The document discusses various knowledge representation schemes. It begins by defining knowledge representation as representing expert knowledge in a computer program. It then describes two main types of representation: analysis representation which is used for initial knowledge gathering; and coding representation which is the working code of an expert system. Several specific schemes are then discussed in detail, including semantic networks, decision tables, decision trees, frames, production rules, and logic. Propositional logic and predicate calculus are also explained as forms of logical representation.
This document discusses various knowledge representation methods used in expert systems, including rules, semantic networks, frames, and constraints. It provides examples and explanations of each method. Procedural and declarative programming techniques are also covered. Forward and backward chaining for rule-based inference engines are explained through examples. Propositional and predicate logic are discussed as mathematical methods for representing knowledge.
In today's era of advanced technology, Artificial Intelligence has been proven as a boon for various fields. Utilization of AI in power system is the need of upcoming future.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This document provides an overview of artificial intelligence techniques and their applications in power systems. It discusses expert systems, artificial neural networks, and fuzzy logic systems as the three major AI techniques used. It describes how each technique works and its advantages/disadvantages. The document also gives examples of how these techniques can be applied in transmission lines, power system protection, and other areas like operations, planning, control, and automation of power systems. The conclusion states that while AI shows promise for improving power system efficiency and reliability, more research is still needed to fully realize its benefits.
Induction and Decision Tree Learning (Part 1)butest
The document discusses machine learning and inductive learning. It provides an overview of types of machine learning including supervised, unsupervised, and reinforcement learning. It also discusses the history of machine learning and how inductive learning works, with the goal being to construct a hypothesis h that approximates the target function f based on examples in the training data. Decision tree learning is introduced as a method for inductive learning.
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 procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
Artificial intelligence in power systems seminar presentationMATHEW JOSEPH
The document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides examples of how each technique can help with tasks like fault detection, improving transmission line performance by adjusting parameters, and automated decision making. Current applications of AI in power systems include operations, planning, control, market strategies, and automation of various functions to improve reliability and reduce costs. Further research is still needed to fully leverage AI across all aspects of modern power systems.
This document discusses various knowledge representation methods used in expert systems, including rules, semantic networks, frames, and constraints. It provides examples and explanations of each method. Procedural and declarative programming techniques are also covered. Forward and backward chaining for rule-based inference engines are explained through examples. Propositional and predicate logic are discussed as mathematical methods for representing knowledge.
In today's era of advanced technology, Artificial Intelligence has been proven as a boon for various fields. Utilization of AI in power system is the need of upcoming future.
Knowledge Representation in Artificial intelligence Yasir Khan
This document discusses different methods of knowledge representation in artificial intelligence, including logical representations, semantic networks, production rules, and frames. Logical representations use formal logics like propositional logic and first-order predicate logic to represent facts and relationships. Semantic networks represent knowledge graphically as nodes and edges to model concepts and their relationships. Production rules represent knowledge as condition-action pairs to model problem-solving. Frames represent stereotyped situations as templates with slots to model attributes and behaviors. Choosing the right knowledge representation method is important for building successful AI systems.
This document provides an overview of artificial intelligence techniques and their applications in power systems. It discusses expert systems, artificial neural networks, and fuzzy logic systems as the three major AI techniques used. It describes how each technique works and its advantages/disadvantages. The document also gives examples of how these techniques can be applied in transmission lines, power system protection, and other areas like operations, planning, control, and automation of power systems. The conclusion states that while AI shows promise for improving power system efficiency and reliability, more research is still needed to fully realize its benefits.
Induction and Decision Tree Learning (Part 1)butest
The document discusses machine learning and inductive learning. It provides an overview of types of machine learning including supervised, unsupervised, and reinforcement learning. It also discusses the history of machine learning and how inductive learning works, with the goal being to construct a hypothesis h that approximates the target function f based on examples in the training data. Decision tree learning is introduced as a method for inductive learning.
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 procedural versus declarative knowledge representation and how logic programming languages like Prolog allow knowledge to be represented declaratively through logical rules. It also covers topics like forward and backward reasoning, matching rules to facts in working memory, and using control knowledge to guide the problem solving process. Logic programming represents knowledge through Horn clauses and uses backward chaining inference to attempt to prove goals.
Artificial intelligence in power systems seminar presentationMATHEW JOSEPH
The document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides examples of how each technique can help with tasks like fault detection, improving transmission line performance by adjusting parameters, and automated decision making. Current applications of AI in power systems include operations, planning, control, market strategies, and automation of various functions to improve reliability and reduce costs. Further research is still needed to fully leverage AI across all aspects of modern power systems.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Artificial intelligence in power systems Riyas K H
1) Artificial intelligence techniques like artificial neural networks, fuzzy logic systems, and expert systems can be applied to problems in power systems.
2) Artificial neural networks are useful for tasks like power system stabilization, load forecasting, fault diagnosis, and security assessment. They do not require extensive programming.
3) Fuzzy logic systems account for measurement errors and are used for stability assessment, fault diagnosis, and other applications. Expert systems use rules and facts to deduce conclusions.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This document provides an overview of knowledge representation and networked schemes in artificial intelligence. It discusses several topics:
- Knowledge representation is how knowledge is encoded in a computer-understandable form in an AI system's knowledge base.
- Networked schemes like semantic nets and conceptual graphs represent knowledge using graphs with nodes for concepts and relationships.
- Semantic nets use nodes for concepts/objects and labeled arcs for relationships between nodes. Conceptual graphs also use concept and relationship nodes but have additional rules for node connections.
- Both schemes allow inheritance of features through restriction and joining operations on the graphs. They can represent logical operations and support reasoning.
ARTIFICIAL INTELLIGENCE IN POWER SYSTEMSvivatechijri
In today’s world we require a continuous & definitive supply of electricity for proper functioning in
modern and advanced society. AI (AI) may be a field that was found on the idea of human intelligence where AI
precisely simulates natural intelligence. AI (Artificial Intelligence) is the mixture of expert task, mundane task
and formal task. Power Systems were used from the late 19th century and that they are one among the essential
needs that we'd like in our modern, developing day to day life. Power systems are used for transmission and
delivering the electricity to all or any machines. AI (Artificial Intelligence) plays a serious role in power systems
where they solve different problems in power systems like scheduling, calculating, statistics, forecast. As AI
(Artificial Intelligence) was being developed in several fields we could see the impact that it made on the facility
systems also, the humanly solved mathematical functions were solved by machines and every one the tasks are
performed by the machines.AI techniques became popular for solving different problems in power systems like
control, planning, scheduling, forecast, etc. These techniques can affect difficult tasks faced by applications in
modern large power systems with even more interconnections installed to satisfy increasing load demand. The
appliance of those techniques has been successful in many areas of power grid engineering
Advanced data structures & algorithms important questionsselvaraniArunkumar
This document contains questions from various units in the course CP7102 Advanced Data Structures and Algorithms. It includes questions related to iterative and recursive algorithms, optimization algorithms, dynamic programming algorithms, shared objects and concurrent objects, and concurrent data structures. Some of the key topics covered include sorting algorithms, tree traversal, dynamic programming, mutual exclusion, producer-consumer problems, and lock-free data structures.
The document discusses artificial intelligence and expert systems. It provides an overview of AI, its major branches including expert systems, and how expert systems work. Expert systems use a knowledge base and inference engine to mimic the decision-making of human experts in specific domains. They have benefits like preserving human expertise but also limitations like narrow applicability. The document outlines the components and applications of expert systems.
First-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science to represent knowledge about domains involving objects and relations. FOL extends propositional logic with quantifiers and predicates to describe properties of and relations between objects. Well-formed formulas in FOL involve constants, variables, functions, predicates, quantifiers, and logical connectives. The meaning and truth of FOL statements is determined with respect to a structure called a model that specifies a domain of objects and interpretations of symbols. FOL can be used to represent knowledge about many different domains and perform logical inference.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
The document discusses the use of artificial intelligence techniques in power systems. It describes how AI can help address challenges from the complex, large amounts of data in power systems. The major AI techniques that can be applied include expert systems, artificial neural networks, and fuzzy logic. These techniques have advantages like consistent processing speed but also disadvantages like inability to learn new problems. The document provides examples of applications for fault diagnosis, load forecasting, stability analysis and more. It concludes that AI can improve reliability and reduce costs but more research is still needed to realize its full benefits.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
MYCIN was an early expert system developed in the 1970s and 1980s to diagnose bacterial infections and recommend antibiotics. It used a production rule-based approach with a static knowledge base of rules and a dynamic knowledge base to represent patient-specific information. MYCIN could explain its reasoning and allow knowledge engineers to update its rules through an interactive dialogue interface. The system demonstrated competence comparable to human experts in bacterial infection diagnosis and treatment selection.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
The document discusses classical or crisp set theory. Some key points:
1) Classical set theory deals with sets that have definite membership - an element either fully belongs to a set or not. This is represented by true/false or yes/no.
2) A set is a well-defined collection of objects. The universal set is the overall context within which sets are defined.
3) Set operations like union, intersection, complement and difference are used to combine or relate sets according to specific rules.
4) Properties like commutativity, associativity and distributivity define the logical behavior of sets under different operations.
This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
This document discusses theories of knowledge representation in the mind. It describes how knowledge can be represented through mental images, words, or abstract propositions. The dual-coding theory proposes that knowledge uses both visual/pictorial and linguistic/verbal representations. Propositional theory suggests knowledge is represented through abstract propositions rather than images or words. The document also discusses mental imagery and ambiguous figures, which can challenge propositional representations and be open to multiple interpretations through reference frame manipulation.
The document discusses different types of knowledge that may need to be represented in AI systems, including objects, events, performance, and meta-knowledge. It also discusses representing knowledge at two levels: the knowledge level containing facts, and the symbol level containing representations of objects defined in terms of symbols. Common ways of representing knowledge mentioned include using English, logic, relations, semantic networks, frames, and rules. The document also discusses using knowledge for applications like learning, reasoning, and different approaches to machine learning such as skill refinement, knowledge acquisition, taking advice, problem solving, induction, discovery, and analogy.
Artificial intelligence in power systems Riyas K H
1) Artificial intelligence techniques like artificial neural networks, fuzzy logic systems, and expert systems can be applied to problems in power systems.
2) Artificial neural networks are useful for tasks like power system stabilization, load forecasting, fault diagnosis, and security assessment. They do not require extensive programming.
3) Fuzzy logic systems account for measurement errors and are used for stability assessment, fault diagnosis, and other applications. Expert systems use rules and facts to deduce conclusions.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This document provides an overview of knowledge representation and networked schemes in artificial intelligence. It discusses several topics:
- Knowledge representation is how knowledge is encoded in a computer-understandable form in an AI system's knowledge base.
- Networked schemes like semantic nets and conceptual graphs represent knowledge using graphs with nodes for concepts and relationships.
- Semantic nets use nodes for concepts/objects and labeled arcs for relationships between nodes. Conceptual graphs also use concept and relationship nodes but have additional rules for node connections.
- Both schemes allow inheritance of features through restriction and joining operations on the graphs. They can represent logical operations and support reasoning.
ARTIFICIAL INTELLIGENCE IN POWER SYSTEMSvivatechijri
In today’s world we require a continuous & definitive supply of electricity for proper functioning in
modern and advanced society. AI (AI) may be a field that was found on the idea of human intelligence where AI
precisely simulates natural intelligence. AI (Artificial Intelligence) is the mixture of expert task, mundane task
and formal task. Power Systems were used from the late 19th century and that they are one among the essential
needs that we'd like in our modern, developing day to day life. Power systems are used for transmission and
delivering the electricity to all or any machines. AI (Artificial Intelligence) plays a serious role in power systems
where they solve different problems in power systems like scheduling, calculating, statistics, forecast. As AI
(Artificial Intelligence) was being developed in several fields we could see the impact that it made on the facility
systems also, the humanly solved mathematical functions were solved by machines and every one the tasks are
performed by the machines.AI techniques became popular for solving different problems in power systems like
control, planning, scheduling, forecast, etc. These techniques can affect difficult tasks faced by applications in
modern large power systems with even more interconnections installed to satisfy increasing load demand. The
appliance of those techniques has been successful in many areas of power grid engineering
Advanced data structures & algorithms important questionsselvaraniArunkumar
This document contains questions from various units in the course CP7102 Advanced Data Structures and Algorithms. It includes questions related to iterative and recursive algorithms, optimization algorithms, dynamic programming algorithms, shared objects and concurrent objects, and concurrent data structures. Some of the key topics covered include sorting algorithms, tree traversal, dynamic programming, mutual exclusion, producer-consumer problems, and lock-free data structures.
The document discusses artificial intelligence and expert systems. It provides an overview of AI, its major branches including expert systems, and how expert systems work. Expert systems use a knowledge base and inference engine to mimic the decision-making of human experts in specific domains. They have benefits like preserving human expertise but also limitations like narrow applicability. The document outlines the components and applications of expert systems.
First-order logic (FOL) is a formal system used in mathematics, philosophy, linguistics, and computer science to represent knowledge about domains involving objects and relations. FOL extends propositional logic with quantifiers and predicates to describe properties of and relations between objects. Well-formed formulas in FOL involve constants, variables, functions, predicates, quantifiers, and logical connectives. The meaning and truth of FOL statements is determined with respect to a structure called a model that specifies a domain of objects and interpretations of symbols. FOL can be used to represent knowledge about many different domains and perform logical inference.
Uncertain Knowledge and Reasoning in Artificial IntelligenceExperfy
Learn how to take informed decisions based on probabilities and expert knowledge
Understand and explore one of the most exciting advances in AI in the last decades.
Many hands-on examples, including Python code.
Check it out: https://www.experfy.com/training/courses/uncertain-knowledge-and-reasoning-in-artificial-intelligence
The document discusses the use of artificial intelligence techniques in power systems. It describes how AI can help address challenges from the complex, large amounts of data in power systems. The major AI techniques that can be applied include expert systems, artificial neural networks, and fuzzy logic. These techniques have advantages like consistent processing speed but also disadvantages like inability to learn new problems. The document provides examples of applications for fault diagnosis, load forecasting, stability analysis and more. It concludes that AI can improve reliability and reduce costs but more research is still needed to realize its full benefits.
This document discusses hybrid intelligent systems that combine different technologies such as neural networks, fuzzy systems, and expert systems. It provides examples of neural expert systems, which combine neural networks and rule-based expert systems, and neuro-fuzzy systems, which integrate neural networks with fuzzy logic systems. The key benefits of these hybrid systems include gaining the learning and parallel processing abilities of neural networks as well as the transparency and human-like knowledge representation of fuzzy and expert systems.
MYCIN was an early expert system developed in the 1970s and 1980s to diagnose bacterial infections and recommend antibiotics. It used a production rule-based approach with a static knowledge base of rules and a dynamic knowledge base to represent patient-specific information. MYCIN could explain its reasoning and allow knowledge engineers to update its rules through an interactive dialogue interface. The system demonstrated competence comparable to human experts in bacterial infection diagnosis and treatment selection.
This document provides an overview and introduction to the course "Knowledge Representation & Reasoning" taught by Ms. Jawairya Bukhari. It discusses the aims of developing skills in knowledge representation and reasoning using different representation methods. It outlines prerequisites like artificial intelligence, logic, and programming. Key topics covered include symbolic and non-symbolic knowledge representation methods, types of knowledge, languages for knowledge representation like propositional logic, and what knowledge representation encompasses.
The document discusses classical or crisp set theory. Some key points:
1) Classical set theory deals with sets that have definite membership - an element either fully belongs to a set or not. This is represented by true/false or yes/no.
2) A set is a well-defined collection of objects. The universal set is the overall context within which sets are defined.
3) Set operations like union, intersection, complement and difference are used to combine or relate sets according to specific rules.
4) Properties like commutativity, associativity and distributivity define the logical behavior of sets under different operations.
This document provides an introduction to knowledge representation in artificial intelligence. It discusses how knowledge representation and reasoning forms the basis of intelligent behavior through computational means. The key types of knowledge that need to be represented are defined, including objects, events, facts, and meta-knowledge. Different types of knowledge such as declarative, procedural, structural and heuristic knowledge are explained. The importance of knowledge representation for modeling intelligent behavior in agents is highlighted. The requirements for effective knowledge representation including representational adequacy, inferential adequacy, inferential efficiency, and acquisitional efficiency are outlined. Propositional logic is introduced as the simplest form of logic using propositions.
The document provides an overview of machine learning. It defines machine learning as algorithms that can learn from data to optimize performance and make predictions. It discusses different types of machine learning including supervised learning (classification and regression), unsupervised learning (clustering), and reinforcement learning. Applications mentioned include speech recognition, autonomous robot control, data mining, playing games, fault detection, and clinical diagnosis. Statistical learning and probabilistic models are also introduced. Examples of machine learning problems and techniques like decision trees and naive Bayes classifiers are provided.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
This document discusses theories of knowledge representation in the mind. It describes how knowledge can be represented through mental images, words, or abstract propositions. The dual-coding theory proposes that knowledge uses both visual/pictorial and linguistic/verbal representations. Propositional theory suggests knowledge is represented through abstract propositions rather than images or words. The document also discusses mental imagery and ambiguous figures, which can challenge propositional representations and be open to multiple interpretations through reference frame manipulation.
The document discusses artificial intelligence (AI) and provides an overview of its history, branches, applications, and key concepts. It begins with an introduction to AI and its history, noting that Alan Turing was an early pioneer. It then outlines the main branches of AI like planning, understanding, natural language processing, knowledge representation, and neural networks. Finally, it discusses applications of AI in fields such as finance, healthcare, customer service, and more before concluding with a discussion of the ongoing progress and potential of AI.
Structured objects like frames, scripts, and conceptual graphs represent knowledge using nodes and relationships. Frames represent concepts as objects with slots and allow inheritance of properties from higher frames. Scripts describe stereotypical sequences of events using contexts, participants, and sub-events. The large-scale Cyc project aims to represent common-sense knowledge at a level that allows understanding ordinary text. These knowledge representation techniques provide flexible structures to simulate human reasoning.
This document discusses different types of knowledge and methods for knowledge acquisition. It describes declarative and procedural knowledge, as well as the knowledge acquisition paradox where experts have difficulty verbalizing their knowledge. Various knowledge acquisition methods are outlined, including observation, problem discussion, and protocol analysis. Knowledge representation techniques like rules, semantic networks, frames, and predicate logic are also introduced.
History of Knowledge Representation (SIKS Course 2010)Rinke Hoekstra
The goal of AI research is the simulation and approximation of human intelligence by computers. To a large extent this comes down to the development of computational reasoning services that allow machines to solve problems. Robots are the stereotypical example: imagine what a robot needs to know before it is able to interact with the world the way we do? It needs to have a highly accurate internal representation of reality. It needs to turn perception into action, know how to reach its goals, what objects it can use to its advantage, what kinds of objects exist, etc.
The field of knowledge representation (KR) tries to deal with the problems surrounding the incorporation of some body of knowledge (in whatever form) in a computer system, for the purpose of automated, intelligent reasoning. In this sense, knowledge representation is the basic research topic in AI. Any artificial intelligence is dependent on knowledge, and thus on a representation of that knowledge. The history of knowledge representation has been nothing less than turbulent. The roller coaster of promise of the 50's and 60's, the heated debates of the 70's, the decline and realism of the 80's and the ontology and knowledge management hype of the 90's each left a clear mark on contemporary knowledge representation technology and its application.
Artificial intelligence is the study of how to make computers intelligent. It aims to make computers able to perform complex tasks, recognize patterns, solve problems, and learn from experience. AI uses heuristics rather than algorithms to mimic human thought processes. The Turing thesis states that any thought process used by humans can be programmed into a computer. AI has many applications including business, engineering, medicine, science, security, and helps break past experiences to increase productivity. It has been successfully used in fields like medical diagnosis, stock trading, and more.
The document discusses human intelligence and artificial intelligence (AI). It defines human intelligence as comprising abilities such as learning, understanding language, perceiving, reasoning, and feeling. AI is defined as the science and engineering of making machines intelligent, especially computer programs. It involves developing systems that exhibit traits associated with human intelligence such as reasoning, learning, interacting with the environment, and problem solving. The document outlines the history of AI and discusses approaches to developing systems that think like humans or rationally. It also covers applications of AI such as natural language processing, expert systems, robotics, and more.
SlideShare is a global platform for sharing presentations, infographics, videos and documents. It has over 18 million pieces of professional content uploaded by experts like Eric Schmidt and Guy Kawasaki. The document provides tips for setting up an account on SlideShare, uploading content, optimizing it for searchability, and sharing it on social media to build an audience and reputation as a subject matter expert.
This document discusses the concept of information from various perspectives. It begins by exploring early definitions of information from scientists like Claude Shannon that viewed it as a way to quantify uncertainty and lay the foundations for digital technologies. Later passages discuss information more broadly as facts that carry meaning and can be communicated, noting that almost anything could be considered information. The document also examines characteristics of information, different types of information formats, and how the proliferation of information in modern times has led to issues like information overload. It concludes by suggesting the only solution is diligent work in selecting genuine information among the abundance available.
This document provides an overview of key concepts in artificial intelligence. It discusses important application areas of AI like game playing, automated reasoning, expert systems, natural language processing, planning and robotics. It also outlines important features of AI, including using computers for reasoning and inference, focusing on problems without algorithmic solutions, dealing with incomplete information, reasoning about qualitative features, and using domain knowledge in expert systems. The document serves as an introduction to the major topics and approaches in the field of artificial intelligence.
This document discusses key concepts related to knowledge management, including ontology, epistemology, explicit vs tacit knowledge, and knowing-that vs knowing-how.
It explains that ontology is the study of what exists, while epistemology is the study of how knowledge is acquired and what can be known. There are two main epistemological perspectives - logical positivism which sees knowledge as objectively reflecting reality, and constructivism which sees knowledge as personally constructed.
The document also distinguishes between explicit knowledge which can be readily articulated and shared, tacit knowledge which is harder to articulate but provides context, knowing-that which is factual knowledge and knowing-how which is practical skill or procedural knowledge.
The document provides an overview of problem spaces and problem solving through searching techniques used in artificial intelligence. It defines a problem space as a set of states and connections between states to represent a problem. Search strategies for finding solutions include breadth-first search, depth-first search, and heuristic search. Real-world problems discussed that can be solved through searching include route finding, layout problems, task scheduling, and the water jug problem is presented as a toy problem example.
The document discusses the evolution of artificial intelligence and the development of knowledge-based systems, which apply domain-specific knowledge rather than general problem-solving techniques. It provides an overview of the components of a KBS, examples of widely used systems, and the advantages and limitations of the approach.
The document discusses various aspects of problem solving and production systems including:
- Problem characteristics like decomposability and recoverability impact the appropriate problem solving approach.
- Production systems consist of rules, databases, and a control strategy to apply rules.
- Well-designed heuristics can efficiently guide search toward solutions without exploring all possibilities.
- Different problem types like classification and design are suited to different control strategies like proposing and refining solutions.
This chapter discusses different approaches for incorporating prior knowledge into machine learning algorithms. It describes decision trees/lists that represent knowledge as facts and learning first-order logic sentences that represent objects and relations. It also discusses constructing hypothesis spaces, inductive learning approaches, and search strategies like least-commitment search. Explanation-based learning and inductive logic programming are presented as ways to leverage background knowledge to more efficiently learn from examples. Inverse resolution is discussed as a way to perform inductive logic programming by running proofs backward.
This document discusses various methods of knowledge representation in artificial intelligence, including semantic networks, conceptual graphs, frames, and scripts. It provides examples of each method through figures and descriptions. Different knowledge representation schemes are compared, along with their suitability for representing different types of knowledge.
The document discusses different types of learning strategies and methods. It defines learning as the acquisition of knowledge through study. It describes several learning methods including rote learning, direct instruction, analogy, induction, and deduction. Rote learning involves simple memorization while direct instruction involves being told information. Induction involves forming general concepts from examples and deduction uses existing knowledge to derive new facts. The document provides examples to illustrate each learning method.
Forward chaining is a data-driven reasoning method that applies rules to existing facts to deduce new facts, adding them to the knowledge base. It starts with known facts and uses inference rules to reach a goal or conclusion. Backward chaining is a goal-driven method that starts with a desired goal and works backwards to see if existing facts and rules can support reaching that goal. Both methods have tradeoffs in efficiency depending on whether the starting point is facts or a specific goal.
The document discusses various concepts in predicate logic including:
1. Universal and existential quantification allow representing statements like "for all" or "there exists".
2. Syntax of first-order logic includes constants, variables, functions, predicates, and quantifiers.
3. A predicate is satisfiable if true for some values, valid if true for all values, and unsatisfiable if false for all values.
4. Negating quantifiers flips the quantifier and negates the predicate. Free variables can be substituted while bound variables cannot. Restrictions filter domains.
SL5 OBJECT: SIMPLER LEVEL 5 OBJECT EXPERT SYSTEM LANGUAGE ijscmcjournal
This paper introduces SL5 Object, the Simpler Level 5 Object Expert System Language. SL5 Object is a
rule-based language for specifying expert systems.This paper first introduces the concept of expert systems
and production systems, as well as the typical architecture of such a system. Then it presents a thorough
outline of the SL5 Object Language: the syntax of rules and Objects, allowed constructs, the module
structure. It also presents the execution cycle of the SL5 Object engine, as well as a number of methods to
influence the default progress of this cycle.Finally, this paper introduces an example of Cars diagnoses
problems to illustrate the capabilities of SL5 Object and the concepts presented.
This document provides an introduction to relational database design and normalization. The goal of normalization is to avoid data redundancy and anomalies. Examples of anomalies include insertion anomalies where new data cannot be added without existing data, and deletion anomalies where deleting data also deletes other related data. The document discusses functional dependencies and normal forms to help guide the decomposition of relations into multiple normalized relations while preserving data integrity and dependencies.
Machine learning involves building agents that can learn from experience rather than just using pre-programmed knowledge. There are two types of machine learning: data mining which extracts useful patterns from data, and knowledge refinement which acquires new information and updates an existing knowledge base. Common data mining methods include learning by analogy, rule induction from data patterns, and neural network training; knowledge refinement methods include analyzing differences in observations, explaining experiences, managing multiple models, and correcting mistakes.
WEKA:Data Mining Input Concepts Instances And Attributesweka Content
This document discusses concepts related to data mining input, including concepts, instances, and attributes. It also covers different types of learning in data mining like classification, numeric prediction, clustering, and association rules. Key steps for data preparation are discussed, such as data assembly and aggregation, data integration, data cleaning, and general preparation. Formats for data like the Attribute-Relation File Format (ARFF) are also introduced.
WEKA: Data Mining Input Concepts Instances And AttributesDataminingTools Inc
This document discusses concepts related to data mining input, including concepts, instances, and attributes. It also covers different types of learning in data mining like classification, numeric prediction, clustering, and association rules. Key steps to prepare data for mining are discussed, such as data assembly, integration, cleaning, and preparation. Formats for data like ARFF files and handling sparse data are also covered.
The document discusses algorithms and data structures. It defines an algorithm as a step-by-step procedure for solving a problem using a computer in a finite number of steps. It categorizes common types of algorithms as search, sort, insert, update, and delete algorithms. The document also defines a data structure as a way to store and organize data for efficient use. It distinguishes between linear and non-linear as well as static and dynamic data structures. Finally, it discusses algorithm design strategies like divide and conquer, merge sort, and dynamic programming.
This document provides information about getting fully solved C# programming assignments. It includes instructions to email the semester and specialization to a provided email address or call a provided phone number to receive assignments. It then provides a sample assignment for C# Programming with 5 questions covering topics like .NET framework components, C# program structure, writing programs to reverse a string and concatenate lists of strings, pass by value/reference and output parameters, differences between structures and classes, and inheritance with an example. Students are instructed to answer all questions, with 10 mark questions being around 400 words each.
Accurately and Reliably Extracting Data from the Web: butest
STALKER is a machine learning algorithm that learns to extract data from web pages using a small number of labeled examples provided by the user. It generates extraction rules in a hierarchical manner, exploiting the structure of the web source. The algorithm is efficient because most web pages have a fixed template with few variations. It also uses an active learning approach called co-testing to select the most informative examples for the user to label. The system verifies extracted data by comparing it to learned statistical patterns, and can automatically repair wrappers when sites change.
Here are the steps to construct the decision tree using the Gini index approach:
1. Calculate the Gini index for the total dataset:
Gini(Total) = 1 - (19/40)2 - (21/40)2 = 0.5
2. Calculate the Gini index for age <= 50 split:
Gini(S1) = 1 - (8/19)2 - (11/19)2 = 0.3789
Gini(S2) = 1 - (11/21)2 - (10/21)2 = 0.4762
3. Calculate the Gini index for the split:
Gini(Split) = (19
Building data fusion surrogate models for spacecraft aerodynamic problems wit...Shinwoo Jang
This document discusses building surrogate models to approximate aerodynamic data from spacecraft. The data comes from both high-fidelity wind tunnel tests and lower-fidelity computational fluid dynamics simulations. It presents three approaches to combine this multi-fidelity data based on tensor product approximations: 1) a "merged solution" that fits models based on data type and weights points, 2) a "fused solution" that estimates high-fidelity values using a bias model between low and high-fidelity data, and 3) a "sequential solution" that first fits a low-fidelity model and then corrects it using high-fidelity data residuals. The goal is to generate accurate and consistent surrogate models over an entire flight envelope from
The document is a syllabus for an algorithms course that covers topics such as abstract data types, analysis of algorithms, data structures, sorting algorithms, trees, external storage algorithms, and NP-complete problems. The syllabus is divided into 9 units that cover these topics in depth over the course. Key concepts that will be taught include algorithm efficiency analysis, data structures like lists, stacks, queues and trees, and algorithm design techniques like divide-and-conquer, dynamic programming, and greedy algorithms. Sorting algorithms like merge sort, quicksort, and heap sort will also be analyzed along with graph algorithms and memory management strategies.
CRC stands for Class, Responsibilities, and Collaborators. The goal of CRC is to provide the simplest conceptual introduction to object-oriented modeling. The heart of CRC is the CRC card, which is used to document classes and their responsibilities and collaborators. CRC cards do not use UML directly, but the information on the cards is later translated into a UML class diagram. The CRC process involves domain experts and facilitators walking through scenarios to identify class responsibilities, which are documented on the cards. The cards are then arranged to show class collaborations, and the resulting model is reviewed.
Object-Oriented Concepts
Attribute: the basic data of the class.
Method (operation): an executable procedure that is encapsulated in a class and is designed to
operate on one or more data attributes that are defined as part of the class.
Object: when specific values are assigned to all the resources defined in a class, the result is an
instance of that class. Any instance of any class is called an object.
OPTIMAL CHOICE: NEW MACHINE LEARNING PROBLEM AND ITS SOLUTIONijcsity
We introduce the task of learning to pick a single preferred example out a finite set of examples, an
“optimal choice problem”, as a supervised machine learning problem with complex input. Problems of
optimal choice emerge often in various practical applications. We formalize the problem, show that it does
not satisfy the assumptions of statistical learning theory, yet it can be solved efficiently in some cases. We propose two approaches to solve the problem. Both of them reach good solutions on real life data from a signal processing application.
What is the Covering (Rule-based) algorithm?
Classification Rules- Straightforward
1. If-Then rule
2. Generating rules from Decision Tree
Rule-based Algorithm
1. The 1R Algorithm / Learn One Rule
2. The PRISM Algorithm
3. Other Algorithm
Application of Covering algorithm
Discussion on e/m-learning application
This document summarizes a machine learning project to predict insurance claim severities for the Kaggle "Allstate Claims Severity" competition. It describes the dataset, preprocessing steps including one-hot encoding and outlier removal. A deep neural network model was trained using H2O. Hyperparameters were optimized in 4 phases: activation function, network architecture, outliers/epochs, and learning rate. 21 model submissions achieved test MAEs from 1,169 to 1,292, outperforming random forest benchmarks. Rectifier activation, 3 hidden layers of 1000 neurons total, removing outliers, 100 epochs, and a learning rate of 0.0001 produced strong results.
The document discusses different levels of abstraction in object-oriented programming and design. It begins by defining abstraction and explaining why it is important for managing complexity. It then describes 5 levels of abstraction in OO programs from the highest level of interacting objects to the lowest level of individual methods. The document also covers different forms of abstraction like specialization and division into parts, and provides a brief history of abstraction mechanisms in computer science from assembly languages to modern object-oriented programming.
1. A class defines common attributes and behaviors of objects through methods and variables. Constructors initialize new instances of a class.
2. The document discusses object-oriented programming concepts like classes, objects, methods, encapsulation, and polymorphism and provides examples in Java.
3. It also covers best practices for naming classes, methods and variables in object-oriented languages.
The document discusses several different machine learning approaches to plain text information extraction, including SRV, RAPIER, WHISK, AutoSlog, and CRYSTAL. These systems use both top-down and bottom-up approaches to induce rules or patterns for extracting structured information from unstructured text. The document compares the different systems and their rule representations, learning algorithms, experiments and performance on various information extraction tasks.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
3. - Raw facts
- Static
- E.g. Liza, S’pore
- Processed data
- Has some meaning and purposes
- E.g. Liza was born in S’pore
- Derived from information
- Stored in human brain
- What we know
5. “The know-how to program a computer to mimic the thought processes of an expert through an appropriate representation scheme” is called knowledge representation (KR)
It involves knowledge of a shell or a programming language that will represent the expert knowledge
6. A number of KR schemes shares 2 common characteristics:
a.They contain facts that can be used in reasoning
b.They can be programmed with existing computer languages
7. There are generally 2 types of KR schemes:
a.Analysis representation
Support knowledge acquisition during scope establishment and initial knowledge gathering
Most techniques are pictorial such as
-semantic networks - decision trees -tables
b.Coding representation
The working code of the ES either in the form of
-frames or - production rules
8. Knowledge Representation
Analysis Representation
Coding Representation
Inference
Frames Production rules
Semantic networks Decision tables Decision trees
Selected KR Schemes
Key Idea
10. Semantic networks are the most general representation scheme.
Represent a graphical representation of knowledge that show hierarchical relationships between objects.
Made up of a network of nodes and arc.
The nodes represent objects and the arc the relationships between objects.
KR Scheme 1
Node
Arc
12. Example:
License
Seal
Examination
Air rescue
Emergency landing procedures
Olesek
Insignia
Shirt sleeve
Two
Male
Harding
Person
Apparel
Uniform
Black
Cap
has-a
certifies
has-a
in-a
in-an
has- an
is-on
is-a
number- of
has-a
is-a
race-is
is-a
wears
is-a
is-an
KR Scheme 1
13. Nodes represent the objects, concepts, or events in the world.
Names of the arcs
correspond to names of relations
indicate which concepts or objects are linked by the relations.
KR Scheme 1
14. The 2 common arcs used are:
IS-A is used to show class relationship.
HAS-A is used to identify characteristics or attributes of the object nodes
other arcs are used for definitional purpose only
KR Scheme 1
15. Organized in a spreadsheet format, using columns and rows
The table divided into two parts:
A list of attributes is developed and for each attribute, all possible values are listed
A list of possible conclusions. The different configurations of attributes are match against the conclusion.
Attributes
Conclusions
KR Scheme 2
16. Decision Table for Gift Problem
Decision Factors
Result
Money
Age
Gift
Much
Adult
Car
Much
Child
Computer
Little
Adult
Toaster
Little
Childs
Calculator
KR Scheme 2
18. A hierarchical arranged semantic network and is closely related to a decision table
It is composed of nodes representing goals and links representing decisions
Rules can be extracted from the decision tree, that can be executed by computer program
A major advantage can simplify knowledge acquisition process
KR Scheme 3
20. A decision tree for an electrical system diagnosis
Terminals
Battery Voltage
Distributor
Charger
Not Loose
Loose
Tighten Terminals
<12
>12
OK
OK
Bad
Bad
Check Starter
Replace Distributor
Replace Charger
Replace Battery
KR Scheme 3
22. A frame is a data structure for representing common concepts and situations (stereotype knowledge).
Like semantic nets, frames can be organized in a hierarchy with general concepts near the top and specific concepts placed at the lower levels.
General -top
Specific -lower
KR Scheme 4
23. Unlike semantic nets, each frame or node in this hierarchy can be very rich in supplementary information.
KR Scheme 4
24. Values that describe one object are grouped together into single unit.
Knowledge partition into slots.
Each slot describes:
• declarative knowledge (colour of a car)
• procedural knowledge (activate a certain rule if the value exceeds a certain value)
KR Scheme 4
25. Frames describe an object in great detail. The detail in form of slots that describe the various and characteristic of the object or situation.
KR Scheme 4
27. Class frame
Represents general characteristics of some set of common objects. For example Cars, Boats, and Birds.
Defines those properties that are common to all the objects within the class, and possibly default property values.
static: describe an object feature whose value doesn’t change
dynamic: is a feature whose value is likely to change during operation of the system
KR Scheme 4
28. Example of Class Frame
Class Name:
Properties:
Bird
Try
Flies
2
No Wings
Worms
Eats
Unknown
Color
Unknown
Hungry
Unknown
Activity
KR Scheme 4
30. An Instance of Frame
Describes a specific instance (sub-class or examples) of a class frame.
The instance inherits both properties and property values from its frame class.
The property values can be changed (recall: static/dynamic) to tailor the object represented in the instance.
Many instances of the frame class can be created.
The instances immediately inherit the frame’s properties.
Can speed up system coding.
KR Scheme 4
31. Instance frame
Frame Name:
Class Name:
Properties:
Tweety
Bird
False
Flies
1
No Wings
Eats
Yellow
Color
Hungry
Activity
Lives
Cage
KR Scheme 4
32. Frame Inheritance
From example, “Tweety” is an instance of Bird class.
Can allow an instance to accept the class default values or provide values unique to the instance.
Like most bird Tweety eats worms, but has only one wing and cannot fly.
Can also provide unique properties. e.g. if Tweety lives in a cage.
KR Scheme 4
33. Frame Inheritance
Inheriting behaviour
Beside inheriting descriptive information from its class, an instance also inherits its behaviour.
Need to include a procedure (method) within class frame that define some actions that the frame performs.
KR Scheme 4
34. A form of procedural knowledge that describe how to solve a problem.
The procedural and/or factual knowledge is represented as rules, called productions, in the form of condition-action pairs.
Are stated as follows:
"IF this condition occurs, THEN do this action; or this result (or conclusion or consequence) will occur.
KR Scheme 5
35. Examples
IF flammable liquid was spilled,
THEN call the fire department.
IF the pH of the spill is less than 6,
THEN the spill material is an acid.
IF the spill material is an acid,
and the spill smells like vinegar,
THEN the spill material is acetic acid.
KR Scheme 5
36. When the IF portion of a rule is satisfied by the facts, the action specified by the THEN is performed.
When this happens, the rule is said to "fire" or "execute".
KR Scheme 5
37. Relationship
IF The battery is dead
THEN The car will not start
Recommendation
IF The car will not start
THEN take a cab
Directive
IF The car will not start
AND the fuel is okay
THEN check out the electrical system
KR Scheme 5
38. Strategy
IF The car will not start
THEN first check out the fuel system then check out electrical system
Heuristic
IF The car will not start
AND The car is a 1957 Ford
THEN check the float
KR Scheme 5
39. Uncertain Rules
IF inflation is high
THEN Almost certainly interest rates are high
Can assign Certainty Factors:
IF inflation is high
THEN interest are high CF=0.8
KR Scheme 5
40. Meta-Rules
A rule that describe how other rules should be used.
IF the car will not start
AND the electrical system is operating normally
THEN use rules concerning the fuel system
KR Scheme 5
41. Rules are easy to understand
Inference and explanation are easy to derive
Modifications and maintenance are relatively easy
Uncertainty is easily combined with rules
Each rule is usually independent of all others
KR Scheme 5
42. The oldest form of knowledge representation in a computer is logic
Logic is concerned with the truthfulness of a chain of statements.
An argument is true if and only if, when all assumptions are true, then all conclusions are also true.
2 kinds of logic:
Propositional Logic
Predicate Calculus
Both use symbols to represent knowledge and operators applied to the symbols to produce logical reasoning
KR Scheme 6
43. Propositional logic represents and reasons with propositions.
PL assigns symbolic variable to a proposition such as
A = The car will start
In PL, if we are concern with the truth of the statement, we will check the truth of A.
KR Scheme 6a
45. Propositions that are linked together with connectives, such as AND, OR, NOT, IMPLIES, and EQUIVALENT, are called compound statements.
Example:
IF The students work hard
AND Always come to lectures
AND Do all their homework
THEN They will get a good grade
Using logic symbols: A B C -> D
Propositional logic is concerned with the truthfulness of compound statements, depending on the connectives.
KR Scheme 6a
46. F
F
F
F
T
T
F
T
F
T
T
F
T
F
F
T
F
F
T
T
T
T
F
T
Truth Table
KR Scheme 6a
47. Implies Operator: C = A B (C is A implies B)
For an implication of C, if A is true, then B is implied to be true
(A B) ( A B)
The implies return an F when A is TRUE and B is FALSE Otherwise it returns TRUE.
A
B
A B
F
F
T
F
T
T
T
F
F
T
T
T
KR Scheme 6a
48. Example to illustrate Implies
IF The battery is dead (A)
THEN The car won’t start (B)
A
B
A B
F
F
T
F
T
T
T
F
F
T
T
T
KR Scheme 6a
49. PL offers techniques for capturing facts or rules in a symbolic form and then operates on them through use of logical operators.
PL provides methods for managing statements that are either TRUE or FALSE.
KR Scheme 6a
50. Some PL weakness:
1.Limited ability to express knowledge and lose much of their meanings.
The Pacific Ocean contains water.
Florida is a state within the USA.
Only assigning a true value without making any statement about ‘oceanhood’ or ‘statehood’.
KR Scheme 6a
51. Some PL weakness:
2. Not all statements can be represented.
All men are mortals.
Some dogs like cats.
Thus, need a more general form of logic that capable of representing the details.
Therefore Predicate Calculus is introduced.
KR Scheme 6a
52. Enhances processing by allowing the use of variables and functions.
Use symbols that represent
• constants
• predicates
• variables
• functions
Operate on these symbols using PL operators ( .
KR Scheme 6b
53. Specific objects or properties about a problem.
Begin with lower case.
Example: ahmad, elephant and temperature
KR Scheme 6b
54. Divide a proposition into 2 parts:
predicate: assertion about object
argument: represents the object
To represent a statement “John likes Mary.” in a predicate calculus.
likes(john, mary)
A predicate Arguments
KR Scheme 6b
55. Represents general classes of objects or properties.
Written as symbols beginning with upper case.
likes(john, X)
KR Scheme 6b
56. Permits symbols to be used to represent functions.
A function denotes a mapping from entities of a set to a unique element of another set.
father_of(john) mother_of(john)
Can be also used within predicates. For example:
married(father_of(john), mother_of(john))
KR Scheme 6b
57. PC uses the same operators as in PL.
Proposition:
David is John’s father. father(david, john)
Jane is John’s mother. mother(jane, john)
If X is John’s father and Y is John’s mother then X is Y’s husband.
father(X, john) mother(Y, john) husband(X, Y)
KR Scheme 6b
58. PC introduces 2 symbols called variable quantifiers.
1. universal quantifier: for all
2. existential quantifier: there exist
KR Scheme 6b
59. indicates an expression is TRUE for all values of designated variable.
Example:
X likes (X, mary)
means for all values of X, X likes Mary.
“Everyone likes Mary.”
KR Scheme 6b
60. indicates an expression is TRUE for some values of the variable; at least one value existed that makes the statement is true:
Example:
X likes (X,mary)
means there exist X where X likes Mary.
“Someone likes Mary.”
KR Scheme 6b
61. Parentheses are used to indicate the scope of quantification
X (likes(X,mary) nice(mary) nice (X))
determines all instances of X who like Mary and if Mary is nice, then it is implied that those who like Mary are nice too.
X (man(X) mortal(X))
All men are mortal.
Man(X)
KR Scheme 6b
62. PC can provide reasoning capability to intelligent systems
Reasoning requires the ability to infer conclusions from available facts.
One simple form of inference is modus ponens:
IF A is true
AND A B is true
THEN B is true
Based on the available facts below: X (man(X) mortal(X)) man(socrates)
We can infer a conclusion of mortal(socrates)
KR Scheme 6b
63. father(david, john)
mother(jane, john)
father(X, john) mother(Y, john) husband(X, Y)
Who is X? Who is Y? Who is Y’s husband?
From the above facts and rule, we can infer some more conclusions …..
KR Scheme 6b
64. The semantic network and its equivalent predicate calculus.
is_a(E1,elephant) name(E1, clyde) num_trunk(elephant, 1) tail(elephant, 1) num_legs(elephant, 4) skin_colour(elephant, grey)
65. Convert the predicate calculus below into its equivalent semantic networks.
has_size(bluebird, small) has_covering(bird, feathers) has_colour(bluebird, blue) has_property(bird, flies) is_a(bluebird, bird) is_a(bird, vetebrate)
66. Represent the following English sentences in predicate calculus:
1.Monkeys like bananas.
2.Dogs chase cats.
3.John doesn’t like ice-creams.
4.If weather is good, I go jogging.
67. Prepare a frame of an automobile that you know, show 2 levels of hierarchy. Fill some property and property values. (static and dynamic)
68. Try to crank the starter. If it is dead or cranks slowly, turn on the headlights. If the headlights are bright (or dim only slightly), 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 (not to ground). If the starter cranks normally, the problem is in the wiring or in the solenoid; check them up to the ignition switch. If the starter does not work normally, check the bushings (see section 7-3 of the manual for instructions). If the bushings are good send the starter to the test station or replace it. If the headlights are out or very dim, check the battery (see section 7-4 for instructions). 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 the ground. Have the starter tested or replace it. (Based on Carrice et al. [5]).