The document discusses internal representation in artificial intelligence. It explains that in order to act intelligently, a computer must have knowledge about the domain of interest represented in a format it can understand called an internal representation. It describes properties an effective internal representation should have such as removing ambiguity and explicitly representing functional structure. Predicate calculus is presented as a common internal representation method that uses predicates, variables, quantifiers and other tools to represent knowledge. Alternative notations for knowledge representation like semantic networks and frame notation are also discussed.
This document discusses distributed query processing and optimization. It covers query processing methodology which includes query decomposition, data localization, and global query optimization. Query decomposition takes a high-level query and breaks it down into fragments. Data localization determines which data fragments are involved. Global query optimization finds the most efficient execution plan by considering costs of operations and minimizing communication. The goal is to optimize queries running across distributed data in a network.
The document discusses importing and exporting data in R. It describes how to import data from CSV, TXT, and Excel files using functions like read.table(), read.csv(), and read_excel(). It also describes how to export data to CSV, TXT, and Excel file formats using write functions. The document also demonstrates how to check the structure and dimensions of data, modify variable names, derive new variables, and recode categorical variables in R.
This document discusses object query language (OQL) and the six-layer architecture model for object-oriented databases. It provides an overview of OQL, describing how it is based on SQL but extends it to support object-oriented notions. It also outlines the main components of the six-layer model - the interaction layer, application layer, administrative layer, security layer, virtual layer, and paging layer - and describes their basic responsibilities in managing and securing object-oriented data. Finally, it briefly lists some disadvantages of object-oriented database management systems.
This document provides instructions for experiments in a Database Management System laboratory course. It includes a list of 12 experiments covering topics like Data Definition Language commands, Data Manipulation Language commands, database design using ER modeling and normalization, and implementation of various database applications. It also provides details on the hardware and software requirements for the course, as well as the internal assessment structure including marks distribution.
Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.
This document discusses concepts related to distributed database management systems (DBMS). It covers topics such as distributed database design, distributed query processing, distributed transaction management, and concurrency control. For concurrency control, it describes locking-based algorithms like two-phase locking and timestamp ordering, as well as optimistic concurrency control. It also discusses issues like deadlocks and approaches for deadlock management.
The document discusses version space learning, an approach to machine learning where both the most general and most specific hypotheses consistent with the training examples are maintained. It begins by introducing concept learning and version spaces, showing how all possible hypotheses can be represented as a lattice. The Find-S and Dual Find-S algorithms are presented for updating the version spaces in response to positive and negative examples. The key properties of version spaces are that they track all hypotheses consistent with the examples seen so far, avoiding premature commitment to a single hypothesis.
This document discusses distributed query processing and optimization. It covers query processing methodology which includes query decomposition, data localization, and global query optimization. Query decomposition takes a high-level query and breaks it down into fragments. Data localization determines which data fragments are involved. Global query optimization finds the most efficient execution plan by considering costs of operations and minimizing communication. The goal is to optimize queries running across distributed data in a network.
The document discusses importing and exporting data in R. It describes how to import data from CSV, TXT, and Excel files using functions like read.table(), read.csv(), and read_excel(). It also describes how to export data to CSV, TXT, and Excel file formats using write functions. The document also demonstrates how to check the structure and dimensions of data, modify variable names, derive new variables, and recode categorical variables in R.
This document discusses object query language (OQL) and the six-layer architecture model for object-oriented databases. It provides an overview of OQL, describing how it is based on SQL but extends it to support object-oriented notions. It also outlines the main components of the six-layer model - the interaction layer, application layer, administrative layer, security layer, virtual layer, and paging layer - and describes their basic responsibilities in managing and securing object-oriented data. Finally, it briefly lists some disadvantages of object-oriented database management systems.
This document provides instructions for experiments in a Database Management System laboratory course. It includes a list of 12 experiments covering topics like Data Definition Language commands, Data Manipulation Language commands, database design using ER modeling and normalization, and implementation of various database applications. It also provides details on the hardware and software requirements for the course, as well as the internal assessment structure including marks distribution.
Dynamic Itemset Counting (DIC) is an algorithm for efficiently mining frequent itemsets from transactional data that improves upon the Apriori algorithm. DIC allows itemsets to begin being counted as soon as it is suspected they may be frequent, rather than waiting until the end of each pass like Apriori. DIC uses different markings like solid/dashed boxes and circles to track the counting status of itemsets. It can generate frequent itemsets and association rules using conviction in fewer passes over the data compared to Apriori.
This document discusses concepts related to distributed database management systems (DBMS). It covers topics such as distributed database design, distributed query processing, distributed transaction management, and concurrency control. For concurrency control, it describes locking-based algorithms like two-phase locking and timestamp ordering, as well as optimistic concurrency control. It also discusses issues like deadlocks and approaches for deadlock management.
The document discusses version space learning, an approach to machine learning where both the most general and most specific hypotheses consistent with the training examples are maintained. It begins by introducing concept learning and version spaces, showing how all possible hypotheses can be represented as a lattice. The Find-S and Dual Find-S algorithms are presented for updating the version spaces in response to positive and negative examples. The key properties of version spaces are that they track all hypotheses consistent with the examples seen so far, avoiding premature commitment to a single hypothesis.
This document discusses database fragmentation in distributed database management systems (DDBMS). Database fragmentation allows a single database object to be broken into multiple segments that can be stored across different sites on a network. This improves efficiency, security, parallelism, availability, reliability and performance. There are three main types of fragmentation: horizontal, vertical, and mixed. Horizontal fragmentation breaks data by attributes like location, vertical by attributes like departments, and mixed uses both. While fragmentation provides advantages, it also increases complexity, cost, and makes security and integrity control more difficult.
The document discusses distributed query processing and optimization in distributed database systems. It covers topics like query decomposition, distributed query optimization techniques including cost models, statistics collection and use, and algorithms for query optimization. Specifically, it describes the process of optimizing queries distributed across multiple database fragments or sites including generating the search space of possible query execution plans, using cost functions and statistics to pick the best plan, and examples of deterministic and randomized search strategies used.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document provides an overview of software engineering processes including requirement engineering, feasibility studies, data flow diagrams, entity relationship diagrams, decision tables, software requirement specifications, IEEE standards, software quality assurance, verification and validation, and ISO quality standards. It discusses the key activities in requirement elicitation and management, and the phases of feasibility analysis and quality planning.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
This document discusses distributed databases and client-server architectures. It begins by outlining distributed database concepts like fragmentation, replication and allocation of data across multiple sites. It then describes different types of distributed database systems including homogeneous, heterogeneous, federated and multidatabase systems. Query processing techniques like query decomposition and optimization strategies for distributed queries are also covered. Finally, the document discusses client-server architecture and its various components for managing distributed databases.
Equivalence class testing is a software testing technique that divides input values into valid and invalid categories called equivalence classes. Representative values are selected from each class as test data. This technique reduces the number of test cases needed while maintaining thorough coverage. An example divides numbers into classes of valid 2-3 digit numbers and invalid single digit numbers to test a program's valid and invalid number handling. There are different types of equivalence class testing that vary in robustness. The technique helps reduce testing time and cases but requires expertise to define classes and may not test all boundary conditions.
Architectural design steps, Representing the system in context, Archetypes, instantiations of system, Refine architecture into components, Refine components structure, ADL, Fundamentals of Software Engineering
A structure chart is a top-down diagram that shows the breakdown of a system into manageable sub-modules. It represents each module as a box with lines connecting them to show relationships. Structure charts are used in software engineering to plan program structure and divide a problem into smaller tasks. They provide a hierarchical visualization of how a program or system is decomposed.
The document discusses various types of physical storage media used in databases, including their characteristics and performance measures. It covers volatile storage like cache and main memory, and non-volatile storage like magnetic disks, flash memory, optical disks, and tape. It describes how magnetic disks work and factors that influence disk performance like seek time, rotational latency, and transfer rate. Optimization techniques for disk block access like file organization and write buffering are also summarized.
Clips basics how to make expert system in clips | facts adding | rules makin...NaumanMalik30
AOA #CS607 k is tutorials ma meny #clips programming ma ES bnana sikhaya
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The document discusses AND/OR graphs, which are a type of graph or tree used to represent solutions to problems that can be decomposed into smaller subproblems. AND/OR graphs have nodes that represent goals or states, with successors labeled as either AND or OR branches. AND branches signify subgoals that must all be achieved to satisfy the parent goal, while OR branches indicate alternative subgoals that could achieve the parent goal. The graph helps model how decomposed subproblems relate and their solutions combine to solve the overall problem.
This document contains a data structures question paper from Anna University. It has two parts:
Part A contains 10 short answer questions covering topics like ADT, linked stacks, graph theory, algorithm analysis, binary search trees, and more.
Part B contains 5 long answer questions each worth 16 marks. Topics include algorithms for binary search, linear search, recursion, sorting, trees, graphs, files, and more. Students are required to write algorithms, analyze time complexity, and provide examples for each question.
Structured Vs, Object Oriented Analysis and DesignMotaz Saad
This document discusses structured vs object-oriented analysis and design (SAD vs OOAD) for software development. It outlines the phases and modeling techniques used in SAD like data flow diagrams, decision tables, and entity relationship diagrams. It also outlines the phases and modeling techniques used in OOAD like use cases, class diagrams, sequence diagrams, and state machine diagrams. The document compares key differences between SAD and OOAD, discusses textbooks on software engineering and UML, and references papers on using UML in practice and evaluating the impact and costs/benefits of UML in software maintenance.
This document outlines the syllabus for a Software Engineering course, including 11 topics that will be covered over several hours: Introduction to Software Engineering, Software Design, Using APIs, Software Tools and Environments, Software Processes, Software Requirements and Specifications, Software Validation, Software Evolution, Software Project Management, Formal Methods, and Specialized Systems Development. The main texts to be used are listed as two Software Engineering books by Sommerville and Pressman.
The document discusses different string matching algorithms:
1. The naive string matching algorithm compares characters in the text and pattern sequentially to find matches.
2. The Robin-Karp algorithm uses hashing to quickly determine if the pattern is present in the text before doing full comparisons.
3. Finite automata models the pattern as states in an automaton to efficiently search the text for matches.
Priti Srinivas Sajja is an Associate Professor in the Department of Computer Science at Sardar Patel University. The document discusses various topics in artificial intelligence including natural vs artificial intelligence, types of AI tests, applications of AI, knowledge representation in AI systems, bio-inspired computing approaches like artificial neural networks, genetic algorithms, and swarm intelligence. It provides examples of different AI techniques and references for further reading.
Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012). She is Principal Investigator of a major research project funded by UGC, India.
She has 113 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Her four publications have won best research paper awards. for more detail, please visir pritisajja.info.
This document discusses database fragmentation in distributed database management systems (DDBMS). Database fragmentation allows a single database object to be broken into multiple segments that can be stored across different sites on a network. This improves efficiency, security, parallelism, availability, reliability and performance. There are three main types of fragmentation: horizontal, vertical, and mixed. Horizontal fragmentation breaks data by attributes like location, vertical by attributes like departments, and mixed uses both. While fragmentation provides advantages, it also increases complexity, cost, and makes security and integrity control more difficult.
The document discusses distributed query processing and optimization in distributed database systems. It covers topics like query decomposition, distributed query optimization techniques including cost models, statistics collection and use, and algorithms for query optimization. Specifically, it describes the process of optimizing queries distributed across multiple database fragments or sites including generating the search space of possible query execution plans, using cost functions and statistics to pick the best plan, and examples of deterministic and randomized search strategies used.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document provides an overview of software engineering processes including requirement engineering, feasibility studies, data flow diagrams, entity relationship diagrams, decision tables, software requirement specifications, IEEE standards, software quality assurance, verification and validation, and ISO quality standards. It discusses the key activities in requirement elicitation and management, and the phases of feasibility analysis and quality planning.
This document discusses different approaches to requirements modeling including scenario-based modeling using use cases and activity diagrams, data modeling using entity-relationship diagrams, and class-based modeling using class-responsibility-collaborator diagrams. Requirements modeling depicts requirements using text and diagrams to help validate requirements from different perspectives and uncover errors, inconsistencies, and omissions. The models focus on what the system needs to do at a high level rather than implementation details.
This document discusses distributed databases and client-server architectures. It begins by outlining distributed database concepts like fragmentation, replication and allocation of data across multiple sites. It then describes different types of distributed database systems including homogeneous, heterogeneous, federated and multidatabase systems. Query processing techniques like query decomposition and optimization strategies for distributed queries are also covered. Finally, the document discusses client-server architecture and its various components for managing distributed databases.
Equivalence class testing is a software testing technique that divides input values into valid and invalid categories called equivalence classes. Representative values are selected from each class as test data. This technique reduces the number of test cases needed while maintaining thorough coverage. An example divides numbers into classes of valid 2-3 digit numbers and invalid single digit numbers to test a program's valid and invalid number handling. There are different types of equivalence class testing that vary in robustness. The technique helps reduce testing time and cases but requires expertise to define classes and may not test all boundary conditions.
Architectural design steps, Representing the system in context, Archetypes, instantiations of system, Refine architecture into components, Refine components structure, ADL, Fundamentals of Software Engineering
A structure chart is a top-down diagram that shows the breakdown of a system into manageable sub-modules. It represents each module as a box with lines connecting them to show relationships. Structure charts are used in software engineering to plan program structure and divide a problem into smaller tasks. They provide a hierarchical visualization of how a program or system is decomposed.
The document discusses various types of physical storage media used in databases, including their characteristics and performance measures. It covers volatile storage like cache and main memory, and non-volatile storage like magnetic disks, flash memory, optical disks, and tape. It describes how magnetic disks work and factors that influence disk performance like seek time, rotational latency, and transfer rate. Optimization techniques for disk block access like file organization and write buffering are also summarized.
Clips basics how to make expert system in clips | facts adding | rules makin...NaumanMalik30
AOA #CS607 k is tutorials ma meny #clips programming ma ES bnana sikhaya
Facebook: https://web.facebook.com/Nauman1
.Here is my #slideshare #link for downloading slides..
Asssignments k lia facebook link per contact krain
umeed hai ki aapko ye video achi lgi.
Please Share, Support, follow , Subscribe!!! or if u Need help me?
Facebook: https://web.facebook.com/Nauman1
Linkedin : https://bit.ly/2DYFgTg
Download #Artificial_intelligence_slides https://bit.ly/2HTb3dD
Subscribe Nauman Malik channel: https://bit.ly/2t1P3Dd
Cs607 #playlist on Youtube: https://bit.ly/2DNUjQM
Instagram: https://www.instagram.com/nauman_mlik/
Google Plus: https://bit.ly/2MSJq3n
BLOGspot https://naumanai.blogspot.com/
About : Nauman Malik is actually a YouTube Channel, where you will find #University
courses videos #Artificial_intelligence #cs607 #robotic technological videos in Urdu_
Hindi, #keep in touch for your Future #needs So don’t forgot to subscribe :)
The document discusses AND/OR graphs, which are a type of graph or tree used to represent solutions to problems that can be decomposed into smaller subproblems. AND/OR graphs have nodes that represent goals or states, with successors labeled as either AND or OR branches. AND branches signify subgoals that must all be achieved to satisfy the parent goal, while OR branches indicate alternative subgoals that could achieve the parent goal. The graph helps model how decomposed subproblems relate and their solutions combine to solve the overall problem.
This document contains a data structures question paper from Anna University. It has two parts:
Part A contains 10 short answer questions covering topics like ADT, linked stacks, graph theory, algorithm analysis, binary search trees, and more.
Part B contains 5 long answer questions each worth 16 marks. Topics include algorithms for binary search, linear search, recursion, sorting, trees, graphs, files, and more. Students are required to write algorithms, analyze time complexity, and provide examples for each question.
Structured Vs, Object Oriented Analysis and DesignMotaz Saad
This document discusses structured vs object-oriented analysis and design (SAD vs OOAD) for software development. It outlines the phases and modeling techniques used in SAD like data flow diagrams, decision tables, and entity relationship diagrams. It also outlines the phases and modeling techniques used in OOAD like use cases, class diagrams, sequence diagrams, and state machine diagrams. The document compares key differences between SAD and OOAD, discusses textbooks on software engineering and UML, and references papers on using UML in practice and evaluating the impact and costs/benefits of UML in software maintenance.
This document outlines the syllabus for a Software Engineering course, including 11 topics that will be covered over several hours: Introduction to Software Engineering, Software Design, Using APIs, Software Tools and Environments, Software Processes, Software Requirements and Specifications, Software Validation, Software Evolution, Software Project Management, Formal Methods, and Specialized Systems Development. The main texts to be used are listed as two Software Engineering books by Sommerville and Pressman.
The document discusses different string matching algorithms:
1. The naive string matching algorithm compares characters in the text and pattern sequentially to find matches.
2. The Robin-Karp algorithm uses hashing to quickly determine if the pattern is present in the text before doing full comparisons.
3. Finite automata models the pattern as states in an automaton to efficiently search the text for matches.
Priti Srinivas Sajja is an Associate Professor in the Department of Computer Science at Sardar Patel University. The document discusses various topics in artificial intelligence including natural vs artificial intelligence, types of AI tests, applications of AI, knowledge representation in AI systems, bio-inspired computing approaches like artificial neural networks, genetic algorithms, and swarm intelligence. It provides examples of different AI techniques and references for further reading.
Priti Srinivas Sajja is an Associate Professor working with Post Graduate Department of Computer Science, Sardar Patel University, India since 1994. She specializes in Artificial Intelligence especially in knowledge-based systems, soft computing and multiagent systems. She is co-author of Knowledge-Based Systems (2009) and Intelligent Technologies for Web Applications (2012). She is Principal Investigator of a major research project funded by UGC, India.
She has 113 publications in books, book chapters, journals, and in the proceedings of national and international conferences. Her four publications have won best research paper awards. for more detail, please visir pritisajja.info.
The document discusses the predicate calculus and its use for representing knowledge. It introduces the motivation and basic components of the predicate calculus language, including terms, well-formed formulas, and quantifiers. It explains the semantics of the language including interpretations, models, and the semantics of quantifiers. Finally, it provides examples of how predicate calculus can be used to conceptualize and represent knowledge about the world.
The document discusses key aspects of requirements engineering including types of requirements, the requirements engineering process, and techniques used in requirements elicitation and analysis. It describes user requirements, system requirements, functional requirements, non-functional requirements, and domain requirements. The requirements engineering process involves activities like feasibility studies, requirements elicitation and analysis, requirements specification, validation, and management. Requirements elicitation and analysis techniques include requirements discovery, classification, prioritization, documentation, and dealing with issues that can arise.
This document discusses different knowledge representation techniques used in artificial intelligence systems, including ad-hoc methods, heuristic reasoning methods, frames, associative networks, and conceptual graphs. It provides examples of each technique and how they can represent knowledge with examples from early expert systems like MYCIN. It also describes how conceptual graphs can be converted to and from first-order predicate logic.
1. The document discusses predicate calculus and knowledge representation. It provides examples of forward chaining, backward chaining, and resolution to perform inference in predicate calculus.
2. It also discusses representing knowledge as semantic graphs and in the UNL format. An example knowledge representation of "Ram is reading the newspaper" is shown.
3. The document then presents examples of using predicate calculus to represent and solve problems, including a problem about members of a himalayan club and their preferences to infer if there is a mountain climber who is not a skier. Resolution refutation is applied to solve this problem.
Artificial intelligence and knowledge representationSajan Sahu
The document discusses artificial intelligence and knowledge representation. It describes how computers can be made intelligent through speed of computation, filtering responses, using algorithms and neural networks. It also discusses knowledge representation techniques in AI like propositional logic, semantic networks, frames, predicate logic and nonmonotonic reasoning. The document provides examples and applications of AI like pattern recognition, robotics and natural language processing. It also discusses some fundamental problems of AI.
Many experts believe that artificial intelligence will reach a level of capability comparable to or exceeding human intelligence sometime this century, known as the singularity. While predictions for what this future will look like vary widely, some possibilities include robots surpassing humans and turning against their creators, humans merging with technology to achieve immortality or vastly extended lifespans, and an economy and rate of technological progress that grows at an unprecedented pace that outpaces human ability to adapt. There are also concerns that superintelligent robots may not prioritize environmental sustainability like humans do.
The document describes heuristic search algorithms including best first search, branch and bound search. Best first search maintains a priority queue of nodes and expands the node with the lowest cost function first. Branch and bound finds the optimal solution by keeping track of the best solution found so far and abandoning partial solutions that cannot improve on the best. It uses pruning to reduce the number of explored nodes. Both algorithms use concepts like traversing the root node and its neighbors in ascending order of distance from the root until reaching the goal node.
The document discusses knowledge-based systems and their ability to reason over extensive knowledge bases. It addresses the theoretical problems of soundness, completeness, and tractability when using logical reasoning systems. Horn clauses and PROLOG are introduced as more efficient ways to perform inference compared to full predicate calculus. Different methods for reasoning including forward chaining and truth and assumption-based maintenance are also summarized.
This document summarizes chapter 2 from George F Luger's book "Artificial Intelligence 6th edition" which discusses predicate logic and the predicate calculus. The chapter introduces predicates and functions, how to represent relationships between objects using predicates, and how to combine predicates using logical connectives and quantifiers. It also provides examples of using predicate logic to represent real-world scenarios like a blocks world and the unification process used in logical reasoning.
The document provides an overview of artificial intelligence (AI), including its history, goals, categories, fields of application, and future scope. It discusses how AI originated in the 1950s and has since been applied in many domains, such as games, speech recognition, and healthcare. The document also outlines the goals of simulating intelligence through traits like reasoning, knowledge representation, and planning. It describes the two main categories of AI as conventional and computational intelligence. Finally, it proposes that while narrow applications will continue advancing, general artificial intelligence remains a long-term challenge.
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.
The document provides an overview of the history and development of artificial intelligence (AI). Some key points:
- The field of AI was established in 1956 at the Dartmouth Conference where researchers proposed using computers to simulate human intelligence.
- Early milestones included programs that played games like checkers and proved mathematical theorems. Research focused on symbolic and knowledge-based approaches.
- In the 1980s, expert systems flourished but funding declined amid doubts about progress, known as an "AI winter." Subsymbolic approaches using neural networks also emerged.
- Modern AI incorporates both symbolic and subsymbolic techniques, with successes in games, robotics, machine learning and other domains. Knowledge representation and common-sense reasoning
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.
Consumer Behavior chapter 03 Learning and Memory theories MoghimiBahman Moghimi
This document discusses learning and memory theories relevant to consumer behavior. It covers classical and instrumental conditioning theories which propose that learning occurs through responses to external stimuli and rewards/punishments. Classical conditioning links an unconditioned stimulus to a conditioned stimulus through repetition, while instrumental conditioning associates behaviors with positive or negative outcomes. Cognitive learning theory suggests learning can be observational when people learn by watching others. The document also discusses memory processes including encoding, storage in sensory short-term and long-term memory, and retrieval factors. Marketers can apply these learning and memory concepts through techniques like branding, advertising, and loyalty programs.
The document provides an overview of the history and development of artificial intelligence (AI). It discusses early pioneers in AI research from the 18th century onward and key milestones like the Dartmouth Conference in 1956 which established AI as an academic field. The document also outlines different types of AI, like weak AI and strong AI, as well as many applications and fields that utilize AI today like robotics, healthcare, manufacturing, and more. It concludes by discussing the increasing use of robots in various industries and envisions the future potential of AI.
Describe what is Artificial Intelligence. What are its goals and Approaches. Different Types of Artificial Intelligence
Explain Machine learning and took one Algorithm "K-means Algorithm" and explained
The document discusses several key concepts in artificial intelligence including the physical symbol system, intelligence requiring knowledge, AI techniques like search and abstraction, the 4 steps to solve a problem using state space representation, and several search techniques like breadth-first search, depth-first search, heuristic search, and means-ends analysis. Production systems and heuristic functions are also covered.
The document defines binary relations and provides examples of binary relations between sets. It then discusses properties of binary relations such as being reflexive, symmetric, transitive, complete, antisymmetric, asymmetric, or irreflexive. It introduces the concepts of preorders, orders, equivalence relations, and partitions. A preorder is a binary relation that is transitive and either reflexive or irreflexive. An order is a complete, transitive, and antisymmetric preorder. An equivalence relation is a reflexive, symmetric, and transitive binary relation that partitions a set into equivalence classes. Utility functions are introduced as a way to represent preorders, where a utility function u represents a preorder R if xRy if and
Propositional calculus (also called propositional logic, sentential calculus, sentential logic, or sometimes zeroth-order logic) is the branch of logic concerned with the study of propositions (whether they are true or false) that are formed by other propositions with the use of logical connectives, and how their value depends on the truth value of their components. Logical connectives are found in natural languages.
This document provides an overview of predicate logic, including:
- The basic components of predicate logic like variables, predicates, quantifiers, and propositional functions
- Explanations of the universal and existential quantifiers
- How to negate quantified expressions using De Morgan's laws
- Examples of translating statements between English and predicate logic
This document provides an overview of discrete structures for computer science. It discusses topics like:
- Logic and propositions - Expressing statements that are either true or false using logical operators like negation, conjunction, disjunction etc.
- Truth tables - Using tables to show the truth values of compound propositions formed by combining simpler propositions with logical operators.
- Logical equivalence - When two statement forms will always have the same truth value no matter the values of the variables.
- Essential topics covered in discrete structures like functions, relations, sets, graphs, trees, recursion, proof techniques and basics of counting.
Logic is important for mathematical reasoning, program design and electronic circuitry. Proposition
Rough sets and fuzzy rough sets in Decision MakingDrATAMILARASIMCA
Rough sets, Fuzzy rough sets, lower approximation, upper approximation, positive region and reduct, Equivalence relation, dependency coefficient, Information system for road accident system
Propositional logic represents facts as being either true or false. It defines a syntax of allowed expressions, semantics that map expressions to meanings in terms of truth values, and inference rules for deriving new conclusions from existing statements. The syntax includes propositional symbols, logical constants, and logical connectives. Semantics define the truth conditions for sentences using truth tables. Inference rules like modus ponens, chain rule, substitution, simplification, conjunction, and transposition allow drawing valid conclusions.
The document discusses propositional logic including:
- Propositional logic uses propositions that can be either true or false and logical connectives to connect propositions.
- It introduces syntax of propositional logic including atomic and compound propositions.
- Logical connectives like negation, conjunction, disjunction, implication, and biconditional are explained along with their truth tables and significance.
- Other concepts discussed include precedence of connectives, logical equivalence, properties of operators, and limitations of propositional logic.
- Examples are provided to illustrate propositional logic concepts like truth tables, logical equivalence, and translating English statements to symbolic form.
L03 ai - knowledge representation using logicManjula V
The document discusses knowledge representation using predicate logic. It begins by reviewing propositional logic and its semantics using truth tables. It then introduces predicate logic, which can represent properties and relations using predicates with arguments. It discusses representing knowledge in predicate logic using quantifiers, predicates, and variables. It also covers inferencing in predicate logic using techniques like forward chaining, backward chaining, and resolution. An example problem is presented to illustrate representing a problem and solving it using resolution refutation in predicate logic.
UGC NET Computer Science & Application book.pdf [Sample]DIwakar Rajput
This document provides an overview of propositional logic and logical connectives. It defines key terms like proposition, logical connectives, truth tables, and normal forms. It describes the five basic logical connectives - negation, conjunction, disjunction, conditional, and bi-conditional. It provides truth tables and examples to explain each connective. It also discusses logical equivalences, precedence of operators, logic and bit operations, tautologies/contradictions, and normal forms. The document is a lesson on propositional logic from Diwakar Education Hub that covers basic concepts and terminology.
The document discusses different methods of representing knowledge in artificial intelligence systems, including formal logic, production rules, and structured objects like semantic networks and frames. It provides examples of representing statements in propositional and predicate calculus, and how logic-based languages like Prolog can be used for knowledge representation and reasoning. Semantic networks are introduced as a way to organize knowledge representation in a graph-like structure similar to how human memory works.
The document discusses knowledge representation using propositional logic and predicate logic. It begins by explaining the syntax and semantics of propositional logic for representing problems as logical theorems to prove. Predicate logic is then introduced as being more versatile than propositional logic for representing knowledge, as it allows quantifiers and relations between objects. Examples are provided to demonstrate how predicate logic can formally represent statements involving universal and existential quantification.
This document discusses arguments and their validity. It defines arguments as statements with premises and a conclusion. Valid arguments are those where the conclusion logically follows from the premises. Invalid arguments have conclusions that can be false even when the premises are true. The document provides examples of valid and invalid arguments. It also discusses rules of inference like modus ponens and modus tollens that are used to determine validity.
The document provides an overview of topics in discrete mathematics including logic, sets, and functions. It outlines the following content: introduction to logic and logical operators; propositions and logical equivalences; predicates and quantifiers; sets and set operations; and functions. For each topic, it provides definitions, examples, and truth tables to illustrate key concepts in propositional and predicate logic, and sets. It also includes examples, explanations and review questions to help explain the material.
The document provides an overview of predicate logic, including:
- Predicates and quantifiers are introduced as the building blocks of predicate logic. Predicates allow representing properties and relations, while quantifiers like "for all" and "there exists" are used to make statements about predicates.
- Examples demonstrate how predicates and quantifiers can be used to represent concepts in logic and translate statements between English and logical expressions.
- Key concepts like universal and existential quantification, propositional functions, logical equivalences for quantifiers, and translating between English and logical expressions are defined and illustrated with examples.
- The document also discusses domains of discourse, precedence of quantifiers, thinking of quantifiers as conjunction
The document provides an overview of predicate logic, including:
- Predicates and quantifiers are introduced as the building blocks of predicate logic. Predicates allow representing properties and relations, while quantifiers like "for all" and "there exists" are used to make statements about predicates.
- Examples demonstrate how predicates and quantifiers can be used to represent concepts in logic and translate statements between English and logical expressions.
- Key concepts like universal and existential quantification, propositional functions, logical equivalences for quantifiers, and translating between English and logical expressions are defined and illustrated with examples.
- The document also discusses domains of discourse, precedence of quantifiers, thinking of quantifiers as conjunction
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1. AI in logic perspective
AI is the study of mental faculties through the
use of computational models.
It is on the premise that what brain does may
be thought of as a kind of computation.
Though what brain does easily takes enormous
efforts to be done by a machine. Eg: vision.
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1
2. Internal representation
In order to act intelligently, a computer must
have the knowledge about the domain of
interest.
Knowledge is the body of facts and principles
gathered or the act, fact, or state of knowing.
This knowledge needs to be presented in a
form, which is understood by the machine.
This
unique
format
is
called
internal
representation.
Thus plain English sentences could be translated
into an internal representation and they could
be used to answer based on the given
sentences.
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2
3. Properties of internal representation
Internal representation must remove all referential
ambiguity.
Referential ambiguity is the ambiguity about what
the sentence refers to.
Eg: ‘ Raj said that Ram was not well. He must be
lying.’
Who does ‘he ‘ refers to…?.
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3
4. Properties of internal representation..
Internal representation should avoid word-sense
ambiguity.
Word-sense ambiguity arise because of multiple
meaning of words.
Eg:
‘Raj caught a pen.
Raj caught a train.
Raj caught fever.’
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5. Properties of internal representation..
Internal representation must explicitly mention
functional structure
Functional structure is the word order used in the
language to express an idea.
Eg: ‘Ram killed Ravan. Ravan was killed by Ram.’
Thus internal representation may not use the
order of the original sentence.
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6. Properties of internal representation..
Internal representation should be able handle
complex sentence without losing meaning
attached with it.
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6
7. Predicate Calculus
Predicate Calculus is an internal representation
methodology which help us in deducing more
results from the given propositions (statements).
Predicate calculus accesses individual
components of a proposition and represent the
proposition.
For example, the sentence ‘ Raj came late on
Sunday’ can be represented in predicate calculus
as
(came-late Raj Sunday)
Here ‘came-late’ is a predicate that describes the
relation between a person and a day.
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8. ‘ Raj came late on a rainy Sunday’ can be
represented as
(came-late Raj Sunday)
(inst Sunday rainy)
Predicate permits us to break a statement down
into component parts namely, objects, a
characteristic of the object, or some assertion
about the object.
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9. Syntax of Predicate calculus
1. Predicate and Arguments
In predicate calculus, a proposition is divided
into two parts:
Arguments (or objects)
Predicate (or assertion)
The arguments are the individual or objects an
assertion is made about. The predicate is the
assertion made about them.
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9
10.
In an English language sentence, objects are
nouns that serve as subject and object of the
sentence and predicate would be the verb or part
of the verb.
For example the proposition:
‘Vinod likes apple’
would be stated as:
(likes Vinod apple)
Where ‘likes’ is the predicate and Vinod and
apple are the arguments.
In some cases, the proposition may not have
any predicates. For example:
Anita is a woman.
12/23/13 i.e. (inst Anita woman).
10
11. 2. Constants
Constants are fixed value terms that belong to
a given domain.
They are denoted by numbers and words. Eg:
123,abc.
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12.
3.Variables
In predicate calculus, letters may be substituted for
the arguments.
The symbols x or y could be used to designate some
object or individual.
The example “Vinod likes apple “ could be expressed
in variable form if x = Vinod and y = apple. Then the
proposition becomes:
(likes x,y)
If variables are used, then the stated proposition
must be true for any names substituted for the
variables.
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12
13.
Instantiation
Instantiation is the process of assigning the
name of a specific individual or object to a
variable.
That object or individual becomes an
“
instance“ of that variable.
In the previous example, supplying Vinod and
apple for x and y is a case of instantiation.
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13
14. 4. Connectives
There are four connectives used in predicate
calculus.
The are ‘not’, ‘and’, ‘or’ and ‘if’.
If p and q are formulas then
(and p, q),
(or p, q), (not p) and
(if p, q) are also
formulas.
They can be expressed in truth tables.
12/23/13
14
19.
5. Quantifiers
A quantifier is a symbol that permits us to state
the range or scope of the variables in a
predicate logic expression.
Two quantifiers are used in logic:
The universal quantifier –’for all’.
i.e (forall (x) f) for a formula f.
The existential quantifier – ‘exists’.
i.e. (exists (x) f) for a formula f.
12/23/13
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21.
“All Maharastrians are Indian citizens” could be
expressed as:
(forall (x) (if Maharastrian(x) Indiancitizen(x)).
“ Every car has a wheel” could be expressed as:
(forall (x) (if (Car x) (exists (y) wheel-of (x y))).
12/23/13
21
22. The predicate calculus consists of:
A set of constant terms.
A set of variables.
A set of predicates, each with a specified
number of arguments.
A set of functions, each with a specified
number of arguments.
The connectives- ‘if’, ‘and’, ‘or’ and ‘not’.
The quantifiers- ‘exists’ and ‘forall’.
12/23/13
22
23.
The terms used in predicate calculus are:
Constant terms.
Variables.
Functions applied to the correct number of
terms.
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23
24.
The formulas used in predicate calculus are:
A predicate applied to the correct number of
terms.
If p and q are formulas then (if p, q), (and
p, q), or(p, q) and (not p).
If x is a variable, and p is a formula, then
(exists(x) p), and (forall(x) p).
12/23/13
24
25.
In predicate calculus, the initial facts from
which we can derive more facts are called
axioms.
The facts we deduce from the axioms are called
theorems.
The set of axioms are not stable and in fact
change over time as new information (axioms)
come.
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25
26. Inference Rules
From a given set of axioms, we can deduce more
facts using inference rules. The important
inference rules are:
Modus ponens: From p and (if p q ) infer q.
Chain rule: From (if p q ) and (if q r )
infer (if p r ).
Substitution: if p is a valid axiom, then a
statement derived using consistent substitution of
propositions is also valid.
Simplification: From (and p q) infer p.
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26
27.
Conjunction: From p and q infer (and p q).
Transposition: From (if p q ) infer (if (not q )
(not p))
Universal instantiation: if something is true
of everything, then it is true for any particular
thing.
Abduction: From q and (if p q ) infer p.
(Abduction can lead to wrong conclusions. Still,
it is very important as it gives lot explanation.
For example: medical diagnosis.)
Induction: From (P a), (P, b),…. infer (forall
(x) (P x)).( Induction leads to learning.)
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28. Express the following in predicate calculus:
Roses are red.
(if (inst x rose) (color x red)).
Violets are blue.
(if (inst x violet) (color x blue)).
Every chicken hatched from an egg.
(forall (x) (if (chicken x) (exists (y) hatched-from(x y))).
Some language is spoken by everyone in this class.
(forall (x) (if (belong-to-class x) (exists (y) speaklanguage(x y))).
If you push anything hard enough, it will fall over.
(forall (x) (if (push-hard x) (fall-over x)).
Everybody loves somebody sometime.
(forall (x) ((exists (y) loves-sometime(x y))).
Anyone with two or more spouses is a bigamist.
(forall (x) ((inst x have-more-spouse) (inst x bigamist(x)))
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29.
Arun likes all kinds of food.
Apples are food.
Chicken is a food.
Anything anyone eats and is not killed
by is food.
Varun eats peanuts and is still alive.
Kavita eats everything Varun eats.
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29
30.
The members of The Club are Anil,
Sangita, Ajit and Vanita.
Anil is married to Sangita.
Ajit is Vanita’s brother.
The spouse of every married person
in the club is also in the club.
The last meeting of the club was at
Anil’s house.
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32.
Knowledge, which is represented in the internal
representation technique predicate calculus,
could be represented in a number of alternative
notations.
The important representations are:
Semantic networks
Slot assertion notation.
Frame notation
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32
33. Semantic network ( Associative networks)
One of the oldest and easiest to understand
knowledge representation schemes is the
semantic network.
They are basically graphical depictions of
knowledge that show hierarchical relationships
between objects.
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33
34.
For example ‘Sachin is a cricketer’
ie. ( inst Sachin cricketer), can be represented
in associative network as
Cricketer
inst
Sachin
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35.
A semantic network is made up of a number of
ovals or circles called nodes.
Nodes represent objects and descriptive
information about those objects.
Objects can be any physical item, concept,
event or an action.
The nodes are interconnected by links called
arcs.
These arcs show the relationships between the
various objects and descriptive factors.
The arrows on the lines point from an object to
its value along the corresponding arc.
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36.
From the viewpoint of predicate calculus,
associative networks replace terms with nodes
and relation with labeled directed arcs.
The semantic network is a very flexible
method of knowledge representation.
There are no hard rules about knowledge in
this form.
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36
37.
Semantic networks can show inheritances in the sense
that it can explain how elements of specific classes
inherit attributes and values from more general classes
in which they are included.
The isa relation is a subset relation. The cricketers is a
subset of the set of sportsman.
Cricketer
inst
isa
Sportsman
Sachin
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38.
Eg: (isa cricketer sportsman).
The instance relation corresponds to the
relation element-of.
Sachin is an element of the set of cricketers.
Thus he is an element of all the supersets of
Indian international cricketers.
The ‘isa’ relation corresponds to the relation
‘subset of’.
Cricketers is a subset of sportsmen and hence
cricketers
inherit
al the
properties of
sportsmen.
12/23/13
38
39. Example..
Is a
Boy
has a
Ravi
Child
Goes to
School
Is a
Anitha
owns
Maruti
White
is a
Anil
is a
S.E
a
Human
Is a
works for
plays
Is a
Color
Woman Is
Man
married to
Car
12/23/13
Is a
Belongs to
TATA
Cricket
made in
is a
India
Sport
TCS
39
40.
The predicate calculus lacks a backward pointer
resulting a long search for retrieving information.
Thus the predicate calculus along with an
indexing (pointing) scheme is a much better
internal representation scheme than semantic
networks as it has connectives and quantifiers.
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41. Slot assertion notation.
In a slot assertion notation various arguments ,
called slots, of predicate are expressed as
separate assertions.
Slot assertion notation is a special type of
predicate calculus representation.
For example (catch-object sachin ball) can be
expressed as
(inst catch1 catch-object)…. // catch1 is a one
type of catching.
(catcher catch1 sachin)….// sachin did the
catching.
(caught catch1 ball)…..// he caught the ball.
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42. Frame ( Slot and Filler)notation.
Frame notation combines the different slots of
the slot assertion notation.
Thus we have,
(catch-object catch1
(catcher sachin)
(caught ball)).
Here we have constructed a single structure
called a frame that includes all the
information.
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43. Convert the following to first-order predicate logic
using the predicates indicated:
swimming_pool(X)
steamy(X)
large(X)
unpleasant(X)
noisy(X)
place(X)
All large swimming pools are noisy and steamy
places.
All noisy and steamy places are unpleasant.
All noisy and steamy places except swimming
pools are unpleasant.
The swimming pool is small and quiet.
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44.
All large swimming pools are noisy and steamy places.
(forall (x) (if (and large(X) swimming_pool(X))
(and noisy(X) (and (steamy(X) place(X)))).
All noisy and steamy places are unpleasant.
(forall (x)(and noisy(X) (and (steamy(X) place(X))
unpleasant(X))).
All noisy and steamy places except swimming pools are
unpleasant.
(forall (x)((not swimming_pool(x)) and noisy(X) (and
(steamy(X) place(X)) unpleasant(X)))).
The swimming pool is small and quiet.
(and swimming_pool(x) and (not large(X)) (not noisy(X)))
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45. Represent in predicate calculus and then in
semantic network
Circus elements are elephants.
Elephants have heads and trunks.
Heads have mouths.
Elephants are animals.
Animals have hearts.
Circus elephants are performers.
Performers have costumes.
Costumes are clothes.
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