The document discusses the architecture and operation of production systems and rule-based expert systems. It describes the typical components of an expert system including the knowledge base, working memory, inference engine, and user interface. It also explains how production rules represent knowledge as IF-THEN statements and how a production system operates by matching rules in the knowledge base to the contents of working memory and firing rules to draw conclusions or take actions.
The document describes the typical architecture of a rule-based or production system. It consists of a working memory that holds facts, a rule memory that contains if-then rules, and an inference engine that fires applicable rules. New information is added to working memory, which can trigger rules whose conditions are satisfied, causing the inference engine to execute their actions and modify working memory. This cycle of matching rules, executing actions, and updating working memory allows the system to draw new conclusions from the initial facts.
Expert systems are computer programs that contain knowledge from human experts and use logical rules to solve problems in a specific domain. They have four main components: a knowledge base that stores rules and data, an inference engine that applies rules to solve problems, an explanation facility to explain solutions, and a user interface. While expert systems were widely developed in the 1980s and 1990s, they have limitations such as a narrow domain of knowledge and inability to learn.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Production systems represent knowledge as rules that specify conclusions that can be drawn from different situations. A rule-based system consists of IF-THEN rules, facts, and an interpreter. Rules are a popular knowledge representation technique because they allow for modular, explainable knowledge that can be incrementally expanded, similar to human cognitive processes.
Expert Systems are computer programs that use knowledge and inference procedures to solve problems that normally require human expertise. They are designed to solve problems at an expert level by accessing a substantial knowledge base and applying reasoning mechanisms. Typical tasks for expert systems include data interpretation, diagnosis, structural analysis, planning, and prediction. Expert systems consist of a knowledge base, inference engine, user interface, knowledge acquisition system, and explanation facility. The inference engine applies rules and reasoning to the knowledge base to solve problems. Knowledge acquisition involves eliciting expertise from human experts to build the knowledge base.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
This document provides an introduction to expert systems. It begins by defining an expert system as an information system that uses human knowledge stored in a computer to solve problems that usually require human expertise. It then lists some common applications of expert systems, such as diagnosing medical conditions, mechanical issues, and identifying security threats. The document also discusses the basic components of an expert system, including the knowledge base which stores facts and rules, the inference engine which uses reasoning to draw conclusions, and the explanation facility which explains the system's decisions.
The document discusses the basic activities and features of expert systems, including interpretation of data, prediction, diagnosis, design, monitoring, planning, debugging, repair, instruction, and control. It also describes knowledge representation techniques like semantic nets, frames, slots, and forward and backward reasoning. The stages of expert system development include identification, conceptualization, formalization, system design, development, testing and evaluation, and revision. Common programming methods are rule-based, frame-based, procedure-oriented, object-oriented, and logic-based. Expert system building tools include shells that provide basic components like a knowledge base and reasoning engine.
The document describes the typical architecture of a rule-based or production system. It consists of a working memory that holds facts, a rule memory that contains if-then rules, and an inference engine that fires applicable rules. New information is added to working memory, which can trigger rules whose conditions are satisfied, causing the inference engine to execute their actions and modify working memory. This cycle of matching rules, executing actions, and updating working memory allows the system to draw new conclusions from the initial facts.
Expert systems are computer programs that contain knowledge from human experts and use logical rules to solve problems in a specific domain. They have four main components: a knowledge base that stores rules and data, an inference engine that applies rules to solve problems, an explanation facility to explain solutions, and a user interface. While expert systems were widely developed in the 1980s and 1990s, they have limitations such as a narrow domain of knowledge and inability to learn.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Production systems represent knowledge as rules that specify conclusions that can be drawn from different situations. A rule-based system consists of IF-THEN rules, facts, and an interpreter. Rules are a popular knowledge representation technique because they allow for modular, explainable knowledge that can be incrementally expanded, similar to human cognitive processes.
Expert Systems are computer programs that use knowledge and inference procedures to solve problems that normally require human expertise. They are designed to solve problems at an expert level by accessing a substantial knowledge base and applying reasoning mechanisms. Typical tasks for expert systems include data interpretation, diagnosis, structural analysis, planning, and prediction. Expert systems consist of a knowledge base, inference engine, user interface, knowledge acquisition system, and explanation facility. The inference engine applies rules and reasoning to the knowledge base to solve problems. Knowledge acquisition involves eliciting expertise from human experts to build the knowledge base.
Problem Decomposition: Goal Trees, Rule Based Systems, Rule Based Expert Systems. Planning:
STRIPS, Forward and Backward State Space Planning, Goal Stack Planning, Plan Space Planning,
A Unified Framework For Planning. Constraint Satisfaction : N-Queens, Constraint Propagation,
Scene Labeling, Higher order and Directional Consistencies, Backtracking and Look ahead
Strategies.
This document provides an introduction to expert systems. It begins by defining an expert system as an information system that uses human knowledge stored in a computer to solve problems that usually require human expertise. It then lists some common applications of expert systems, such as diagnosing medical conditions, mechanical issues, and identifying security threats. The document also discusses the basic components of an expert system, including the knowledge base which stores facts and rules, the inference engine which uses reasoning to draw conclusions, and the explanation facility which explains the system's decisions.
The document discusses the basic activities and features of expert systems, including interpretation of data, prediction, diagnosis, design, monitoring, planning, debugging, repair, instruction, and control. It also describes knowledge representation techniques like semantic nets, frames, slots, and forward and backward reasoning. The stages of expert system development include identification, conceptualization, formalization, system design, development, testing and evaluation, and revision. Common programming methods are rule-based, frame-based, procedure-oriented, object-oriented, and logic-based. Expert system building tools include shells that provide basic components like a knowledge base and reasoning engine.
A production system consists of productions (rules), knowledge databases, a control strategy, and a rule applier. Productions have two parts - a condition and an action. The control strategy determines the order of rule application and resolves conflicts. Production systems represent knowledge as "If (condition) Then (action)" rules and a database. They can be classified as monotonic, non-monotonic, or partially commutative based on rule application properties. Production systems are simple, modular, modifiable and knowledge-intensive but can also be opaque, inefficient and lack learning abilities.
Hybrid Model using Unsupervised Filtering Based on Ant Colony Optimization an...IRJET Journal
This document proposes a hybrid model for medical data mining that uses unsupervised filtering followed by ant colony optimization and multiclass support vector machines. It first discusses data mining and describes ant colony optimization, random forests, and ant colony decision trees. It then explains the proposed hybrid model, which applies unsupervised filtering techniques to raw medical data before using ant colony optimization to build a decision tree. Finally, it briefly introduces multiclass support vector machines as the final component of the hybrid model. The overall goal is to extract useful information and patterns from medical data using this combined approach.
The document discusses knowledge-based systems and artificial neural networks. It describes an early expert system developed in 1980 to approve credit applications. It also outlines the key components of expert systems, including the knowledge base and rules. Neural networks are discussed as being inspired by the human brain and capable of learning in a similar way. The multi-layer perception model is presented as a way to break tasks into smaller subtasks performed concurrently.
Artificial Intelligence data related to aichougulesup79
This document provides an overview of logical reasoning systems and different types of knowledge representation and reasoning approaches. It discusses production systems, which are rule-based systems that use an "if-then" rule format. Production systems have three main components: a rule base containing rules, a working memory of facts, and an inference engine. The inference engine uses a match-resolve-act cycle to apply rules by matching facts in working memory and executing rule actions. The document also briefly describes semantic networks, which represent knowledge through labeled nodes and links to capture relationships between concepts.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
Automated identification of sensitive informationJeff Long
October 21, 1999: "Using Ultra-Structure for Automated Identification of Sensitive Information in Documents". Presented at the 20th annual conference of the American Society for Engineering Management. Paper published in conference proceedings.
Expert systems are knowledge-based programs that use specialized knowledge to solve problems in a particular domain. They consist of a knowledge base containing rules and a navigational capability called an inference engine. Knowledge is extracted from human experts and encoded in the knowledge base. The key components of an expert system are the knowledge base, inference engine, knowledge acquisition module, explanation module, and user interface.
Expert system prepared by fikirte and hayat im assignmentfikir getachew
The document discusses expert systems, their components, types, and uses. An expert system is an intelligent system that can perform complex tasks like a human expert. It consists of a knowledge base, inference engine, user interface, interpreter, and blackboard. Expert systems are classified based on their function, such as for interpretation, prediction, diagnosis, design, or planning. They can benefit industries and countries by advancing fields like agriculture, education, medicine, and more.
The document provides an overview of systems, including definitions and common types. It discusses systems as having components, boundaries, inputs, outputs and purposes. Natural systems include physical and living systems, while man-made systems include social, organizational and information systems. The roles in systems development projects are outlined, including users at different levels and with varying experience who have different needs. The systems analyst must understand the perspectives of operational, supervisory and executive level users.
An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that require significant human expertise. It emulates the decision-making ability of a human expert in a narrow problem domain. The basic components of an expert system are a knowledge base containing the expert knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems have advantages over human experts such as increased availability, reduced costs, reliability, and ability to provide detailed explanations. However, they are limited compared to human experts in areas such as causal knowledge, knowledge depth, and analogical reasoning.
An expert system is software that attempts to reproduce the performance of one or more human experts in a specific problem domain. It contains a knowledge base of rules, an inference engine to manipulate the rules, and a user interface. Early expert systems were created in the 1970s and proliferated in the 1980s, being among the first truly successful forms of AI software. They derive their power from the knowledge in their knowledge bases rather than specific formalisms.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
The document provides an introduction to expert systems. It defines an expert system as a computer system that emulates the decision-making abilities of a human expert. The key components of an expert system are a knowledge base containing the expertise knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems offer advantages like increased availability, reduced costs, reliability, and the ability to explain their reasoning. However, they also have limitations like dealing with uncertainty and an inability to generalize knowledge like humans.
The document discusses expert systems, which are computer programs that simulate human expertise to solve complex problems. It defines expert systems and describes their key components, including the knowledge base, inference engine, working memory, and explanation facility. The document also outlines the structure of rule-based expert systems, explaining how production rules work and the two main methods of inference: forward chaining and backward chaining. Finally, it briefly discusses the roles of domain experts, knowledge engineers, and knowledge users in developing expert systems.
Applying a new software development paradigm to biologyJeff Long
May 7-11, 2003: Giddings, M. C. and Long, J. “Applying a New Software Development Paradigm to Biology: Developing applications that handle complexity and stand the test of time”. Poster session presented with Dr. M. C. Giddings, of the University of North Carolina, Chapel Hill, at the Genome Informatics Conference, sponsored by Cold Spring Harbor Laboratory.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and examples of their applications.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and potential applications in areas like diagnosis, planning, and monitoring.
Survey: Biological Inspired Computing in the Network SecurityEswar Publications
Traditional computing techniques and systems consider a main process device or main server, and technique details generally
serially. They're non-robust and non-adaptive, and have limited quantity. Indifference, scientific technique details in a very similar and allocated manner, while not a main management. They're exceedingly strong, elastic, and ascendible. This paper offers a short conclusion of however the ideas from biology are will never to style new processing techniques and techniques that even have a number of the beneficial qualities of scientific techniques. Additionally, some illustrations are a device given of however these techniques will be used in details security programs.
The document discusses the history and development of expert systems. It describes some of the earliest expert systems like DENDRAL and MYCIN, which demonstrated that expert knowledge could be encoded in a computer system. The key aspects of expert systems are discussed, including what characterizes a human expert, how expert knowledge is represented in an expert system, and the typical components of an expert system like the knowledge base, inference engine, and user interface. The roles of domain experts, knowledge engineers, and system engineers in building expert systems are also outlined.
The document discusses the architecture and operation of production systems and rule-based expert systems. It describes the typical components of an expert system, including the knowledge base containing rules, working memory containing facts, and an inference engine that applies rules. It explains that the inference engine selects rules from the knowledge base according to what is in working memory, fires the rules to draw conclusions or take actions, and modifies working memory. The document also discusses terminology related to production rules and systems, as well as how production systems operate through a recognize-act cycle of matching rules to working memory and firing rules.
This document proposes the development of a computer aided crime investigation system for developing countries. It describes an expert system that would store knowledge from human experts in crime investigation and apply intelligent processing to aid in investigations. The goals are to enhance the work of human experts and provide a system for learning crime investigation. It outlines the proposed architecture, including a knowledge base storing facts and experiences, an inference engine to make deductions, and a user interface. The focus is on developing a system to help address challenges with current manual investigation methods.
A production system consists of productions (rules), knowledge databases, a control strategy, and a rule applier. Productions have two parts - a condition and an action. The control strategy determines the order of rule application and resolves conflicts. Production systems represent knowledge as "If (condition) Then (action)" rules and a database. They can be classified as monotonic, non-monotonic, or partially commutative based on rule application properties. Production systems are simple, modular, modifiable and knowledge-intensive but can also be opaque, inefficient and lack learning abilities.
Hybrid Model using Unsupervised Filtering Based on Ant Colony Optimization an...IRJET Journal
This document proposes a hybrid model for medical data mining that uses unsupervised filtering followed by ant colony optimization and multiclass support vector machines. It first discusses data mining and describes ant colony optimization, random forests, and ant colony decision trees. It then explains the proposed hybrid model, which applies unsupervised filtering techniques to raw medical data before using ant colony optimization to build a decision tree. Finally, it briefly introduces multiclass support vector machines as the final component of the hybrid model. The overall goal is to extract useful information and patterns from medical data using this combined approach.
The document discusses knowledge-based systems and artificial neural networks. It describes an early expert system developed in 1980 to approve credit applications. It also outlines the key components of expert systems, including the knowledge base and rules. Neural networks are discussed as being inspired by the human brain and capable of learning in a similar way. The multi-layer perception model is presented as a way to break tasks into smaller subtasks performed concurrently.
Artificial Intelligence data related to aichougulesup79
This document provides an overview of logical reasoning systems and different types of knowledge representation and reasoning approaches. It discusses production systems, which are rule-based systems that use an "if-then" rule format. Production systems have three main components: a rule base containing rules, a working memory of facts, and an inference engine. The inference engine uses a match-resolve-act cycle to apply rules by matching facts in working memory and executing rule actions. The document also briefly describes semantic networks, which represent knowledge through labeled nodes and links to capture relationships between concepts.
1. The document describes an expert system and its components.
2. It defines an expert system as an intelligent computer program that uses knowledge and reasoning to solve problems that usually require human expertise.
3. The key components of an expert system are the knowledge base, inference engine, explanation facility, and knowledge acquisition facility.
Automated identification of sensitive informationJeff Long
October 21, 1999: "Using Ultra-Structure for Automated Identification of Sensitive Information in Documents". Presented at the 20th annual conference of the American Society for Engineering Management. Paper published in conference proceedings.
Expert systems are knowledge-based programs that use specialized knowledge to solve problems in a particular domain. They consist of a knowledge base containing rules and a navigational capability called an inference engine. Knowledge is extracted from human experts and encoded in the knowledge base. The key components of an expert system are the knowledge base, inference engine, knowledge acquisition module, explanation module, and user interface.
Expert system prepared by fikirte and hayat im assignmentfikir getachew
The document discusses expert systems, their components, types, and uses. An expert system is an intelligent system that can perform complex tasks like a human expert. It consists of a knowledge base, inference engine, user interface, interpreter, and blackboard. Expert systems are classified based on their function, such as for interpretation, prediction, diagnosis, design, or planning. They can benefit industries and countries by advancing fields like agriculture, education, medicine, and more.
The document provides an overview of systems, including definitions and common types. It discusses systems as having components, boundaries, inputs, outputs and purposes. Natural systems include physical and living systems, while man-made systems include social, organizational and information systems. The roles in systems development projects are outlined, including users at different levels and with varying experience who have different needs. The systems analyst must understand the perspectives of operational, supervisory and executive level users.
An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that require significant human expertise. It emulates the decision-making ability of a human expert in a narrow problem domain. The basic components of an expert system are a knowledge base containing the expert knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems have advantages over human experts such as increased availability, reduced costs, reliability, and ability to provide detailed explanations. However, they are limited compared to human experts in areas such as causal knowledge, knowledge depth, and analogical reasoning.
An expert system is software that attempts to reproduce the performance of one or more human experts in a specific problem domain. It contains a knowledge base of rules, an inference engine to manipulate the rules, and a user interface. Early expert systems were created in the 1970s and proliferated in the 1980s, being among the first truly successful forms of AI software. They derive their power from the knowledge in their knowledge bases rather than specific formalisms.
Introduction to Expert Systems {Artificial Intelligence}FellowBuddy.com
The document provides an introduction to expert systems. It defines an expert system as a computer system that emulates the decision-making abilities of a human expert. The key components of an expert system are a knowledge base containing the expertise knowledge and an inference engine that draws conclusions from the knowledge base. Expert systems offer advantages like increased availability, reduced costs, reliability, and the ability to explain their reasoning. However, they also have limitations like dealing with uncertainty and an inability to generalize knowledge like humans.
The document discusses expert systems, which are computer programs that simulate human expertise to solve complex problems. It defines expert systems and describes their key components, including the knowledge base, inference engine, working memory, and explanation facility. The document also outlines the structure of rule-based expert systems, explaining how production rules work and the two main methods of inference: forward chaining and backward chaining. Finally, it briefly discusses the roles of domain experts, knowledge engineers, and knowledge users in developing expert systems.
Applying a new software development paradigm to biologyJeff Long
May 7-11, 2003: Giddings, M. C. and Long, J. “Applying a New Software Development Paradigm to Biology: Developing applications that handle complexity and stand the test of time”. Poster session presented with Dr. M. C. Giddings, of the University of North Carolina, Chapel Hill, at the Genome Informatics Conference, sponsored by Cold Spring Harbor Laboratory.
Ali Akram Saber's document discusses intelligent urban traffic control systems using various artificial intelligence techniques. It covers neural networks, genetic algorithms, expert systems, fuzzy logic, and rule-based systems. Neural networks can be separated into models, networks, and learning rules. Genetic algorithms mimic natural selection to find solutions. Expert systems contain knowledge bases and reasoning engines. Rule-based systems separate knowledge from execution. Fuzzy logic handles approximate reasoning between true and false values.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and examples of their applications.
This document discusses expert systems and their application in road transport. It begins with definitions of expert systems and how they emulate human decision making. It then outlines the typical design of rule-based expert systems, including the knowledge base and inference engine. Next, it describes the six phase development process for building an expert system and provides comments on each phase. It also discusses rule-based reasoning approaches, including goal-driven and data-driven reasoning. Finally, it lists some advantages of expert systems and potential applications in areas like diagnosis, planning, and monitoring.
Survey: Biological Inspired Computing in the Network SecurityEswar Publications
Traditional computing techniques and systems consider a main process device or main server, and technique details generally
serially. They're non-robust and non-adaptive, and have limited quantity. Indifference, scientific technique details in a very similar and allocated manner, while not a main management. They're exceedingly strong, elastic, and ascendible. This paper offers a short conclusion of however the ideas from biology are will never to style new processing techniques and techniques that even have a number of the beneficial qualities of scientific techniques. Additionally, some illustrations are a device given of however these techniques will be used in details security programs.
The document discusses the history and development of expert systems. It describes some of the earliest expert systems like DENDRAL and MYCIN, which demonstrated that expert knowledge could be encoded in a computer system. The key aspects of expert systems are discussed, including what characterizes a human expert, how expert knowledge is represented in an expert system, and the typical components of an expert system like the knowledge base, inference engine, and user interface. The roles of domain experts, knowledge engineers, and system engineers in building expert systems are also outlined.
The document discusses the architecture and operation of production systems and rule-based expert systems. It describes the typical components of an expert system, including the knowledge base containing rules, working memory containing facts, and an inference engine that applies rules. It explains that the inference engine selects rules from the knowledge base according to what is in working memory, fires the rules to draw conclusions or take actions, and modifies working memory. The document also discusses terminology related to production rules and systems, as well as how production systems operate through a recognize-act cycle of matching rules to working memory and firing rules.
This document proposes the development of a computer aided crime investigation system for developing countries. It describes an expert system that would store knowledge from human experts in crime investigation and apply intelligent processing to aid in investigations. The goals are to enhance the work of human experts and provide a system for learning crime investigation. It outlines the proposed architecture, including a knowledge base storing facts and experiences, an inference engine to make deductions, and a user interface. The focus is on developing a system to help address challenges with current manual investigation methods.
Google Docs allows users to create and collaborate on documents online for free. It includes word processing, spreadsheet, presentation, drawing, and forms tools. Documents can be accessed from any device and shared with others to work simultaneously. Revisions are tracked, and multiple file formats can be uploaded and downloaded. Creating a Google account provides online storage and allows editing shared documents. Folders help organize documents, which can be published online or embedded in blogs and websites for sharing.
This document summarizes several agile software development methodologies including:
- Extreme Programming (XP) which emphasizes iterative development, unit testing, and pair programming.
- Adaptive Software Development (ASD) which focuses on collaboration, self-organization, and learning through iterations.
- Dynamic Systems Development Method (DSDM) which provides an agile framework using incremental prototyping.
- Scrum which partitions work into packets and emphasizes ongoing testing and documentation.
All the methodologies aim to improve responsiveness to change through principles like iterative delivery, collaboration, and simplicity.
This document contains a series of slides about software engineering principles from the textbook "Software Engineering: A Practitioner's Approach". The slides cover principles related to software engineering knowledge, process, practice, communication, and planning. They define key principles such as focusing on quality, managing change, dividing problems concisely, and involving customers in planning. The document provides an overview of fundamental principles that guide software engineering work.
The document introduces an artificial intelligence course, discussing why AI is studied, potential benefits, and definitions of AI. It explores different approaches to AI like systems that act intelligently by passing the Turing test or thinking rationally. The document also provides a brief history of AI, discussing pioneers in the field and important questions and challenges in developing intelligent systems.
This document discusses cloud storage options and provides details on Google Drive. It asks if the reader wants to store files securely online and lists several cloud providers like Apple iCloud, Dropbox, Google Drive, Amazon Cloud Drive, and Microsoft SkyDrive. Google Drive is highlighted as offering 15GB of free storage and allowing users to create and edit docs, sheets and slides in the cloud. Steps to get started with Google Drive are outlined, including uploading files, sharing content with others, and organizing files.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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3. Expert system architecture (1)
The typical architecture of an e.s. is often
described as follows:
user
user
interface
inference
engine
knowledge
base
4. Expert system architecture (1)
The inference engine and knowledge
base are separated because:
the reasoning mechanism needs to be as
stable as possible;
the knowledge base must be able to grow
and change, as knowledge is added;
this arrangement enables the system to be
built from, or converted to, a shell.
5. Expert system architecture (2)
It is reasonable to produce a richer,
more elaborate, description of the typical
expert system.
A more elaborate description, which still
includes the components that are to be
found in almost any real-world system,
would look like this:
8. Expert system architecture (2)
The system holds a collection of general
principles which can potentially be applied to
any problem - these are stored in the
knowledge base.
The system also holds a collection of specific
details that apply to the current problem
(including details of how the current
reasoning process is progressing) - these are
held in working memory.
Both these sorts of information are processed
by the inference engine.
10. Expert system architecture (2)
Any practical expert system needs an
explanatory facility. It is essential that an
expert system should be able to explain
its reasoning. This is because:
14. Expert system architecture (2)
It is not unreasonable to include an
expert interface & a knowledge base
editor, since any practical expert system
is going to need a mechanism for
efficiently building and modifying the
knowledge base.
18. Rule-based reasoning
One can often represent the expertise
that someone uses to do an expert task
as rules.
A rule means a structure which has an if
component and a then component.
This is actually a very old idea indeed -
19. The Edwin Smith papyrus
The Edwin Smith papyrus is a 3700-
year-old ancient Egyptian text.
ABCDEECDBBACDACDBCDECDADCADBADE
ECDBBACDACDBCDECDADCADBADCDBBACDA
BCDEECDBBACDACDBCDECDAD
BBACDACDBCDECDADCADBADEDCDBBA
DCDBBADCDBBABCDECDADCADBADEACDA
BACDACDBCDECDADBACDACDBCDECDAD
20. The Edwin Smith papyrus
It contains medical descriptions of 48
different types of head wound.
There is a fixed format for each problem
description: Title - symptoms - diagnosis
- prognosis - treatment.
21. The Edwin Smith papyrus
There's a fixed style for the parts of each
problem description. Thus, the prognosis
always reads "It is an injury that I will
cure", or "It is an injury that I will
combat", or "It is an injury against which
I am powerless".
An example taken from the Edwin Smith
papyrus:
22. The Edwin Smith papyrus
Title:
Instructions for treating a fracture of the
cheekbone.
Symptoms:
If you examine a man with a fracture of the
cheekbone, you will find a salient and
red fluxion, bordering the wound.
23. The Edwin Smith papyrus
Diagnosis and prognosis:
Then you will tell your patient: "A fracture of
the cheekbone. It is an injury that I will
cure."
Treatment:
You shall tend him with fresh meat the first
day. The treatment shall last until the fluxion
resorbs. Next you shall treat him with
raspberry, honey, and bandages to be
renewed each day, until he is cured.
24. Rule-based reasoning: rules
examples:
if - the leaves are dry, brittle and
discoloured
then - the plant has been attacked by red
spider mite
if - the customer closes the account
then - delete the customer from the
database
25. Rule-based reasoning: rules
The statement, or set of statements,
after the word if represents some pattern
which you may observe.
The statement, or set of statements,
after the word then represents some
conclusion that you can draw, or some
action that you should take.
26. Rule-based reasoning: rules
A rule-based system, therefore, either
identifies a pattern and draws
conclusions about what it means,
or
identifies a pattern and advises what
should be done about it,
or
identifies a pattern and takes
appropriate action.
27. Rule-based reasoning: rules
The essence of a rule-based reasoning system is
that it goes through a series of cycles.
In each cycle, it attempts to pick an appropriate rule
from its collection of rules, depending on the
present circumstances, and to use it as described
above.
Because using a rule produces new information, it's
possible for each new cycle to take the reasoning
process further than the cycle before. This is rather
like a human following a chain of ideas in order to
come to a conclusion.
28. Terminology
A rule as described above is often
referred to as a production rule.
A set of production rules, together with
software that can reason with them, is
known as a production system.
29. Terminology
There are several different terms for the statements
that come after the word if, and those that come after
the word then.
The statements after if may be called the
conditions, those after then may be called the
conclusions.
The statements after if may be called the premises,
those after then may be called the actions.
The statements after if may be called the
antecedents, those after then may be called the
consequents.
31. Terminology
If a production system chooses a
particular rule, because the conditions
match the current state of affairs, and
puts the conclusions into effect, this is
known as firing the rule.
32. Terminology
In a production system, the rules are
stored together, in an area called the
rulebase.
33. Historical note
Mathematicians, linguists, psychologists
and artificial intelligence specialists
explored the possibilities of production
rules during the 40s, 50s and 60s.
When the first expert systems were
invented in the 70s, it seemed natural to
use production rules as the knowledge
representation formalism for the
knowledge base.
34. Historical note
Production rules have remained the
most popular form of knowledge
representation for expert systems ever
since.
35. Conditional branching
Is a production rule the same as a
conditional branching statement?
A production rule looks similar to the
if (statement to be evaluated) then (action)
pattern which is a familiar feature of all
conventional programming languages.
37. Conditional branching
{ int magic;
int guess;
magic = rand( );
printf(“guess the magic number: ”);
scanf(“%d”, &guess);
if (guess == magic) printf(“** Right **”);
else {
printf(“Wrong, ”);
if (guess > magic) printf(“too high”);
else printf(“too low”);
}
}
38. Conditional branching vs. production
rules
However, the similarity is misleading.
There is a radical difference between a
production system and a piece of
conventional software.
In a conventional program, the
if...then... structure is an integral part
of the code, and represents a point
where the execution can branch in
one of two (or more) directions.
39. Conditional branching vs. production
rules
In a production system, the if...then...
rules are gathered together in a rule
base, and the controlling part of the
system has some way of choosing a
rule from this knowledge base which
is appropriate to the current
circumstances, and then using it.
40. Reasoning with production rules
The statements forming the conditions,
or the conclusions, in such rules, may
be structures, following some syntactic
convention (such as three items
enclosed in brackets).
41. Reasoning with production rules
Very often, these structures will include
variables - such variables can, of
course, be given a particular value, and
variables with the same name in the
same rule will share the same value.
42. Reasoning with production rules
For example (assuming words beginning
with capital letters are variables, and
other words are constants):
if [Person, age, Number] &
[Person, employment, none] &
[Number, greater_than, 18] &
[Number, less_than, 65]
then [Person, can_claim,
unemployment_benefit].
43. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
interpreter
working
memory
observed data
fire
modify
select
output
44. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
interpreter
working
memory
New information
fire
modify
select
output
45. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
interpreter
working
memory
New information
fire
modify
select
output
46. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
Interprete
r
executes
actions
working
memory
New information
fire
modify
select
output
47. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
Interprete
r
executes
actions
working
memory
New information
fire
modify
select
output
48. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
interpreter
working
memory
New information
fire
modify
select
output
49. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
Interprete
r
executes
actions
working
memory
New information
fire
modify
select
output
50. Reasoning with production rules
Architecture of a typical production
system:
rule
memory
Interprete
r
executes
actions
working
memory
New information
fire
modify
select
output
51. Architecture of a typical production system
Has a working memory.
Holds items of data. Their presence, or
their absence, causes the interpreter to
trigger certain rules.
e.g. W.M. contains [john, age, 29] &
[john, employment, none]
The system decides: does this match
any rules in the rulebase? If so, choose
the rule.
52. Architecture of a typical production system
has an interpreter. Behaviour of the
interpreter:
the system is started by putting a
suitable data item into working memory.
recognise-act cycle: when data in the
working memory matches the conditions
of one of the rules in the system, the rule
fires (i.e.is brought into action).
53. Advantages of production
systems ... at first glance
The principle advantage of production
rules is notational convenience - it’s
easy to express suitable pieces of
knowledge in this way.
The principle disadvantage of production
rules is their restricted power of
expression - many useful pieces of
knowledge don’t fit this pattern.
54. Advantages of production
systems ... at first glance
This would seem to be a purely declarative
form of knowledge representation. One
gathers pieces of knowledge about a
particular subject, and puts them into a
rulebase. One doesn't bother about when or
how or in which sequence the rules are used;
the production system can deal with that.
When one wishes to expand the knowledge,
one just adds more rules at the end of the
rulebase.
55. Advantages of production
systems ... at first glance
The rules themselves are very easy to
understand, and for someone (who is expert
in the specific subject the system is
concerned with) to criticise and improve.
56. Advantages of production
systems ... at first glance
It's fairly straightforward to implement a
production system interpreter. Following the
development of the Rete Matching
Algorithm, and other improvements, quite
efficient interpreters are now available.
58. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
59. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
60. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
61. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
62. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
63. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
64. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
65. Operation of a production system
in more detail
The recognise-act cycle (forward-chaining):
Halt
yes
no
Has
the rule
got the
command
"halt" at
Produce some output
Put the right-hand side
of the rule into effect,
using the information
from working memory
Halt
Pick rules on the
basis of what's in
working memory
Set the cycle going
Put the word "start"
in working memory
Use conflict resolution
strategy to cut this
down to one rule.
the
end?
Any
rules
eligible
to fire
?
no
yes
Information
sources & recipients
the working
user memory
67. The recognise-act cycle
conflict resolution strategy: if more than
one rule matches working memory
contents, this decides which one is to
fire. Alternatively, the rulebase could be
designed so there's never any conflict
(but usually isn't).
68. The recognise-act cycle
Applying the rule will probably modify
the contents of working memory. Then
the system continues with the
recognise-act cycle.
The system stops when
the rules stop firing, or
a rule fires which specifically tells the
system to halt.
69. Conflict resolution strategies
Choice of c.r.s. can make a big
difference to system performance.
Three favourite strategies:
Refractoriness: don't allow a rule to fire
twice on same data.
Recency: take the data which arrived in
working memory most recently, and find a
rule that uses this data.
Specificity: use the most specific rule (the
one with the most conditions attached).
70. Conflict resolution strategies
However, in recent years the fashion (in
expert system shells) has been for very
simple CRSs, coupled with a reluctance
to mention the problem to the potential
system builder.
Simple strategies:
Give each rule a priority number. If a choice
has to be made, choose the rule with the
highest number.
If a choice has to be made, choose the rule
that comes first in the rule base.
71. Advantages of production
systems reconsidered.
Because of the effect of conflict
resolution strategies, rules interact and
the order of rules matters.
One must go beyond the declarative
meaning of the rules and consider when
(under which circumstances) they will fire.
One cannot properly understand a rule
simply by reading it in isolation; one must
consider the related rules, the meta-rules,
and the conflict resolution strategy as well.
72. Advantages of production
systems reconsidered.
For the same reason, attempting to
expand a production system by simply
adding more rules at the end is
dangerous.
Unexpected rule interactions are liable
to happen.
The need to consider all these
possible rule interactions makes large
rule-based systems unwieldy and hard
to update.
73. Advantages of production
systems reconsidered.
Although non-computer-specialists find it
easy to grasp the meaning of individual
rules, they don't find it easy to grasp
these issues concerned with
interactions.
74. Advantages of production
systems reconsidered.
Although efficient rule interpreters are
available, one may still need to engage
in meta-level programming in order to
achieve a production system that shows
acceptable performance on a large
rulebase.
75. Exercise on production rules
The following is a set of instructions
taken from the workshop manual for a
Nissan car:
Topic: starter system troubles.
76. Exercise on production rules
Procedures: Try to crank the starter. If it is dead
or cranks slowly, turn on the headlights. If the
headlights are bright, the trouble is either in
the starter itself, the solenoid, or in the wiring.
To find the trouble, short the two large
solenoid terminals together. If the starter
cranks normally, the problem is in the wiring
up to the ignition switch or in the solenoid;
check them. If the starter does not work
normally check the bushings.
77. Exercise on production rules
If the bushings are good send the starter to a
test station or replace it. If the headlights are
out, or very dim, check the battery. If the
battery is OK, check the wiring for breaks,
shorts, and dirty connections. If the battery
and connecting wires are not at fault, turn the
headlights on and try to crank the starter. If
the lights dim drastically, it is probably
because the starter is shorted to ground.
Have the starter tested or replace it.
78. Exercise on production rules
As an exercise, translate this information
into production rules.
Write the conditions and conclusions, but
use IF, THEN, AND, OR as keywords.
You are allowed to use a condition having
the form “You crank the starter and it is
dead” - in other words a test, together with
what you observe when you do it.
79. Exercise on production rules
Each conclusion should be a deduction you
can make about what is wrong, or an action
you should take.
You are allowed to use the word "probably"
in the conclusion of a rule.
Try not to use more than 7 rules.