Unit 2 discusses knowledge representation in artificial intelligence. It describes knowledge representation as the process of representing knowledge in a form that enables an AI system to reason with it and use it to solve problems. There are several types of knowledge that can be represented, including declarative, procedural, heuristic, and structural knowledge. Common approaches to knowledge representation include simple relational knowledge, inheritable knowledge, inferential knowledge, and procedural knowledge. Logical representation is a core technique that uses formal logic to represent knowledge through propositions and inference rules. Propositional logic represents the simplest form of logical representation using atomic and compound propositions connected by logical operators.
The document discusses different techniques for knowledge representation in artificial intelligence. It describes knowledge representation as allowing machines to understand and utilize knowledge to solve complex problems like human-human communication. It then covers various types of knowledge representation including declarative, procedural, meta-knowledge and more. Finally, it discusses common techniques for knowledge representation such as logical representation, semantic networks, frames, production rules and more.
Unit 2 discusses knowledge representation, which is important for intelligent systems to achieve useful tasks. It cannot be done without a large amount of domain-specific knowledge. Humans tackle problems using their knowledge resources, so knowledge must be represented inside computers for AI programs to manipulate. The document then defines knowledge representation as the part of AI concerned with how agents think and how thinking enables intelligent behavior. It represents real-world information so computers can understand and utilize knowledge to solve complex problems.
This document provides an overview of unit 4 on logical agents and planning in artificial intelligence. It discusses inference in propositional and first-order logic, logic programming, and different approaches to planning problems including state-space search, partial order planning, and both forward and backward search methods. Textbook and reference information is also provided.
Foundations of Knowledge Representation in Artificial Intelligence.pptxkitsenthilkumarcse
Knowledge representation in artificial intelligence (AI) is a fundamental concept that involves the process of structuring and encoding knowledge so that AI systems can understand, reason, and make decisions. Effective knowledge representation is essential for AI systems to model and work with complex real-world information. Here are some key aspects of knowledge representation in AI:
Symbolic Knowledge Representation: This approach uses symbols and rules to represent knowledge. It involves encoding information using symbols, predicates, and logical statements. Common formalisms include first-order logic and propositional logic. Symbolic representation is particularly suited for knowledge-based systems and expert systems.
Semantic Networks: In a semantic network, knowledge is represented using nodes and links to denote relationships between concepts. This form of representation is intuitive and is often used for organizing knowledge in a structured manner.
Frames and Ontologies: Frames and ontologies are used to represent knowledge by structuring information into frames or classes. Frames contain attributes and values, and they help in organizing and categorizing knowledge. Ontologies, such as OWL (Web Ontology Language), provide a more formal representation of knowledge for use in the semantic web and knowledge graphs.
Rule-Based Systems: Rule-based systems use a set of rules to represent and reason with knowledge. These rules can be encoded in the form of "if-then" statements, allowing AI systems to make decisions and draw inferences.
Fuzzy Logic: Fuzzy logic allows for the representation of uncertainty and vagueness in knowledge. It is particularly useful in situations where information is not black and white but falls within degrees of truth.
Bayesian Networks: Bayesian networks represent knowledge using probability distributions and conditional dependencies. They are valuable for modeling uncertain or probabilistic relationships in various domains, such as medical diagnosis and risk analysis.
Connectionist Models: Connectionist models, like neural networks, use distributed representations to encode knowledge. In these models, knowledge is spread across interconnected nodes (neurons), and learning occurs through the adjustment of connection weights. These networks are particularly effective in tasks such as pattern recognition and natural language processing.
Hybrid Approaches: Many AI systems use a combination of different knowledge representation techniques to address the complexities of real-world problems. For instance, combining symbolic representation with connectionist models is a common approach in modern AI.
The choice of knowledge representation method depends on the specific problem domain, the nature of the data, and the requirements of the AI system.
Artificial intelligence represents knowledge in a form that computer systems can use to solve complex tasks. There are different types of knowledge, including declarative knowledge which represents facts and concepts, and procedural knowledge which represents how to perform tasks. Knowledge representation approaches in artificial intelligence include simple relational knowledge, inheritable knowledge which uses hierarchies and inheritance, inferential knowledge which represents knowledge through formal logic, and procedural knowledge which represents knowledge through programs and rules.
This document discusses the architecture of knowledge-based systems (KBS). It explains that a KBS contains a knowledge module called the knowledge base (KB) and a control module called the inference engine. The KB explicitly represents knowledge that can be easily updated by domain experts without programming expertise. A knowledge engineer acts as a liaison between domain experts and the computer implementation. Propositional logic is then introduced as a basic technique for representing knowledge in KBS. It represents statements as atomic or compound propositions connected by logical operators like negation, conjunction, disjunction, implication, and biconditional.
Lec 3 knowledge acquisition representation and inferenceEyob Sisay
Artificial Intelligence lecture notes. AI summarized notes for knowledge reasoning and knowledge representation, its for you in order for reading and may be for self-learning, I think.
This document discusses concepts, constructs, hypotheses, and variables used in research. It defines key terms like concept, construct, hypothesis, and variables. It explains the different types of variables such as independent and dependent variables. It also discusses the different types of scales used to measure variables, including nominal, ordinal, interval, and ratio scales. Examples are provided for each term and concept to illustrate their meaning. The document provides essential information on fundamental building blocks and steps used in the scientific research process.
The document discusses different techniques for knowledge representation in artificial intelligence. It describes knowledge representation as allowing machines to understand and utilize knowledge to solve complex problems like human-human communication. It then covers various types of knowledge representation including declarative, procedural, meta-knowledge and more. Finally, it discusses common techniques for knowledge representation such as logical representation, semantic networks, frames, production rules and more.
Unit 2 discusses knowledge representation, which is important for intelligent systems to achieve useful tasks. It cannot be done without a large amount of domain-specific knowledge. Humans tackle problems using their knowledge resources, so knowledge must be represented inside computers for AI programs to manipulate. The document then defines knowledge representation as the part of AI concerned with how agents think and how thinking enables intelligent behavior. It represents real-world information so computers can understand and utilize knowledge to solve complex problems.
This document provides an overview of unit 4 on logical agents and planning in artificial intelligence. It discusses inference in propositional and first-order logic, logic programming, and different approaches to planning problems including state-space search, partial order planning, and both forward and backward search methods. Textbook and reference information is also provided.
Foundations of Knowledge Representation in Artificial Intelligence.pptxkitsenthilkumarcse
Knowledge representation in artificial intelligence (AI) is a fundamental concept that involves the process of structuring and encoding knowledge so that AI systems can understand, reason, and make decisions. Effective knowledge representation is essential for AI systems to model and work with complex real-world information. Here are some key aspects of knowledge representation in AI:
Symbolic Knowledge Representation: This approach uses symbols and rules to represent knowledge. It involves encoding information using symbols, predicates, and logical statements. Common formalisms include first-order logic and propositional logic. Symbolic representation is particularly suited for knowledge-based systems and expert systems.
Semantic Networks: In a semantic network, knowledge is represented using nodes and links to denote relationships between concepts. This form of representation is intuitive and is often used for organizing knowledge in a structured manner.
Frames and Ontologies: Frames and ontologies are used to represent knowledge by structuring information into frames or classes. Frames contain attributes and values, and they help in organizing and categorizing knowledge. Ontologies, such as OWL (Web Ontology Language), provide a more formal representation of knowledge for use in the semantic web and knowledge graphs.
Rule-Based Systems: Rule-based systems use a set of rules to represent and reason with knowledge. These rules can be encoded in the form of "if-then" statements, allowing AI systems to make decisions and draw inferences.
Fuzzy Logic: Fuzzy logic allows for the representation of uncertainty and vagueness in knowledge. It is particularly useful in situations where information is not black and white but falls within degrees of truth.
Bayesian Networks: Bayesian networks represent knowledge using probability distributions and conditional dependencies. They are valuable for modeling uncertain or probabilistic relationships in various domains, such as medical diagnosis and risk analysis.
Connectionist Models: Connectionist models, like neural networks, use distributed representations to encode knowledge. In these models, knowledge is spread across interconnected nodes (neurons), and learning occurs through the adjustment of connection weights. These networks are particularly effective in tasks such as pattern recognition and natural language processing.
Hybrid Approaches: Many AI systems use a combination of different knowledge representation techniques to address the complexities of real-world problems. For instance, combining symbolic representation with connectionist models is a common approach in modern AI.
The choice of knowledge representation method depends on the specific problem domain, the nature of the data, and the requirements of the AI system.
Artificial intelligence represents knowledge in a form that computer systems can use to solve complex tasks. There are different types of knowledge, including declarative knowledge which represents facts and concepts, and procedural knowledge which represents how to perform tasks. Knowledge representation approaches in artificial intelligence include simple relational knowledge, inheritable knowledge which uses hierarchies and inheritance, inferential knowledge which represents knowledge through formal logic, and procedural knowledge which represents knowledge through programs and rules.
This document discusses the architecture of knowledge-based systems (KBS). It explains that a KBS contains a knowledge module called the knowledge base (KB) and a control module called the inference engine. The KB explicitly represents knowledge that can be easily updated by domain experts without programming expertise. A knowledge engineer acts as a liaison between domain experts and the computer implementation. Propositional logic is then introduced as a basic technique for representing knowledge in KBS. It represents statements as atomic or compound propositions connected by logical operators like negation, conjunction, disjunction, implication, and biconditional.
Lec 3 knowledge acquisition representation and inferenceEyob Sisay
Artificial Intelligence lecture notes. AI summarized notes for knowledge reasoning and knowledge representation, its for you in order for reading and may be for self-learning, I think.
This document discusses concepts, constructs, hypotheses, and variables used in research. It defines key terms like concept, construct, hypothesis, and variables. It explains the different types of variables such as independent and dependent variables. It also discusses the different types of scales used to measure variables, including nominal, ordinal, interval, and ratio scales. Examples are provided for each term and concept to illustrate their meaning. The document provides essential information on fundamental building blocks and steps used in the scientific research process.
The document discusses knowledge representation in artificial intelligence. It covers several key issues in knowledge representation including important attributes, relationships among attributes, choosing an appropriate level of granularity, representing sets of objects, and finding the right knowledge structure. It also discusses different levels of knowledge-based agents including the knowledge, logical, and implementation levels. Finally, it defines knowledge representation and discusses what types of knowledge should be represented, including objects, events, performance, meta-knowledge, and facts. It identifies two main types of knowledge: declarative knowledge, which represents facts and concepts, and procedural knowledge, which represents how to perform tasks and achieve goals.
The document discusses theories in behavior science research. It defines theory as an explanation for how things work together that can make predictions. A good theory can be tested and either supported or discarded based on the evidence. The major goal of science is to generate and verify theories, as theories predict and explain natural phenomena. The document provides examples of theories like Marx's theory and Freud's theory. It distinguishes theories from hypotheses, noting that a proven hypothesis becomes a theory. It outlines several functions and roles of theories in research, such as guiding research, classifying concepts, summarizing knowledge, and predicting facts. The document also discusses deductive, inductive, and adaptive approaches to relating theory and research. Finally, it outlines several characteristics of
The document discusses discrete mathematics and provides examples of problems that can be solved using discrete mathematics. Some examples include determining the number of ways to choose a valid password, calculating the probability of winning a lottery, and finding the shortest path between two cities using a transportation system. Discrete mathematics involves counting discrete objects and studying relationships between finite sets. It is used whenever objects are counted or processes with a finite number of steps are analyzed. Studying discrete math helps develop mathematical maturity and provides foundations for computer science courses involving algorithms, databases, and more.
Knowledge engineering is the process of building a knowledge base by extracting knowledge from human experts. It involves knowledge acquisition, choosing a knowledge representation formalism, and selecting reasoning and problem-solving strategies. The knowledge engineer determines important concepts and relations in a domain and creates a formal representation. The main tasks of knowledge engineering are knowledge acquisition through interviewing experts and knowledge representation using techniques like logic for knowledge representation and reasoning. An effective knowledge base should be clear, correct, expressive, concise, context-insensitive, and effective.
The document discusses propositional logic as a knowledge representation language. It defines key concepts in propositional logic including: syntax, semantics, validity, satisfiability, interpretation, models, and entailment. It explains that propositional logic uses symbols to represent facts about the world and connectives to combine symbols into sentences. Sentences can then be evaluated based on the truth values assigned to symbols to determine if the overall sentence is true or false. Propositional logic allows new sentences to be deduced from existing sentences through inference rules while maintaining logical validity.
https://www.slideshare.net/amaresimachew/hot-topics-132093738Assosa University
The document provides information on learning, communication, perception, and action in artificial intelligence. It discusses different types of learning including supervised, unsupervised, and reinforcement learning. It also covers natural language processing and how machines can communicate using natural language. It describes the challenges of natural language understanding and outlines some common techniques used in syntactic analysis, including context-free grammars and top-down parsing.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses the syntax and semantics of semantic networks, how they can represent relations between concepts using nodes and links, and techniques for inference including inheritance and intersection search. Frames are also introduced as another knowledge representation technique where information about concepts is organized into objects with attributes and values.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses different types of knowledge representation including relational, inheritable, inferential, and procedural knowledge. It then focuses on semantic networks, describing their basic components like concepts and relations, and how inference can be performed through intersection search and inheritance. Finally, it discusses extensions to semantic networks like partitioned networks and quantified expressions.
Knowledge representation techniques are used to store knowledge in artificial intelligence systems so they can understand the world and solve complex problems. There are several common techniques, including logic, rules, semantic networks, frames, and scripts. Ontological engineering is used to develop large, modular ontologies that represent complex domains and allow knowledge to be integrated and combined. For knowledge representation systems to be effective, they must adequately and efficiently represent, store, manipulate, and acquire new knowledge.
Logical representation is a method of knowledge representation that uses formal logic. It has precise syntax and semantics to support sound inference. Propositional logic is a type of logical representation that uses atomic and compound propositions connected by logical operators like negation, conjunction, disjunction, implication, and biconditional. Propositional logic represents statements with propositional variables and determines their truth value using truth tables.
The document discusses knowledge representation issues in artificial intelligence. It covers several key topics:
- Knowledge and its representation are distinct but related entities that are central to intelligent systems. Knowledge describes the world while representation defines how knowledge is encoded and manipulated.
- There are various ways to represent knowledge, including logical representations, inheritance hierarchies, rules-based systems, and procedural representations. Different types of knowledge require different representation schemes.
- Issues in knowledge representation include ensuring representations are adequately expressive and support effective inference, as well as how to structure knowledge at the appropriate level of granularity and represent sets of objects. Choosing the right representation approach is important for building intelligent systems.
This document provides an overview of hypotheses, theories, and scientific laws. It defines a hypothesis as a tentative explanation for observed facts that can be tested. A theory is a well-established explanation that organizes facts and allows for predictions. The document outlines the key elements of theories, such as assumptions, theoretical terms, and theorems. It also discusses different types of theories and their functions in scientific research and knowledge discovery. Scientific laws are general statements about relationships between natural phenomena that are well-established through repeated empirical evidence.
This document discusses the process of theory construction in social science research. It defines a theory as a set of statements about relationships between concepts or constructs. Theories can take various forms, such as models or hypotheses, and can be used in both basic and applied research. A good theory is useful, has consensus among scientists, and is logically consistent, agrees with known facts, and is testable. While science aims to be objective, the process of developing theories inherently involves some subjectivity.
New Quantitative Methodology for Identification of Drug Abuse Based on Featur...Carrie Wang
This project developed a new quantitative methodology using feature-based context-free grammar to analyze discourse semantics from social media discussions in order to identify potential drug abuse. The methodology was able to parse YouTube comments about recreational cough syrup use and perform anaphora resolution. This computational representation of discourse contributes to understanding human language structure and has applications in public health monitoring and clinical research.
This document provides an overview of Bloom's Taxonomy, which classifies learning objectives into six levels: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. Each level is defined and examples of learning objectives for that level are given. The document also discusses using Bloom's Taxonomy to design classroom lectures and assessments that target different cognitive abilities.
The document provides an introduction to knowledge representation and reasoning in artificial intelligence. It discusses how knowledge representation focuses on studying what knowledge agents need to behave intelligently rather than studying the agents themselves. The key aspects of knowledge representation covered include knowledge, representation, and reasoning. It also describes different methods of knowledge representation like logical representation, semantic networks, frames, and production rules.
The document discusses knowledge representation (KR) and different approaches to KR, including:
1) KR provides a surrogate for reasoning about the world by representing knowledge in a computable format. It determines how an agent thinks about the world.
2) Logics like propositional and predicate/first-order logic use symbols and rules to represent knowledge unambiguously, though they have limitations in expressiveness.
3) Semantic networks, frames, and conceptual graphs are other non-logical KR that focus on expressiveness, simplicity, and formality over logic-based representations. They provide flexible ways to represent objects, attributes, and relationships.
1) The document discusses a session on propositional logic and knowledge representation in artificial intelligence.
2) Key topics covered include propositional logic, knowledge representation using logic, inference rules, resolution proofs and Horn clauses.
3) Examples of knowledge representation using propositional logic and semantic networks are provided.
Qualitative Analysis- Dr Ryan Thomas WilliamsRyan Williams
Non-standardised and complex in nature
Demanding process- not an ‘easy option’
This happens during data collection, therefore preparing is key, recordings, transcripts etc
Understanding characteristics and language
You do this by finding patterns in your data and by producing explanations
Reflection
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
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The document discusses knowledge representation in artificial intelligence. It covers several key issues in knowledge representation including important attributes, relationships among attributes, choosing an appropriate level of granularity, representing sets of objects, and finding the right knowledge structure. It also discusses different levels of knowledge-based agents including the knowledge, logical, and implementation levels. Finally, it defines knowledge representation and discusses what types of knowledge should be represented, including objects, events, performance, meta-knowledge, and facts. It identifies two main types of knowledge: declarative knowledge, which represents facts and concepts, and procedural knowledge, which represents how to perform tasks and achieve goals.
The document discusses theories in behavior science research. It defines theory as an explanation for how things work together that can make predictions. A good theory can be tested and either supported or discarded based on the evidence. The major goal of science is to generate and verify theories, as theories predict and explain natural phenomena. The document provides examples of theories like Marx's theory and Freud's theory. It distinguishes theories from hypotheses, noting that a proven hypothesis becomes a theory. It outlines several functions and roles of theories in research, such as guiding research, classifying concepts, summarizing knowledge, and predicting facts. The document also discusses deductive, inductive, and adaptive approaches to relating theory and research. Finally, it outlines several characteristics of
The document discusses discrete mathematics and provides examples of problems that can be solved using discrete mathematics. Some examples include determining the number of ways to choose a valid password, calculating the probability of winning a lottery, and finding the shortest path between two cities using a transportation system. Discrete mathematics involves counting discrete objects and studying relationships between finite sets. It is used whenever objects are counted or processes with a finite number of steps are analyzed. Studying discrete math helps develop mathematical maturity and provides foundations for computer science courses involving algorithms, databases, and more.
Knowledge engineering is the process of building a knowledge base by extracting knowledge from human experts. It involves knowledge acquisition, choosing a knowledge representation formalism, and selecting reasoning and problem-solving strategies. The knowledge engineer determines important concepts and relations in a domain and creates a formal representation. The main tasks of knowledge engineering are knowledge acquisition through interviewing experts and knowledge representation using techniques like logic for knowledge representation and reasoning. An effective knowledge base should be clear, correct, expressive, concise, context-insensitive, and effective.
The document discusses propositional logic as a knowledge representation language. It defines key concepts in propositional logic including: syntax, semantics, validity, satisfiability, interpretation, models, and entailment. It explains that propositional logic uses symbols to represent facts about the world and connectives to combine symbols into sentences. Sentences can then be evaluated based on the truth values assigned to symbols to determine if the overall sentence is true or false. Propositional logic allows new sentences to be deduced from existing sentences through inference rules while maintaining logical validity.
https://www.slideshare.net/amaresimachew/hot-topics-132093738Assosa University
The document provides information on learning, communication, perception, and action in artificial intelligence. It discusses different types of learning including supervised, unsupervised, and reinforcement learning. It also covers natural language processing and how machines can communicate using natural language. It describes the challenges of natural language understanding and outlines some common techniques used in syntactic analysis, including context-free grammars and top-down parsing.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses the syntax and semantics of semantic networks, how they can represent relations between concepts using nodes and links, and techniques for inference including inheritance and intersection search. Frames are also introduced as another knowledge representation technique where information about concepts is organized into objects with attributes and values.
The document provides an overview of knowledge representation formalisms including semantic networks and frames. It discusses different types of knowledge representation including relational, inheritable, inferential, and procedural knowledge. It then focuses on semantic networks, describing their basic components like concepts and relations, and how inference can be performed through intersection search and inheritance. Finally, it discusses extensions to semantic networks like partitioned networks and quantified expressions.
Knowledge representation techniques are used to store knowledge in artificial intelligence systems so they can understand the world and solve complex problems. There are several common techniques, including logic, rules, semantic networks, frames, and scripts. Ontological engineering is used to develop large, modular ontologies that represent complex domains and allow knowledge to be integrated and combined. For knowledge representation systems to be effective, they must adequately and efficiently represent, store, manipulate, and acquire new knowledge.
Logical representation is a method of knowledge representation that uses formal logic. It has precise syntax and semantics to support sound inference. Propositional logic is a type of logical representation that uses atomic and compound propositions connected by logical operators like negation, conjunction, disjunction, implication, and biconditional. Propositional logic represents statements with propositional variables and determines their truth value using truth tables.
The document discusses knowledge representation issues in artificial intelligence. It covers several key topics:
- Knowledge and its representation are distinct but related entities that are central to intelligent systems. Knowledge describes the world while representation defines how knowledge is encoded and manipulated.
- There are various ways to represent knowledge, including logical representations, inheritance hierarchies, rules-based systems, and procedural representations. Different types of knowledge require different representation schemes.
- Issues in knowledge representation include ensuring representations are adequately expressive and support effective inference, as well as how to structure knowledge at the appropriate level of granularity and represent sets of objects. Choosing the right representation approach is important for building intelligent systems.
This document provides an overview of hypotheses, theories, and scientific laws. It defines a hypothesis as a tentative explanation for observed facts that can be tested. A theory is a well-established explanation that organizes facts and allows for predictions. The document outlines the key elements of theories, such as assumptions, theoretical terms, and theorems. It also discusses different types of theories and their functions in scientific research and knowledge discovery. Scientific laws are general statements about relationships between natural phenomena that are well-established through repeated empirical evidence.
This document discusses the process of theory construction in social science research. It defines a theory as a set of statements about relationships between concepts or constructs. Theories can take various forms, such as models or hypotheses, and can be used in both basic and applied research. A good theory is useful, has consensus among scientists, and is logically consistent, agrees with known facts, and is testable. While science aims to be objective, the process of developing theories inherently involves some subjectivity.
New Quantitative Methodology for Identification of Drug Abuse Based on Featur...Carrie Wang
This project developed a new quantitative methodology using feature-based context-free grammar to analyze discourse semantics from social media discussions in order to identify potential drug abuse. The methodology was able to parse YouTube comments about recreational cough syrup use and perform anaphora resolution. This computational representation of discourse contributes to understanding human language structure and has applications in public health monitoring and clinical research.
This document provides an overview of Bloom's Taxonomy, which classifies learning objectives into six levels: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. Each level is defined and examples of learning objectives for that level are given. The document also discusses using Bloom's Taxonomy to design classroom lectures and assessments that target different cognitive abilities.
The document provides an introduction to knowledge representation and reasoning in artificial intelligence. It discusses how knowledge representation focuses on studying what knowledge agents need to behave intelligently rather than studying the agents themselves. The key aspects of knowledge representation covered include knowledge, representation, and reasoning. It also describes different methods of knowledge representation like logical representation, semantic networks, frames, and production rules.
The document discusses knowledge representation (KR) and different approaches to KR, including:
1) KR provides a surrogate for reasoning about the world by representing knowledge in a computable format. It determines how an agent thinks about the world.
2) Logics like propositional and predicate/first-order logic use symbols and rules to represent knowledge unambiguously, though they have limitations in expressiveness.
3) Semantic networks, frames, and conceptual graphs are other non-logical KR that focus on expressiveness, simplicity, and formality over logic-based representations. They provide flexible ways to represent objects, attributes, and relationships.
1) The document discusses a session on propositional logic and knowledge representation in artificial intelligence.
2) Key topics covered include propositional logic, knowledge representation using logic, inference rules, resolution proofs and Horn clauses.
3) Examples of knowledge representation using propositional logic and semantic networks are provided.
Qualitative Analysis- Dr Ryan Thomas WilliamsRyan Williams
Non-standardised and complex in nature
Demanding process- not an ‘easy option’
This happens during data collection, therefore preparing is key, recordings, transcripts etc
Understanding characteristics and language
You do this by finding patterns in your data and by producing explanations
Reflection
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
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
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!"
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
2. Knowledge representation
• It is the part of Artificial intelligence which concerned with AI
agents thinking
• And how thinking contributes to intelligent behavior of agents.
• It is a way which describes how we can represent knowledge in
artificial intelligence.
• Knowledge representation is not just storing data into some
database, but it also enables an intelligent machine to learn
from that knowledge and experiences so that it can behave
intelligently like a human.
3. • It is responsible for representing information about the real
world
• A computer use this information and can understand and can
utilize this knowledge to solve the complex real world problems
• Example diagnosis a medical condition or communicating with
humans in natural language.
4. What to Represent
• Object: All the facts about objects in our world domain. E.g., Guitars
contains strings, trumpets are brass instruments.
• Events: Events are the actions which occur in our world.
• Performance: It describe behavior which involves knowledge about
how to do things.
• Meta-knowledge: It is knowledge about what we know.
• Facts: Facts are the truths about the real world and what we
represent.
• Knowledge-Base: The central component of the knowledge-based
agents is the knowledge base. It is represented as KB. The
Knowledgebase is a group of the Sentences (Here, sentences are
used as a technical term and not identical with the English
language).
6. 1. Declarative Knowledge
• Declarative knowledge is to know about something.
• It includes concepts, facts, and objects.
• It is also called descriptive knowledge and expressed in
declarative sentences.
• It is simpler than procedural language.
7. 2. Procedural Knowledge
• It is also known as imperative knowledge.
• Procedural knowledge is a type of knowledge which is
responsible for knowing how to do something.
• It can be directly applied to any task.
• It includes rules, strategies, procedures, agendas, etc.
• Procedural knowledge depends on the task on which it can be
applied.
9. 4. Heuristic knowledge
• knowledge is representing knowledge of some experts in a filed
or subject.
• Heuristic knowledge is rules of thumb based on previous
experiences, awareness of approaches, and which are good to
work but not guaranteed.
10. 5. Structural knowledge
• Structural knowledge is basic knowledge to problem-solving.
• It describes relationships between various concepts such as
kind of, part of, and grouping of something.
• It describes the relationship that exists between concepts or
objects.
11. Approaches to knowledge representation
1. Simple relational knowledge:
• It is the simplest way of storing facts which uses the relational
method, and each fact about a set of the object is set out
systematically in columns.
• This approach of knowledge representation is famous in database
systems where the relationship between different entities is
represented.
• This approach has little opportunity for inference.
13. 2. Inheritable knowledge
• In the inheritable knowledge approach, all data must be stored into a
hierarchy of classes.
• All classes should be arranged in a generalized form or a hierarchal
manner.
• In this approach, we apply inheritance property.
• Elements inherit values from other members of a class.
• This approach contains inheritable knowledge which shows a
relation between instance and class, and it is called instance
relation.
• Every individual frame can represent the collection of attributes and
its value.
• In this approach, objects and values are represented in Boxed
nodes.
• We use Arrows which point from objects to their values.
14.
15. 3. Inferential knowledge:
• Inferential knowledge approach represents knowledge in the
form of formal logics.
• This approach can be used to derive more facts.
• It guaranteed correctness.
• Example: Let's suppose there are two statements:
• Marcus is a man
• All men are mortal
Then it can represent as;
man(Marcus)
∀x = man (x) ----------> mortal (x)s
16. 4. Procedural knowledge:
• Procedural knowledge approach uses small programs and
codes which describes how to do specific things, and how to
proceed.
• In this approach, one important rule is used which is If-Then
rule.
• In this knowledge, we can use various coding languages such
as LISP language and Prolog language.
• We can easily represent heuristic or domain-specific knowledge
using this approach.
• But it is not necessary that we can represent all cases in this
approach.
17. Requirements for knowledge Representation
system:
A good knowledge representation system must possess the following
properties.
1. Representational Accuracy:
KR system should have the ability to represent all kind of required
knowledge.
2. Inferential Adequacy:
KR system should have ability to manipulate the representational
structures to produce new knowledge corresponding to existing
structure.
3. Inferential Efficiency:
The ability to direct the inferential knowledge mechanism into the most
productive directions by storing appropriate guides.
4. Acquisitional efficiency- The ability to acquire the new knowledge
easily using automatic methods.
19. There are mainly four ways of knowledge representation which
are given as follows:
1.Logical Representation
2.Semantic Network Representation
3.Frame Representation
4.Production Rules
20. 1. Logical Representation
• Logical representation is a language with some concrete rules
which deals with propositions and has no ambiguity in
representation.
• Logical representation means drawing a conclusion based on
various conditions.
• This representation lays down some important communication
rules.
• It consists of precisely defined syntax and semantics which
supports the sound inference.
• Each sentence can be translated into logics using syntax and
semantics.
21. Syntax:
• Syntaxes are the rules which decide how we can construct legal
sentences in the logic.
• It determines which symbol we can use in knowledge
representation.
• How to write those symbols.
Semantics:
• Semantics are the rules by which we can interpret the sentence
in the logic.
• Semantic also involves assigning a meaning to each sentence.
22. Logical representation can be categorised into mainly two logics:
1.Propositional Logics
2.Predicate logics
23. Propositional logic in Artificial intelligence
• Propositional logic (PL) is the simplest form of logic where all
the statements are made by propositions.
• A proposition is a declarative statement which is either true or
false.
• It is a technique of knowledge representation in logical and
mathematical form.
24. Following are some basic facts about
propositional logic
• Propositional logic is also called Boolean logic as it works on 0 and 1.
• In propositional logic, we use symbolic variables to represent the logic, and we can use
any symbol for a representing a proposition, such A, B, C, P, Q, R, etc.
• Propositions can be either true or false, but it cannot be both.
• Propositional logic consists of an object, relations or function, and logical connectives.
• These connectives are also called logical operators.
• The propositions and connectives are the basic elements of the propositional logic.
• Connectives can be said as a logical operator which connects two sentences.
• A proposition formula which is always true is called tautology, and it is also called a valid
sentence.
• A proposition formula which is always false is called Contradiction.
• A proposition formula which has both true and false values is called
• Statements which are questions, commands, or opinions are not propositions such as
"Where is Rohini", "How are you", "What is your name", are not propositions.
25. Syntax of propositional logic
• The syntax of propositional logic defines the allowable
sentences for the knowledge representation.
• There are two types of Propositions:
1.Atomic Propositions
2.Compound propositions
26. Atomic Proposition
• Atomic propositions are the simple propositions. It consists of a
single proposition symbol.
• These are the sentences which must be either true or false.
Example
1. 2+2 is 4, it is an atomic proposition as it is a true fact.
2."The Sun is cold" is also a proposition as it is a false fact.
27. Compound proposition
• Compound propositions are constructed by combining simpler
or atomic propositions, using parenthesis and logical
connectives.
Example
a) "It is raining today, and street is wet."
b) "Ankit is a doctor, and his clinic is in Mumbai."
28. Logical Connectives
• Logical connectives are used to connect two simpler
propositions or representing a sentence logically.
• We can create compound propositions with the help of logical
connectives.
• There are mainly five connectives, which are given as follows:
1.Negation: A sentence such as ¬ P is called negation of P. A
literal can be either Positive literal or negative literal.
2.Conjunction: A sentence which has ∧ connective such as, P ∧
Q is called a conjunction.
Example: Rohan is intelligent and hardworking. It can be
written as,
P= Rohan is intelligent,
Q= Rohan is hardworking. → P∧ Q.
29. 3. Disjunction: A sentence which has ∨ connective, such as P ∨
Q. is called disjunction, where P and Q are the propositions.
Example: "Ritika is a doctor or Engineer",
Here P= Ritika is Doctor. Q= Ritika is Doctor, so we can write it
as P ∨ Q.
4. Implication: A sentence such as P → Q, is called an
implication. Implications are also known as if-then rules. It can be
represented as
If it is raining, then the street is wet.
Let P= It is raining, and Q= Street is wet, so it is represented
as P → Q
5. Biconditional: A sentence such as P⇔ Q is a Biconditional
sentence, example If I am breathing, then I am alive
P= I am breathing, Q= I am alive, it can be represented as
30.
31. Truth Table
• In propositional logic, we need to know the truth values of
propositions in all possible scenarios.
• We can combine all the possible combination with logical
connectives, and the representation of these combinations in a
tabular format is called Truth table.
• Following are the truth table for all logical connectives:
35. Precedence of connectives
Precedence Operators
First Precedence Parenthesis
Second Precedence Negation
Third Precedence Conjunction(AND)
Fourth Precedence Disjunction(OR)
Fifth Precedence Implication
Six Precedence Biconditional
36. Logical equivalence
• Logical equivalence is one of the features of propositional logic.
Two propositions are said to be logically equivalent if and only if
the columns in the truth table are identical to each other.
• Let's take two propositions A and B, so for logical equivalence,
we can write it as A⇔B. In below truth table we can see that
column for ¬A∨ B and A→B, are identical hence A is Equivalent
to B
37.
38. Properties of Operators
• Commutativity:
• P∧ Q= Q ∧ P, or
• P ∨ Q = Q ∨ P.
• Associativity:
• (P ∧ Q) ∧ R= P ∧ (Q ∧ R),
• (P ∨ Q) ∨ R= P ∨ (Q ∨ R)
• Identity element:
• P ∧ True = P,
• P ∨ True= True.
40. First Order Predicate Logic
FOPL was developed by logicians to extend the expressiveness of PL. The symbols
and rules of combination permitted in FOPL are defined as follows:
1. Connectives: There are five connectives symbols: ∨, ^ , ¬ , ⇒ and ⇔.
2. Quantifiers: Two quantifier symbols are ∀ & ∃ where ∃x means for some x and
∀x means for all x.
3. Variables: These are the terms which may have different values in a given
domain.
4. Constants: These are the fixed value terms that belongs to a given domain.
41. 5. Function: It is used to identify a domain element. It maps n elements
to a single element. Symbols f, g, h and words such as age_of,
income_of reprent a function.
6. Predicates: They are the relation within the domain to show how an
element is related to another. Capital letters or capitalized words are
used to represent a predicate.
42. Properties of a statement
Satisfiable:
A statement is satisfiable if there is some interpretation for which it is true.
Contradiction:
A sentence is contradictory (unsatisfiable) if there is no interpretation for which it is
true.
Valid:
A sentence is valid if it is true for every interpretation.
Equivalence:
Two sentence are equivalent if they have same truth values under every
interpretation.
Logical Consequences:
A sentence is a logical consequence of another if it is satisfied by all interpretation
which satisfies the first.
43. Well-Formed Formula(WFF)
• Well-Formed Formula(WFF) is an expression consisting of
variables(capital letters), parentheses, and connective symbols.
• An expression is basically a combination of operands &
operators and here operands and operators are the connective
symbols.
44. Well-formed formula (WFF)
• Any expression that obeys the syntactic rules of propositional
logic is called a well-formed formula, or WFF.
• Fortunately, the syntax of propositional logic is easy to learn. It
has only three rules:
1.A Statement variable standing alone is a Well-Formed
Formula(WFF).
For example– Statements like P, ∼P, Q, ∼Q are themselves
Well Formed Formulas.
2.If ‘P’ is a WFF then ∼P is a formula as well.
3.If P & Q are WFFs, then (P∨Q), (P∧Q), (P⇒Q), (P⇔Q), etc. are
also WFFs.
45. Some Equivalence Laws
Negative Law: ~(~P) ≈ P
De Morgan’s Laws: ~ (P V Q) ≈ ~P & ~Q
~ (P & Q) ≈ ~P V ~Q
Distributivity: P & ( Q V R) ≈ (P & Q) V (P & R)
P V ( Q & R) ≈ (P V Q) & (P V R)
46. Inference Rule
• Generating the conclusions from evidence and facts is termed
as Inference.
• Inference rules are applied to derive proofs in artificial
intelligence.
• The proof is a sequence of the conclusion that leads to the
desired goal.
• In inference rules, the implication among all the connectives
plays an important role.
47. Following are some terminologies related to inference rules:
• Implication: It is one of the logical connectives which can be
represented as P → Q. It is a Boolean expression.
• Converse: The converse of implication, which means the right-
hand side proposition goes to the left-hand side and vice-versa.
It can be written as Q → P.
• Contrapositive: The negation of converse is termed as
contrapositive, and it can be represented as ¬ Q → ¬ P.
• Inverse: The negation of implication is called inverse. It can be
represented as ¬ P → ¬ Q.
48. Types of Inference rules
1. Modus Ponens:
The Modus Ponens rule is one of the most important rules of
inference, and it states that if P and P → Q is true, then we can
infer that Q will be true. It can be represented as:
49. Example:
Statement-1: "If I am sleepy then I go to bed" ==> P→ Q
Statement-2: "I am sleepy" ==> P
Conclusion: "I go to bed." ==> Q.
Hence, we can say that, if P→ Q is true and P is true then Q will
be true.
50. 2. Modus Tollens:
The Modus Tollens rule state that if P→ Q is true and ¬ Q is true,
then ¬ P will also true. It can be represented as:
51. Example
Statement-1: "If I am sleepy then I go to bed" ==> P→ Q
Statement-2: "I do not go to the bed."==> ~Q
Statement-3: Which infers that "I am not sleepy" => ~P
52. 3. Hypothetical Syllogism:
The Hypothetical Syllogism rule state that if P→R is true
whenever P→Q is true, and Q→R is true. It can be represented
as the following notation:
Example:
Statement-1: If you have my home key then you can unlock my
home. P→Q
Statement-2: If you can unlock my home then you can take my
money. Q→R
Conclusion: If you have my home key then you can take my
money. P→R
53. 4. Disjunctive Syllogism:
The Disjunctive syllogism rule state that if P∨Q is true, and ¬P is
true, then Q will be true. It can be represented as:
54. Example:
Statement-1: Today is Sunday or Monday. ==>P∨Q
Statement-2: Today is not Sunday. ==> ¬P
Conclusion: Today is Monday. ==> Q
55. 5. Addition:
The Addition rule is one the common inference rule, and it states
that If P is true, then P∨Q will be true.
56. Example:
Statement: I have a vanilla ice-cream. ==> P
Statement-2: I have Chocolate ice-cream.
Conclusion: I have vanilla or chocolate ice-cream. ==> (P∨Q)
58. 7. Resolution:
The Resolution rule state that if P∨Q and ¬ P∧R is true, then
Q∨R will also be true. It can be represented as
59. Forward Chaining and backward chaining in
AI
The inference engine is the component of the intelligent system
in artificial intelligence, which applies logical rules to the
knowledge base to infer new information from known facts.
The first inference engine was part of the expert system.
Inference engine commonly proceeds in two modes, which are:
1.Forward chaining
2.Backward chaining
60. Horn Clause and Definite clause:
• Horn clause and definite clause are the forms of sentences,
which enables knowledge base to use a more restricted and
efficient inference algorithm.
• Logical inference algorithms use forward and backward
chaining approaches, which require KB in the form of the first-
order definite clause.
61. • Definite clause: A clause which is a disjunction of literals
with exactly one positive literal is known as a definite clause
or strict horn clause.
• Horn clause: A clause which is a disjunction of literals with at
most one positive literal is known as horn clause. Hence all
the definite clauses are horn clauses.
Example: (¬ p V ¬ q V k). It has only one positive literal k.
It is equivalent to p ∧ q → k.
62. Forward Chaining
• Forward chaining is also known as a forward deduction or
forward reasoning method when using an inference engine.
• Forward chaining is a form of reasoning which start with atomic
sentences in the knowledge base and applies inference rules
(Modus Ponens) in the forward direction to extract more data
until a goal is reached.
• The Forward-chaining algorithm starts from known facts,
triggers all rules whose premises are satisfied, and add their
conclusion to the known facts.
• This process repeats until the problem is solved.
63.
64. Properties of Forward-Chaining
• It is a down-up approach, as it moves from bottom to top.
• It is a process of making a conclusion based on known facts or
data, by starting from the initial state and reaches the goal
state.
• Forward-chaining approach is also called as data-driven as we
reach to the goal using available data.
• Forward -chaining approach is commonly used in the expert
system, such as CLIPS, business, and production rule systems.
65. Example
• Tom is running (A)
• If a person is running, he will sweat (A->B)
• Therefore, Tom is sweating. (B)
66. Example
Facts
1.If D barks and D eats bone, then D is a dog.
2.If V is cold and V is sweet, then V is ice-cream.
3.If D is a dog, then D is black.
4.If V is ice-cream, then it is Vanilla.
Derive forward chaining using the given known facts to
prove Tomy is black.
• Tomy barks.
• Tomy eats bone.
67. Solution: Given Tomy barks.
From (1), it is clear:
If Tomy barks and Tomy eats bone, then Tomy is a dog.
From (3), it is clear:
If Tomy is a dog, then Tomy is black.
Hence, it is proved that Tomy is black.
68. Backward Chaining
• Backward-chaining is also known as a backward deduction or
backward reasoning method when using an inference engine.
• A backward chaining algorithm is a form of reasoning, which
starts with the goal and works backward, chaining through rules
to find known facts that support the goal.
69.
70. Properties of backward chaining
• It is known as a top-down approach.
• Backward-chaining is based on modus ponens inference rule.
• In backward chaining, the goal is broken into sub-goal or sub-goals
to prove the facts true.
• It is called a goal-driven approach, as a list of goals decides which
rules are selected and used.
• Backward -chaining algorithm is used in game theory, automated
theorem proving tools, inference engines, proof assistants, and
various AI applications.
• The backward-chaining method mostly used a depth-first
search strategy for proof.
71. Example
• Tom is sweating (B).
• If a person is running, he will sweat (A->B).
• Tom is running (A).
72. Conjunctive Normal Form (CNF)
• In Boolean logic, a formula is in conjunctive normal form (CNF)
or clausal normal form if it is a conjunction of one or more clauses,
where a clause is a disjunction of literals.
• It is a product of sums or an AND of ORs.
• It is an ∧ of ∨s of (possibly negated, ¬) variables (called literals ).
73. (y ∨ ¬z) ∧ ( ¬y) ∧ (y ∨ z) is a CNF
(x ∨ ¬y ∨ z) is a CNF. So is (x ∧ ¬y ∧ z) .
(x ∨ ¬y ∧ z) is not a CNF
74. • To convert a formula into a CNF
– Open up the implications to get ORs.
Replace: P → Q with ¬P ∨ Q
Replace: P⇔Q with (¬P ∨ Q) ∧(P ∨ ¬Q)
– Get rid of double negations
Replace ¬¬P with P
– Convert F ∨ (G ∧ H) to (F ∨ G) ∧( F ∨ H)
75. Disjunctive Normal Form (DNF)
• This is a reverse approach of CNF. The process is similar to
CNF with the following difference:
• (A1 ? B1) V (A2 ? B2) V…V (An ? Bn).
• In DNF, it is OR of ANDS
76. Resolution
• It is one kind of proof technique that works this way -
(i) select two clauses that contain conflicting terms
(ii) combine those two clauses and
(iii) cancel out the conflicting terms.
77. Resolution Principle
Given two clauses C1 and C2 with no variables in common,
• if there is a literal L1 in C1 which is a complement of literal L2 in C2, both L1 and
L2 are deleted and a disjuncted C is formed from the remaining reduced clauses.
• The new clause C is called resolvent of C1 and C2. Resolution is the process of
generating these resolvents from a set of clauses.
78. For example we have following statements,
(1) If it is a pleasant day you will do strawberry picking
(2) If you are doing strawberry picking you are happy.
Goal-If it is a pleasant day then you are happy
79. Above statements can be written in propositional logic like this -
(1) pleasant → strawberry_picking
(2)strawberry_picking → happy
80. And again these statements can be written in CNF like this -
(1) (strawberry_picking ∨~pleasant) ∧
(2) (happy ∨~strawberry_picking)
81. By resolving these two clauses and cancelling out the conflicting
terms 'strawberry_picking' and '~strawberry_picking', we can
have one new clause,
~pleasant ∨ happy or pleasant → happy
i.e. If it is a pleasant day you are happy.
82. Process to apply the resolution
method
• Convert the given axiom into CNF, i.e., a conjunction of clauses.
Each clause should be dis-junction of literals.
• Apply negation on the goal given.
• Use literals which are required and prove it.
83. English sentences
(1) If it is sunny and warm day you will enjoy.
(2) If it is warm and pleasant day you will do strawberry picking
(3) If it is raining then no strawberry picking.
(4) If it is raining you will get wet.
(5) It is warm day
(6) It is raining
(7) It is sunny
85. CNF
(1) (enjoy ∨~sunny∨~warm) ∧
(2) (strawberry_picking ∨~warm∨~pleasant) ∧
(3) (~strawberry_picking ∨~raining) ∧
(4) (wet ∨~raining) ∧
(5) (warm) ∧
(6) (raining) ∧
(7) (sunny)
[Note: In our examples propositional logic has 7 statements. So, we will write these
statements in CNF as below (1) and (2) and (3) and (4) and (5) and (6) and (7)
Here and is replaced by ∧ to show them in conjunction of clauses (in CNF). Thus, it
will become (1) ∧ (2) ∧ (3) ∧ (4) ∧ (5) ∧ (6) ∧ (7) ]
86. • (Goal 1) You are not doing strawberry picking.
• (Goal 2) You will enjoy.
87. Goal 1 : You are not doing strawberry picking.
Prove : ~strawberry_picking
Assume : strawberry_picking (negate the goal and add it to
given clauses).
88. Goal 2 : You will enjoy.
Prove : enjoy
Assume : ~enjoy (negate the goal and add it to given clauses)
90. Consider the following Knowledge Base:
1.The humidity is high or the sky is cloudy.
2.If the sky is cloudy, then it will rain.
3.If the humidity is high, then it is hot.
4.It is not hot.
• Goal: It will rain.
91. Rule-Based System Architecture
• The most common form of architecture used in expert and other types
of knowledge based systems is the production system or it is called
rule based systems.
• This type of system uses knowledge encoded in the form of production
rules i.e. if-then rules.
• The rule has a conditional part on the left hand side and a conclusion
or action part on the right hand side.
For example if: condition1 and condition2 and condition3
Then: Take action4
92. • The rule based architecture of an expert system consists of:-
1. the domain expert,
2. knowledge engineer,
3. inference engine,
4. working memory,
5. knowledge base,
6. external interfaces,
7. user interface,
8. explanation module,
9. database spreadsheets executable programs s
93.
94. User Interface
• It is the mechanism by which the user and the expert system
communicate with each other i.e. the use interacts with the system
through a user interface.
• It acts as a bridge between user and expert system.
• This module accepts the user queries and submits those to the expert
system.
• The user normally consults the expert system for following reasons.
a) To get answer of his/her queries.
b) To get explanation about the solution for psychological satisfaction
95. • The user interface module is designed in such a way that at user level
it accepts the query in a language understandable by expert system.
• To make the expert system user friendly, the user interface interacts
with the user in natural language.
• The user interface provides as much facilities as possible such as
menus, graphical interfaces etc. to make the dialog user friendly and
more attractive.
96. Explanation Module
• The explanation module explains the reasoning of the system to a user.
• It provides the user with an explanation of the reasoning process when requested.
• The credibility of expert system will be established only when it is able to explain
“how and why” a particular conclusion is drawn.
• This explanation increases the belief of user in the expert system.
97. a) Explanation(How): To respond to a how query, the explanation module traces
the chain of rules fired during a consolation with the user.
b) Explanation (Why)? To respond to a why query, the explanation module must
be able to explain why certain information is needed by the inference engine to
complete a step in the reasoning process.
99. knowledge engineer
• The primary people involved in building an expert system are
the knowledge engineer, the domain expert and the end user.
• Once the knowledge engineer has obtained a general overview of the
problem domain and gone through several problem solving sessions with
the domain expert, he/she is ready to begin actually designing the system,
selecting a way to represent the knowledge, determining the search strategy
(backward or forward) and designing the user interface.
• After making complete designs, the knowledge engineer builds a prototype.
• The prototype should be able to solve problems in a small area of the
domain.
• Once the prototype has been implemented, the knowledge engineer and
domain expert test and refine its knowledge by giving it problems to solve
and correcting its disadvantages.
100. Knowledge Base
• In rule based architecture of an expert system, the knowledge base is
the set of production rules.
101. Inference Engine
• The inference engine accepts user input queries and responses to questions
through the I/O interface.
• It uses the dynamic information together with the static knowledge stored in the
knowledge base.
• The knowledge in the knowledge base is used to derive conclusions about the
current case as presented by the user’s input.
• Inference engine is the module which finds an answer from the knowledge base.
• It applies the knowledge to find the solution of the problem.
102. TYPES OF RULE-BASED SYSTEMS
Like expert systems, rule-based systems can also be
categorized into:
• Forward Chaining: Also known as data-driven reasoning,
forward chaining is a data-driven technique that follows a
deductive approach to reach a conclusion.
• Backward Chaining: Often used in formulating plans,
backward chaining is an alternative to forward chaining. It is a
goal-driven technique that follows an inductive approach
or associative reasoning.
103. Algorithm For Forward Chaining:
Repeat
Collect the rules whose conditions match facts in Working Memory.
If more than one rule matches, use conflict resolution strategy to eliminate all but
one.
Do actions indicated by the rules (add facts to WM or delete facts from WM)
Until the problem is solved or no condition match.
104. Algorithm For Backard Chaining
A chain that is traversed from a hypothesis back to the facts that support the hypothesis is a backward
chain.
To prove goal G:
If G is in the initial facts, it is proven.
Otherwise, find a rule which can be used to conclude G, and try to prove
each of that rule’s conditions.
105. ADVANTAGES OF RULE-BASED SYSTEMS
• Rule-based programming is easy to understand.
• It can be built to represent expert judgment in simple or
complicated subjects.
• The cause-and-effect in Rule-Based Systems is transparent.
• It offers flexibility and an adequate mechanism to model several
basic mental processes into machines.
• Mechanizes the reasoning process.
106. DISADVANTAGES OF RULE-BASED SYSTEMS
Though exceptionally beneficial, rule-based systems have
certain drawbacks associated with them, such as:
• They require deep domain knowledge and manual work.
• Generating rules for a complex system is quite challenging and
time-consuming.
• It has less learning capacity, as it generates results based on
the rules.
107. Conflict resolution
• Suppose we have two rules, Rule 1 and Rule 2, with the
same IF part. Thus both of them can be set to fire when
the condition part is satisfied.
• These rules represent a conflict set.
• The inference engine must determine which rule to fire
from such a set.
• A method for choosing a rule to fired in a given cycle is
called conflict resolution.
108. Conflict resolution strategies
Conflict resolution strategies are used in production
systems in artificial intelligence, such as in rule-based expert
systems, to help in choosing which production rule to fire.
The need for such a strategy arises when the conditions of two or
more rules are satisfied by the currently known facts.
109. Categories of Conflict resolution strategies
Conflict resolution strategies fall into several main categories.
1.Specificity - If all of the conditions of two or more rules are
satisfied, choose the rule according to how specific its
conditions are. The most specific may be identified roughly as
the one having the greatest number of preconditions.
2.Recency - When two or more rules could be chosen, favor the
one that matches the most recently added facts, as these are
most likely to describe the current situation.
110. 3.Not previously used - If a rule's conditions are satisfied, but
previously the same rule has been satisfied by the same facts,
ignore the rule. This helps to prevent the system from entering
infinite loops.
4.Order - Pick the first applicable rule in order of presentation.
5.Arbitrary choice - Pick a rule at random. This has the merit of
being simple to compute
111. Use of backtracking
• A backtracking algorithm is a problem-solving algorithm that
uses a brute force approach for finding the desired output.
• The Brute force approach tries out all the possible solutions and
chooses the desired/best solutions.
• The term backtracking suggests that if the current solution is not
suitable, then backtrack and try other solutions. Thus, recursion
is used in this approach.
• This approach is used to solve problems that have multiple
solutions.
112. • There are three main types of problems in backtracking. They are decision
problems, optimization problems, and enumeration problems.
• To understand if backtracking can be an effective solution, the constraints of
the problem must be clear and well-defined.
• Only then the concepts of dynamic programming can be implemented in the
form of algorithms to solve these problems effectively.
113. • Backtracking is an important tool for solving constraint
satisfaction problems, such as crosswords, verbal arithmetic,
Sudoku, and many other puzzles.
• It is often the most convenient technique for parsing, for the
knapsack problem and other combinatorial optimization
problems.
114. Backtracking Algorithm Applications
1.To find all Hamiltonian Paths present in a graph.
2.To solve the N Queen problem.
3.Maze solving problem.
4.The Knight's tour problem.
115. Semantic Nets
• AI agents have to store and organize information in their memory.
• One of the ways they do this is by using semantic networks.
• Semantic networks are a way of representing relationships between
objects and ideas.
• For example, a network might tell a computer the relationship between
different animals.
116.
117. • A semantic network is a graphic notation for representing knowledge
in patterns of interconnected nodes.
• Semantic networks became popular in artificial intelligence and
natural language processing only because it represents knowledge or
supports reasoning.
• These act as another alternative for predicate logic in a form of
knowledge representation.
• The structural idea is that knowledge can be stored in the form of
graphs, with nodes representing objects in the world, and arcs
representing relationships between those objects.
118. Semantic Networks Are Majorly Used
For
• Representing data
• Revealing structure (relations, proximity, relative importance)
• Supporting conceptual edition
• Supporting navigation
119. • This representation consists of mainly two types of relations:
• a. IS-A relation (Inheritance)
• b. Kind-of-relation
Example: Following are some statements which we need to represent in the
form of nodes and links. Statements:
1. Jerry is a cat.
2. Jerry is a mammal
3. Jerry is owned by Priya.
4. Jerry is brown colored.
5. All Mammals are animal.
121. • In the above diagram, we have represented the different type of
knowledge in the form of nodes and edges.
• Each object is connected with another object by some relation
122. Advantages of Semantic network
1. Semantic networks are a natural representation of knowledge.
2. Semantic networks convey meaning in a transparent manner.
3. These networks are simple and easily understandable.
123. Inheritance in Semantic Net
• Inheritance allows us to specify properties of a superclass and then to define
a subclass, which inherits the properties of the superclass.
• Example: If we say that all mammals give birth to live babies and we also
say that all dogs are mammals and that Tommy is a dog then we can
conclude that Tommy gives birth to live mammals.
• In our example, mammals are the superclass of dogs and Tommy. Dogs are
the subclass of mammals and superclass of Tommy.
• Although inheritance is a useful way to express generalization about a class
of objects, in some cases we need to express exceptions to those
generalizations such as “Male animals do not give birth” or “Female dogs
below the age of 6 months do not give birth”.
• In such cases, we say that the default value has been overridden in the
subclass
124. Frames
• Frame based representation is a development of semantic nets and
allow us to express the idea of inheritance.
• A Frame System consists of a set of frames (or nodes), which are
connected together by relations. Each frame describes either an
instance or a class.
• Each frame has one or more slots, which are assigned slot values.
This is the way in which the frame system is built up.
• Rather than simply having links between frames, each relationship is
expressed by a value being placed in a slot.
125. Frame Name Slot Slot Value
Bob Is a Builder
Owns Tommy
eats Cheese
Tommy Is a Dog
chases Bella
Bella Is a Cat
chases mice
126. • When we say, “Tommy is a dog” we really mean, “Tommy is an
instance of the class dog” or “Tommy is a member of the class dogs”.
• The main advantage of using frame-based systems for expert systems
is that all information about a particular object is stored in one place