered many fields of decision-making from
these medical fields, for example, as they help in diagnosing the disease and giving treatment, and also in
the field of administration, where they give the manager a rational decision to solve a problem and other
fields, DSS is an interactive information system that provides information, models, and data processing
tools to assist decision-making. Islamic banks such as commercial banks offer products and services to
customers, but these banks face many problems and the most important ones are the problems financing
where Islamic banks seek to participate in money rather than lending and interest the participatory
financing system is one of the most important sources of financing within Islamic banks This system is
based on the agreement between the Bank and the customer to participate in a new project or project
already in place in the proportions that agree to by the bank and the client but this funding takes a long
time and many actions so the researcher has built an expert system to reduce the time it takes to award
Funding and also to reduce procedures as the expert systems have the ability to help the human element in
making decisions. This paper presents expert systems in Islamic banks in the system of co-financing in
order to save time and effort and maximize profit.
Integration of Other Software Components with the Agricultural Expert Systems...IJARTES
Expert System is a rapidly growing technology in
the field of Artificial Intelligence. It is a computer program
which captures the knowledge of a human expert on a given
problem, and uses this knowledge to solve problems in a
fashion similar to the expert. The system can assist the expert
during problem-solving, or act in the place of the expert in
those situations where the expertise is lacking. Expert systems
have been developed in such diverse areas as agriculture,
science, engineering, business, and medicine. In these areas,
they have increased the quality, efficiency, and competitive
leverage of the organizations employing the technology. This
paper highlights the major characteristics of expert systems,
reviews several systems developed for application in the area
of agriculture and an overview about the integration of other
software components with the agricultural expert systems.
This follow up post on the subject of Artificial Intelligence focuses on Expert Systems and the role of traditional experts in their design and development. It explores four main themes:
What do we mean by Expert?
How do experts work?
Expert Systems Application Domains, and
Features of rule based Expert (KB) Systems
It gives an individual a general Idea about Expert System and the wide variety of it's Applications.We discuss the scope of Expert System in upcoming Future in various Domains and various Challenges.Some examples are also given of a few Expert Systems.
Differences between the e edition of newspaper n web edition of newspaper, most of the times we think they were same but their is a differences n here are some points...
advancement in communication - print, television, electronic kiran paul
dissemination of ideas is required and media is responsible for that and advancement in communication media is the requirement of time and responsibility of time.
There are many environmental issues in India. Air pollution, water pollution, garbage, and pollution of the natural environment are all challenges for India. The situation was worse between 1947 through 1995. According to data collection and environment assessment studies of World Bank experts, between 1995 through 2010, India has made one of the fastest progress in the world, in addressing its environmental issues and improving its environmental quality.Still, India has a long way to go to reach environmental quality similar to those enjoyed in developed economies. Pollution remains a major challenge and opportunity for India.Environmental issues are one of the primary causes of disease, health issues and long term livelihood impact for India.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- 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?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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/
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
2. EXPERT SYSTEM
Expert system are intelligent computer
programs designed to stimulate the problem-solving
behavior of human experts.
These programs perform a task that would
otherwise be performed by a human expert.
It create a tool that can be used by a layman
to solve difficult or ambiguous problems.
3. There are expert systems that can be diagnose
human illnesses, make financial forecast, and
schedule routes for delivery vehicles.
They also play chess, underwrite insurance policies,
and perform many services which previously required
human expertise.
Some expert systems are designed to take the place
of human experts, While others are designed to aid
them.
4. DEFINITION
“An expert system is an interactive computer-based
decision tool that uses both facts and heuristics to solve
difficult decision problems based on knowledge acquired
from an expert.”
An expert system compared with traditional computer :
Inference engine + Knowledge = Expert system
8. In addition to above components,
Working memory
Domain expert
Explanation capacity
A knowledge maintenance module to update the
knowledge base.
An interface to other systems, such as database
management and spreadsheets etc.
9. Components of Expert System
User Interface
Inference
Engine
Knowledge
Base
Working
Storage
User
System
Engineer
Domain
Expert
Expertise
Knowledge
Engineer
Encoded
Expertise
10. NEED OF EXPERT SYSTEM?
Human expertise is very scarce.
Humans get tired from physical or mental
workload.
Humans forget crucial details of a problem.
Humans are inconsistent in their day-to-day
decisions.
Humans have limited working memory.
Humans are unable to comprehend large
amounts of data quickly.
11. Humans are unable to retain large amounts
of data in memory.
Humans are slow in recalling information
stored in memory.
Humans are subject to deliberate or
inadvertent bias in their actions.
Humans can deliberately avoid decision
responsibilities.
Humans lie, hide, and die.
12. WHERE EXPERT SYSTEM ARE MOST
EFFECTIVE?
Decision Support
Problem-Solving Diagnostics
Data Analysis
Advisory System
Background Monitoring
Inconsistency Detection
Smart Questionnaires
Application in Agriculture
13. Diagnosis diseases of wheat
symptoms - recommendations.
Problem in leaves- disease may be
Stem rust(h1)
Problem in leaves- disease may be Flag
smut (h0)
14. The developmental process then adds
additional information to help determine
whether the hypothesis “disease is flag
smut on leaves” is true.
15. This is not as simple as it might look as
other considerations include
symptoms appeared on the defected
parts including size of patches
color of spots
condition of defected organ and other
information.
16. The representation of flag smut disease is
mentioned below.
• RULE [Is it going to find the problem
area which is flag smut?]
• If [season] = "spring" and
• [stage]="early" and
• [problem_is_in]="leaves" and
• [leave_part]="upper" and
• [strip_color]="light_green_to_grey_black
17. Then [the problem] = "FALG
SMUT IS DIAGNOSED"
• [recommendation 1]= "CAUSAL
ORGANISM: Urocystic tritici" and
• [recommendation 2]=
"PERPETUATION: The disease is
perpetuated through seed borne and/or
soil borne spores, which can survive in
the soil up to a period of three years"
and
• [
18. • recommendation 3]= "MANAGEMENT:
1. GROW RESISTANT CULTIVARS.
5. EARLY PLANTING
6. USING THE WET METHOD OF
SOWING. IRRIGATING JUST AFTER
SOWING.
7. ROGUE OUT AND BURN THE
INFECTED PLANT. "
22. EXPERT SYSTEM IN AGRICULTURE
AMRAPALIKA: An expert system for the diagnosis of
pests, diseases and disorders in Indian mango.
Dr. Wheat: A Web-based expert system for diagnosis
of diseases and pests in Pakistani wheat.
23. BENEFITS OF EXPERT SYSTEMS
Increase the probability, frequency, and
consistency of making good decisions.
Help distribute human expertise.
Facilitate real-time, low-cost expert level
decisions by the non expert.
Enhance the utilization of most of the
available data.
24. Permit objectivity by weighing evidence without bias
and without regard for the user’s personal and
emotional reactions.
Permit dynamism through modularity of structure.
Free up the mind and time of the human expert to
enable him or her to concentrate on more creative
activities.
Encourage investigations into the subtle areas of a
problem.
25. LIMITATIONS OF EXPERT SYSTEMS
Not widely used or tested.
Limited to relatively narrow problems.
No “common sense”.
Cannot readily deal with “mixed” knowledge.
Possibility of error.
Cannot refine own knowledge base.
Difficult to maintain.
System are not always up to date.
May have high development costs
26. Conclusion
An expert system is a powerful tool with
extensive potential in various field.
The development of an expert system
requires the combined effort of specialists
from many fields of different areas and can
only be accomplished with the cooperation of
the users and advisors who will use them.
