This document summarizes built-in predicates in Prolog including:
- Utility goals like help and halt
- Loading and testing types of Prolog programs
- Arithmetic, equality, and database manipulation predicates
- Input/output predicates for writing, reading, and file handling
- Control predicates for negation, conjunction, disjunction
- Finding all solutions with findall, bagof, and setof
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
This document provides a summary of key functions and commands in the R programming language for getting help, inputting and outputting data, creating and manipulating data, selecting and extracting data, performing mathematical operations, working with dates and times, plotting graphs, and more. It includes brief explanations and examples of commonly used functions like read.table(), plot(), hist(), summary(), str(), and others.
The document discusses regular expressions (regex) in Python. It provides examples of using regex to search for patterns in strings, extract matches, and find and group substrings. Key concepts covered include regex syntax like anchors, character classes, repetition, capturing groups, greedy/non-greedy matching, and the re module's functions like search, findall, finditer, and sub. Real-world applications mentioned include validating formats like IP addresses and parsing structured data.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
Beyond xUnit example-based testing: property-based testing with ScalaCheckFranklin Chen
Test-Driven Development has become deservedly popular in the past decade, with easy-to-use xUnit unit testing frameworks leading the way toward encouraging developers to write tests. But xUnit has limitations: how does one know one has written enough test cases for a desired behavior? And what if the behavior is conditional on other behavior? Property-based testing, first popularized for Haskell with the QuickCheck library, but available now for other languages as well, offers a powerful addition to one's testing toolkit.
I will discuss the concepts of property-based testing and illustrate them concretely using ScalaCheck for Scala, and point toward similar test frameworks in other languages.
Talk is about simple data structures like queue and Tree and their possible implementation in Scala. It also talks about binary search trees and their traversals.
Proposals for new function in Java SE 9 and beyondBarry Feigenbaum
Slides that propose new features for future Java SE editions (9+). Includes proposals for language and JRE enhancements across multiple domains such as ease-of-use, reduced boilerplate, increased conciseness, improved concurrency and capability.
The goal is to make Java/JRE into a state-of-the-art platform for modern software development.
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
This document provides a summary of key functions and commands in the R programming language for getting help, inputting and outputting data, creating and manipulating data, selecting and extracting data, performing mathematical operations, working with dates and times, plotting graphs, and more. It includes brief explanations and examples of commonly used functions like read.table(), plot(), hist(), summary(), str(), and others.
The document discusses regular expressions (regex) in Python. It provides examples of using regex to search for patterns in strings, extract matches, and find and group substrings. Key concepts covered include regex syntax like anchors, character classes, repetition, capturing groups, greedy/non-greedy matching, and the re module's functions like search, findall, finditer, and sub. Real-world applications mentioned include validating formats like IP addresses and parsing structured data.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses declarative programming and how Prolog uses declarative rules, facts, and predicates. It explains how Prolog performs logical operations like unification and resolution to evaluate queries against its knowledge base. It provides examples of using Prolog to represent graphs, lists, arithmetic, and more.
Beyond xUnit example-based testing: property-based testing with ScalaCheckFranklin Chen
Test-Driven Development has become deservedly popular in the past decade, with easy-to-use xUnit unit testing frameworks leading the way toward encouraging developers to write tests. But xUnit has limitations: how does one know one has written enough test cases for a desired behavior? And what if the behavior is conditional on other behavior? Property-based testing, first popularized for Haskell with the QuickCheck library, but available now for other languages as well, offers a powerful addition to one's testing toolkit.
I will discuss the concepts of property-based testing and illustrate them concretely using ScalaCheck for Scala, and point toward similar test frameworks in other languages.
Talk is about simple data structures like queue and Tree and their possible implementation in Scala. It also talks about binary search trees and their traversals.
Proposals for new function in Java SE 9 and beyondBarry Feigenbaum
Slides that propose new features for future Java SE editions (9+). Includes proposals for language and JRE enhancements across multiple domains such as ease-of-use, reduced boilerplate, increased conciseness, improved concurrency and capability.
The goal is to make Java/JRE into a state-of-the-art platform for modern software development.
The document discusses Python lists, tuples, and dictionaries. It provides examples of how to create, access, modify, and loop through each of these data types. Lists are ordered and changeable collections that allow duplicate elements. Tuples are ordered and unchangeable collections that allow duplicate elements. Dictionaries are unordered collections of key-value pairs that do not allow duplicate keys. The document demonstrates various methods and operations available for each data type, such as appending and removing elements from lists, accessing elements by index in lists and tuples, and adding or modifying elements in dictionaries.
In this presentation, You will get to know about Function Literal,Higher Order Function,Partial Function,Partial Applied Function,Nested Function,Closures.
This document discusses Prolog programming. It covers data structures in Prolog like lists and terms, programming techniques like guess-and-verify queries and open lists, and control in Prolog through goal ordering and rule selection. Lists can represent data structures and terms correspond to tree structures. Variables serve as placeholders and open lists allow modification of data. Control is characterized by selecting the leftmost goal and first applicable rule.
This document provides an overview of the Prolog programming language. It begins with an outline of the topics that will be covered, including syntax of terms, simple programs, terms as data structures, the cut operator, and writing real programs. It then defines Prolog as a logic programming language that uses logical variables and unification. The rest of the document explains key aspects of Prolog like clauses, facts, rules, queries, compound terms, and unification through examples. It also discusses how Prolog programs are executed and how clauses can be read both declaratively and procedurally.
This document provides an introduction to functional programming concepts in Haskell, including:
- Defining functions and evaluating expressions through reduction sequences.
- Currying and partial application of functions.
- Pattern matching and defining functions through multiple cases.
Learning notes of r for python programmer (Temp1)Chia-Chi Chang
R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.
Functional Programming by Examples using Haskellgoncharenko
The document discusses functional programming concepts in Haskell compared to traditional imperative languages like C++. It provides:
1) An example of quicksort implemented in both C++ and Haskell to illustrate the differences in approach and syntax between the two paradigms. The Haskell version is much more concise, using only 5 lines compared to 14 lines in C++.
2) Explanations of key functional programming concepts in Haskell including pure functions, recursion, pattern matching, and higher-order functions like map and fold.
3) Examples and definitions of commonly used Haskell functions and data types to summarize lists, sorting, and traversing elements - highlighting the more declarative style of functional programming.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
Functions are treated as objects in Scala, with the function type A => B being an abbreviation for the class scala.Function1[A, B]. Functions are objects that have an apply method. Case classes implicitly define companion objects with apply methods, allowing constructor-like syntax. Pattern matching provides a way to generalize switch statements to class hierarchies. The for expression provides a cleaner syntax than higher-order functions like map and flatMap for working with collections, but compiles to calls to these functions. Types like Option and Try are examples of monads in Scala, making failure or missing data explicit in the type while hiding boilerplate code.
Processing data with Python, using standard library modules you (probably) ne...gjcross
Tutorial #2 from PyCon AU 2012
You have data.
You have Python.
You also have a lot of choices about the best way to work with that data...
Ever wondered when you would use a tuple, list, dictionary, set, ordered dictionary, bucket, queue, counter or named tuple? Phew!
Do you know when to use a loop, iterator or generator to work through a data container?
Why are there so many different "containers" to hold data?
What are the best ways to work with these data containers?
This tutorial will give you all the basics to effectively working with data containers and iterators in Python. Along the way we will cover some very useful modules from the standard library that you may not have used before and will end up wondering how you ever did without them.
This tutorial is aimed at Python beginners. Bring along your laptop so you can interactively work through some of the examples in the tutorial. If you can, install ipython (http://ipython.org/) as we will use it for the demonstrations.
The document discusses cuts and negation in Prolog. It explains that a cut (!) allows for controlling backtracking by committing to choices made before it. Negation as failure uses fail/0 to force backtracking and, combined with a cut, can write programs to capture negation. For example, a rule saying p holds if a and b, or if not a and c, can be written as p :- a,!,b and p :- c.
The Collections Framework (java.util)- Collections overview, Collection Interfaces, The Collection classes- Array List, Linked List, Hash Set, Tree Set, Priority Queue, Array Deque. Accessing a Collection via an Iterator, Using an Iterator, The For-Each alternative, Map Interfaces and Classes, Comparators, Collection algorithms, Arrays, The Legacy Classes and Interfaces- Dictionary, Hashtable ,Properties, Stack, Vector More Utility classes, String Tokenizer, Bit Set, Date, Calendar, Random, Formatter, Scanner
Regular expressions (regex) allow users to search for patterns in text. The document provides an introduction to regex, including its basic components: a regex engine, text to search, and a regular expression pattern. It then covers various regex patterns such as literals, character classes, quantifiers, and grouping. Examples are provided to illustrate how different regex patterns can be used to extract or match text.
The document discusses one-to-one functions and their inverses. It begins by defining a one-to-one function as a function where each input has exactly one output and each output has exactly one input. It must pass both vertical and horizontal line tests. The sine function is shown to fail the horizontal line test, so it is not one-to-one. However, the inverse sine or arcsine function is one-to-one. In general, the inverse of a one-to-one function will also be a function. Examples are provided of finding the inverse of functions algebraically and graphically.
O documento discute listas em Prolog. Resume-se em 3 frases:
1) Listas são uma poderosa estrutura de dados em Prolog representada por colchetes com termos separados por vírgulas.
2) A notação [Cabeça|Calda] permite acessar o primeiro elemento e o resto da lista, possibilitando a escrita de predicados recursivos como membro/2 e anexar/3.
3) Listas podem ser usadas para representar dados como localização de objetos e predicados podem ser escritos para manipular essas list
Regression and Classification: An Artificial Neural Network ApproachKhulna University
This presentation introduces artificial neural networks (ANN) as a technique for regression and classification problems. It provides historical context on the development of ANN, describes common network structures and activation functions, and the backpropagation algorithm for training networks. Experimental results on 7 datasets show ANN outperformed other methods for both regression and classification across a variety of problem types and data characteristics. Limitations of ANN and areas for further research are also discussed.
This document discusses hill climbing, an optimization technique used to find the best solution to a problem. It begins by explaining hill climbing search and its implementation. It then provides examples of applying hill climbing to solve the N-Queen problem and the 8-puzzle problem. The document notes some drawbacks of hill climbing and introduces random restart hill climbing as a variation that can help overcome local maxima issues.
The document discusses artificial neural networks and the backpropagation algorithm. It provides an overview of ANN structure and learning process. Specifically, it discusses using ANN to control an autonomous vehicle, the backpropagation algorithm which aims to minimize error, and an example of an ANN learning a simple identity function.
Artificial Intelligence AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
The document discusses Python lists, tuples, and dictionaries. It provides examples of how to create, access, modify, and loop through each of these data types. Lists are ordered and changeable collections that allow duplicate elements. Tuples are ordered and unchangeable collections that allow duplicate elements. Dictionaries are unordered collections of key-value pairs that do not allow duplicate keys. The document demonstrates various methods and operations available for each data type, such as appending and removing elements from lists, accessing elements by index in lists and tuples, and adding or modifying elements in dictionaries.
In this presentation, You will get to know about Function Literal,Higher Order Function,Partial Function,Partial Applied Function,Nested Function,Closures.
This document discusses Prolog programming. It covers data structures in Prolog like lists and terms, programming techniques like guess-and-verify queries and open lists, and control in Prolog through goal ordering and rule selection. Lists can represent data structures and terms correspond to tree structures. Variables serve as placeholders and open lists allow modification of data. Control is characterized by selecting the leftmost goal and first applicable rule.
This document provides an overview of the Prolog programming language. It begins with an outline of the topics that will be covered, including syntax of terms, simple programs, terms as data structures, the cut operator, and writing real programs. It then defines Prolog as a logic programming language that uses logical variables and unification. The rest of the document explains key aspects of Prolog like clauses, facts, rules, queries, compound terms, and unification through examples. It also discusses how Prolog programs are executed and how clauses can be read both declaratively and procedurally.
This document provides an introduction to functional programming concepts in Haskell, including:
- Defining functions and evaluating expressions through reduction sequences.
- Currying and partial application of functions.
- Pattern matching and defining functions through multiple cases.
Learning notes of r for python programmer (Temp1)Chia-Chi Chang
R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.
Functional Programming by Examples using Haskellgoncharenko
The document discusses functional programming concepts in Haskell compared to traditional imperative languages like C++. It provides:
1) An example of quicksort implemented in both C++ and Haskell to illustrate the differences in approach and syntax between the two paradigms. The Haskell version is much more concise, using only 5 lines compared to 14 lines in C++.
2) Explanations of key functional programming concepts in Haskell including pure functions, recursion, pattern matching, and higher-order functions like map and fold.
3) Examples and definitions of commonly used Haskell functions and data types to summarize lists, sorting, and traversing elements - highlighting the more declarative style of functional programming.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
Functions are treated as objects in Scala, with the function type A => B being an abbreviation for the class scala.Function1[A, B]. Functions are objects that have an apply method. Case classes implicitly define companion objects with apply methods, allowing constructor-like syntax. Pattern matching provides a way to generalize switch statements to class hierarchies. The for expression provides a cleaner syntax than higher-order functions like map and flatMap for working with collections, but compiles to calls to these functions. Types like Option and Try are examples of monads in Scala, making failure or missing data explicit in the type while hiding boilerplate code.
Processing data with Python, using standard library modules you (probably) ne...gjcross
Tutorial #2 from PyCon AU 2012
You have data.
You have Python.
You also have a lot of choices about the best way to work with that data...
Ever wondered when you would use a tuple, list, dictionary, set, ordered dictionary, bucket, queue, counter or named tuple? Phew!
Do you know when to use a loop, iterator or generator to work through a data container?
Why are there so many different "containers" to hold data?
What are the best ways to work with these data containers?
This tutorial will give you all the basics to effectively working with data containers and iterators in Python. Along the way we will cover some very useful modules from the standard library that you may not have used before and will end up wondering how you ever did without them.
This tutorial is aimed at Python beginners. Bring along your laptop so you can interactively work through some of the examples in the tutorial. If you can, install ipython (http://ipython.org/) as we will use it for the demonstrations.
The document discusses cuts and negation in Prolog. It explains that a cut (!) allows for controlling backtracking by committing to choices made before it. Negation as failure uses fail/0 to force backtracking and, combined with a cut, can write programs to capture negation. For example, a rule saying p holds if a and b, or if not a and c, can be written as p :- a,!,b and p :- c.
The Collections Framework (java.util)- Collections overview, Collection Interfaces, The Collection classes- Array List, Linked List, Hash Set, Tree Set, Priority Queue, Array Deque. Accessing a Collection via an Iterator, Using an Iterator, The For-Each alternative, Map Interfaces and Classes, Comparators, Collection algorithms, Arrays, The Legacy Classes and Interfaces- Dictionary, Hashtable ,Properties, Stack, Vector More Utility classes, String Tokenizer, Bit Set, Date, Calendar, Random, Formatter, Scanner
Regular expressions (regex) allow users to search for patterns in text. The document provides an introduction to regex, including its basic components: a regex engine, text to search, and a regular expression pattern. It then covers various regex patterns such as literals, character classes, quantifiers, and grouping. Examples are provided to illustrate how different regex patterns can be used to extract or match text.
The document discusses one-to-one functions and their inverses. It begins by defining a one-to-one function as a function where each input has exactly one output and each output has exactly one input. It must pass both vertical and horizontal line tests. The sine function is shown to fail the horizontal line test, so it is not one-to-one. However, the inverse sine or arcsine function is one-to-one. In general, the inverse of a one-to-one function will also be a function. Examples are provided of finding the inverse of functions algebraically and graphically.
O documento discute listas em Prolog. Resume-se em 3 frases:
1) Listas são uma poderosa estrutura de dados em Prolog representada por colchetes com termos separados por vírgulas.
2) A notação [Cabeça|Calda] permite acessar o primeiro elemento e o resto da lista, possibilitando a escrita de predicados recursivos como membro/2 e anexar/3.
3) Listas podem ser usadas para representar dados como localização de objetos e predicados podem ser escritos para manipular essas list
Regression and Classification: An Artificial Neural Network ApproachKhulna University
This presentation introduces artificial neural networks (ANN) as a technique for regression and classification problems. It provides historical context on the development of ANN, describes common network structures and activation functions, and the backpropagation algorithm for training networks. Experimental results on 7 datasets show ANN outperformed other methods for both regression and classification across a variety of problem types and data characteristics. Limitations of ANN and areas for further research are also discussed.
This document discusses hill climbing, an optimization technique used to find the best solution to a problem. It begins by explaining hill climbing search and its implementation. It then provides examples of applying hill climbing to solve the N-Queen problem and the 8-puzzle problem. The document notes some drawbacks of hill climbing and introduces random restart hill climbing as a variation that can help overcome local maxima issues.
The document discusses artificial neural networks and the backpropagation algorithm. It provides an overview of ANN structure and learning process. Specifically, it discusses using ANN to control an autonomous vehicle, the backpropagation algorithm which aims to minimize error, and an example of an ANN learning a simple identity function.
Artificial Intelligence AI Topics History and Overviewbutest
The document discusses the history and concepts of artificial intelligence including machine learning. It provides definitions of key AI terms and describes some famous early AI programs. It also discusses machine learning methods and applications, different types of learning, and challenges in the field. Games AI is explored through techniques like min-max trees used in chess programs. The Turing Test is introduced as a proposal to measure intelligence along with proposed modifications.
Lecture 14 Heuristic Search-A star algorithmHema Kashyap
A* is a search algorithm that finds the shortest path through a graph to a goal state. It combines the best aspects of Dijkstra's algorithm and best-first search. A* uses a heuristic function to evaluate the cost of a path passing through each state to guide the search towards the lowest cost goal state. The algorithm initializes the start state, then iteratively selects the lowest cost node from its open list to expand, adding successors to the open list until it finds the goal state. A* is admissible, complete, and optimal under certain conditions relating to the heuristic function and graph structure.
The document summarizes key developments in artificial intelligence, including:
1. It describes human intelligence and the Turing test for testing machine intelligence.
2. It explains early developments in AI focused on game playing and language processing with programs like ELIZA.
3. It discusses expert systems, neural networks, vision systems, speech recognition, and knowledge representation using semantic nets.
4. It also mentions developments in hardware that supported AI and applications of intelligent robots.
Hill climbing is an optimization technique that iteratively improves the current state by evaluating possible successor states. It works by considering states laid out on a landscape, where the height corresponds to the evaluation function - it can only move to higher points. A problem is that it can get stuck at local maxima. Variants like simulated annealing allow worse states to be taken temporarily to avoid dead ends. Hill climbing has been applied to problems like the travelling salesman problem and in areas like robotics coordination and machine learning.
The document describes using a Hopfield neural network to detect moving objects in videos. The objective is to devise a method to identify differences between frames to detect movements. A Hopfield network is used because it can serve as a content addressable memory. The network consists of neurons corresponding to pixels that are connected to neighboring pixels. Difference frames are obtained and iteratively updated until the network reaches a stable minimum energy state. This allows changed and unchanged pixels to be classified. Applications include video surveillance, people tracking, and traffic monitoring.
Hill climbing is a heuristic search algorithm that starts with an initial solution and iteratively improves it by incrementally changing a single element of the solution. It selects the change that results in the greatest improvement to the solution based on an evaluation function. However, hill climbing is prone to getting stuck at local optima rather than finding the global optimum. Solutions include backtracking, making larger jumps, or applying multiple changes before evaluating.
This document presents information on Hopfield networks through a slideshow presentation. It begins with an introduction to Hopfield networks, describing them as fully connected, single layer neural networks that can perform pattern recognition. It then discusses the properties of Hopfield networks, including their symmetric weights and binary neuron outputs. The document proceeds to provide derivations of the Hopfield network model based on an additive neuron model. It concludes by discussing applications of Hopfield networks.
Prolog is a logic programming language invented in the 1970s. It uses a declarative programming paradigm where programs describe relations and logic, rather than algorithms. The document provides an introduction to Prolog, covering its key features like facts, rules, questions, terms, backtracking, recursion, and lists. It also discusses resolution and unification, depth first search, scope, type systems, and bindings in Prolog. Examples demonstrate implementing the Towers of Hanoi problem and a non-deterministic finite state automaton in Prolog.
This document discusses various heuristic search algorithms including generate-and-test, hill climbing, best-first search, problem reduction, and constraint satisfaction. Generate-and-test involves generating possible solutions and testing if they are correct. Hill climbing involves moving in the direction that improves the state based on a heuristic evaluation function. Best-first search evaluates nodes and expands the most promising node first. Problem reduction breaks problems into subproblems. Constraint satisfaction views problems as sets of constraints and aims to constrain the problem space as much as possible.
Artificial intelligence is the study and design of intelligent agents, with no single goal. It aims to put the human mind into computers by developing machines that can achieve goals through computation. The origins of AI began in the 1940s with the development of electronic computers. Significant early developments included the first stored program computer in the 1950s, the Dartmouth Conference which coined the term "artificial intelligence" in the 1950s, and the development of the LISP programming language. In the following decades, AI research expanded and led to applications in fields like expert systems, games, and military systems. While progress has been made, the full extent of intelligence and the future of AI remains unknown.
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Prolog Programming is a document about the Prolog programming language. It discusses key features of Prolog such as logical variables, unification, backtracking, defining procedures with clauses, and using Prolog as a relational database. It provides examples of Prolog code including simple programs involving terms, facts, and rules. It also covers syntax of Prolog terms and compound terms, and how Prolog interprets clauses both declaratively and procedurally.
The document discusses functions and the pigeonhole principle. It defines what a function is, how functions can be represented graphically and with tables and ordered pairs. It covers one-to-one, onto, and bijective functions. It also discusses function composition, inverse functions, and the identity function. The pigeonhole principle states that if n objects are put into m containers where n > m, then at least one container must hold more than one object. Examples are given to illustrate how to apply the principle to problems involving months, socks, and selecting numbers.
This document provides an overview of algorithms and their analysis. It begins with definitions of a computer algorithm and problem solving using computers. It then gives an example of searching an unordered array, detailing the problem, strategy, algorithm, and analysis. It introduces several tools used for algorithm analysis, including sets, logic, probability, and more.
Prolog approximates first-order logic and represents programs as sets of Horn clauses. It performs inference through resolution and searches for solutions through backtracking with unification. Prolog can represent semantic networks and frames to describe relationships between objects, classes, and their properties. It can also perform depth-first search on graphs to find paths between nodes. However, Prolog has limitations in representing higher-order relationships and probabilities.
This document provides an overview of logic programming and the logic programming language Prolog. It discusses key concepts in logic programming like predicates, clauses, resolution, and backward chaining. It also describes the basic syntax and execution model of Prolog, including how it uses unification, backtracking, and trace to evaluate queries against a knowledge base of facts and rules.
Wrong provides a general assert method that takes a predicate block. Assertion failure messages are rich in detail. The Wrong idea is to replace all those countless assert\_this, assert\_that, should\_something library methods which only exist to give a more useful failure message than "assertion failed". Wrong replaces all of them in one fell swoop, since if you can write it in Ruby, Wrong can make a sensible failure message out of it.
The document provides an introduction to the Prolog programming language. It discusses defining relations using facts and rules, recursive rules, how Prolog answers questions, and the declarative and procedural meaning of programs. Key concepts covered include using Prolog to define relations between objects, querying the relations, clauses that make up a Prolog program, and the two types of meanings for Prolog programs - declarative and procedural.
The document provides a recap of Python programming concepts like conditions and statements, while loops, for loops, break and continue statements, and working with strings. It also introduces regular expressions as a way to match patterns in strings using a formal language that can be interpreted by a regular expression processor.
[SUMMARY
This document provides an introduction to Prolog, including:
- SWI-Prolog is an open source Prolog environment that can be freely downloaded.
- Prolog is a declarative logic programming language based on logic, predicates, facts, and rules. It is often used for artificial intelligence applications.
- Key concepts in Prolog include facts, rules, queries, unification, and backtracking to find solutions. Arithmetic can also be performed.
- Control structures like cuts can be used to optimize searching for solutions and avoid unnecessary backtracking.
- Examples are provided of coding simple logic and relationships in Prolog along with queries to demonstrate how it works.
Prolog is a logic programming language well-suited for symbolic problems involving objects and relations. The document introduces Prolog concepts like defining relations using facts and rules, recursive rules, how Prolog answers questions, and the declarative and procedural meanings of programs. Key points covered include using constants and variables, different clause types, matching terms, declarative versus procedural execution, and representing lists.
This document provides an introduction to functions and their representations. It defines what a function is and gives examples of functions expressed through formulas, numerically, graphically and verbally. It also covers properties of functions including monotonicity, symmetry, even and odd functions. Key examples are used throughout to illustrate concepts.
This document provides an overview of using regular expressions (regex) in Java. It explains that regex allow you to describe and search for patterns in strings. The java.util.regex package includes the Pattern, Matcher, and PatternSyntaxException classes for working with regex in Java. The document then demonstrates various regex syntax like character classes, quantifiers, capturing groups, boundaries and more through code examples.
This document provides an introduction to Prolog programming through examples of syllogisms and modeling logic problems. It discusses SWI-Prolog, facts and rules, queries, variables, structures, predicates, programs, and modeling change. Key concepts are summarized such as backward chaining, cuts, dynamic facts, and modeling "real life" problems through games and adventure programs. The document uses a dragon adventure game to demonstrate Prolog programming concepts in action.
The WiMAX (IEEE 802.16e) standard offers peak data rates of 128Mbps downlink and
56Mbps uplink over 20MHz wide channels whilst the new standard in development, 4G
WiMAN-Advanced (802.16m) is targeting the requirements to be fully 4G using 64Q QAM,
BPSK and MIMO technologies to reach the 1Gbps rate. It is predicted that in an actual
deployment, using 4X2 MIMO in an urban microcell application using a 20 MHz TDD
channel, the 4G WiMAN-Advanced system will be able to support 120Mbps downlink and
60Mbps uplink per site concurrently. WiMAX applications are already in use in many countries
globally but research in 2010 gave results that showed only just over 350 set ups were actually
in use. Many previous WiMAX operators were found to have moved to LTE along with Yota,
who were the largest WiMAX operator in the world.
1) Propositional functions are propositions that contain variables and have no truth value until the variables are assigned values or quantified.
2) Quantifiers like "for all" (universal quantification) and "there exists" (existential quantification) are used to bind variables and give propositional functions truth values.
3) Quantification can be thought of as nested loops over variables, with universal quantification checking for truth at each value and existential checking for at least one true value.
This document provides an overview of regular expressions in Python. It discusses how regular expressions can be used to search, match, replace, and split strings. The syntax for common regular expression patterns is explained, including character classes, grouping, quantifiers, anchors, and more. Examples are given showing how to use the re module to compile patterns, search and match strings, and extract matched groups. Regular expressions in Python provide a powerful tool for manipulating text data.
Erlang is a functional programming language developed in 1986 by Joe Armstrong for developing distributed and fault-tolerant systems. It emphasizes concurrency, distribution and fault tolerance. Erlang uses lightweight processes instead of threads and supports pattern matching. Its data types include atoms, tuples, and lists. Variables start with uppercase letters while atoms start with lowercase letters. Erlang was released as open source in 1998.
Genetic algorithms are biologically inspired algorithms that use processes such as selection, crossover, and mutation to evolve solutions to problems by iteratively attempting to improve a population of candidate solutions. They work by initializing a population of random solutions and using the Darwinian principle of survival of the fittest to select the best solutions for reproduction, combining them to form new solutions. This process is repeated until an optimal solution emerges. Genetic algorithms are well-suited for large, complex search spaces as they can find optimal solutions that may not be found through other means in a reasonable amount of time.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
The document presents a taxonomy for classifying wireless network attacks. It proposes an 8 category taxonomy covering: (1) the stage of the attack, (2) effects on power consumption, (3) the OSI layer targeted, (4) security attributes utilized, (5) vulnerabilities exploited, (6) effects of the attacks, (7) precautions for each attack, and (8) the network type. The taxonomy aims to provide a simple classification system for end users to understand wireless network security requirements and emerging threats. It analyzes common wireless attacks and categorizes them according to the proposed taxonomy.
Here are the key points about standard analysis techniques:
- Count the number of basic operations like assignments, comparisons, etc. to determine time complexity.
- Analyze worst case scenario where inputs cause maximum number of operations.
- Ignore lower order terms and constant factors for asymptotic analysis. Focus on highest order term.
- Compare functions using Big-O notation which indicates how fast a function grows relative to input size.
- Be aware that even small differences in complexity orders can have huge impact on runtime as input sizes increase.
So in summary, carefully count operations, analyze worst case, use asymptotic analysis, and compare growth rates using Big-O. This allows determining how efficiently algorithms solve problems as input sizes
The document introduces fuzzy logic and its key concepts. It discusses fuzzy sets and membership functions, operators on fuzzy sets, fuzzy relations, and linguistic variables. Graphs are provided to illustrate membership functions for small and prime sets as well as examples of fuzzy relations. The goal is to explain the basic concepts and components of fuzzy logic.
The document contains monthly calendars from January 2013 through November 2014. Each calendar shows the days of the week and dates for that month. Weekends are highlighted. The calendars are arranged chronologically with the year and month labeled above each new calendar.
Fuzzy logic is a form of multi-valued logic where the truth values of variables may be any real number between 0 and 1. It deals with fuzzy sets whose elements have degrees of membership. A fuzzy set is characterized by a membership function that assigns a degree of membership to each element. Fuzzy relations are defined between fuzzy sets and composition of fuzzy relations allows inference about one fuzzy set given information about another. Fuzzy rules use max-min inference to relate fuzzy sets through a fuzzy associative matrix. This allows approximate reasoning using fuzzy logic.
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This document provides an overview of fuzzy logic and fuzzy sets. It discusses key concepts such as membership functions, operations on fuzzy sets like complement, intersection and union, properties of fuzzy sets including equality and inclusion, and alpha cuts. The document also introduces fuzzy rules and compares classical rules to fuzzy rules. Finally, it provides examples of applying concepts like support, core and complement to fuzzy sets.
2. Utility goals
• help(S)
S should be a symbolic atom, e.g.,
help(assert).
• halt
Stops Prolog, resume operating system.
• trace, notrace
Turns trace on and off, respectively.
3. Universals
• true
Always succeeds as a goal.
• fail
Always fails as a goal.
4. Loading Prolog programs
• consult(F)
Loads program from the file F. F should be bound to file
designator expression, e.g.,
F='/home/user/prolog/sample.pl', depending on the
file system.
• reconsult(F)
Like consult except that each predicate already defined
has its definition replaced by the new definition being
loaded.
• [F1,F2,...]
Bracket notation, meaning consult F1, then consult F2,
then ...
5. Testing types
• var(Term) succeeds if Term is currently uninstantiated (which therefore
has not been bound to anything, except possibly another uninstantiated
variable).
• nonvar(Term) succeeds if Term is currently instantiated (opposite of
var/1).
• atom(Term) succeeds if Term is currently instantiated to an atom.
• integer(Term) succeeds if Term is currently instantiated to an integer.
• float(Term) succeeds if Term is currently instantiated to a floating point
number.
• number(Term) succeeds if Term is currently instantiated to an integer or a
floating point number.
• atomic(Term) succeeds if Term is currently instantiated to an atom, an
integer or a floating point number.
• string(Term) Tests whether Term is bound to a string.
6. Arithmetic Predicates
• X is E Evaluate E and unify the result with X.
• X + Y When evaluated, yields the sum of X and Y.
• X - Y When evaluated, yields the difference of X and Y.
• X * Y When evaluated, yields the product of X and Y.
• X / Y When evaluated, yields the quotient of X and Y.
• X mod Y When evaluated, yields the remainder of X
divided by Y.
• X =:= Y Evaluate X and Y and compare them for
equality.
• X == Y Evaluate X and Y and succeed if they are not
equal. ...and similarly for >, <, >=, =<.
7. ?- X=2,Y=3,Z is X+Y.
X = 2,
Y = 3,
Z = 5.
?- X=3, Y=5, X =:= Y .
False
?- X=3, Y=5, X >= Y.
false.
X=3, Y=5, X =< Y.
X = 3,
Y = 5.
8. Equality of Prolog expressions
• X = Y, X =Y
Tests whether X and Y can be unified, or cannot,
respectively. For example, ?- [X,Y|R] = [a,b,c].
X = a, Y = b, R = [c]
?- [X,Y,Z] = [a,b].
No
• X ==Y, X == Y
Tests whether X and Y are currently co-bound, i.e.,
have been bound to or share same value, or not,
respectively. For example, ?- X = 3, Y = 1 * 3, X == Y.
no
?- X = a, [Y|_]= [a,b,c], X == Y.
X = a, Y = a
9. Testing for variables
• ground(G)
Tests whether G has unbound logical
variables.
• var(X)
Tests whether X is bound to a Prolog variable.
10. Database Manipulation P
• assert(X) Add X to the database. For syntactic reasons, if X
is not a base clause, use assert((X)).
• asserta(X) Add X to the database in front of other clauses
of this predicate.
• assertz(X) Add X to the database after other clauses of this
predicate.
• retract(X) Remove X from the database. For syntactic
reasons, if X is not a base clause, use retract((X)).
• abolish(F,A) Remove all clauses with functor F and arity A
from the database.
• clause(X,V) Find a clause in the database whose head (left
hand side) matches X and whose body (right hand side)
matches V. To find a base clause, use true for V.
11. Assert and Retract
• asserta(C)
Assert clause C into database above other clauses with
the same key predicate. The key predicate of a clause is
the first predicate encountered when the clause is read
from left to right.
• assertz(C), assert(C)
Assert clause C into database below other clauses with
the same key predicate.
• retract(C)
Retract C from the database. C must be sufficiently
instantiated to determine the predicate key.
12. Binding a logical variable to a numeric
value
• X is E
Binds logical variable V to the numerical value of
E. The expression E must either be a number or
be a number-valued expression, conventionally
parenthesized,
• ?- X is 22, Y is X * 3, Z is sqrt(Y).
X = 22 .
Y = 66 .
Z = 8.12404 .
• ?- X is sin(45).
X = 0.8509035245341184.
13. Control Predicates
• X ; Y X or Y. Try X first; if it fails (possibly after being backtracked
into), try Y.
• (X -> Y) If X, then try Y, otherwise fail. Y will not be backtracked into.
• (X -> Y ; Z) If X, then try Y, else try Z. X will not be backtracked into.
• not X (Sometimes written +X or not(X)) Succeed only when X fails.
• true Succeed once, but fail when backtracked into.
• repeat Always succeed, even when backtracked into.
• fail Never succeed.
• ! (Pronounced ``cut".) Acts like true, but cannot be backtracked
past, and prevents any other clauses of the predicate it occurs in
from being tried.
• abort Return immediately to the top-level Prolog prompt.
14. Negation as failure
• not(Q), +Q
Negation-as-failure, as if defined by:
• not(Q) :- call(Q), !, fail.
not(Q).
15. Prolog terms and clauses as data
• name(A,L)
Convert between atom and list.
• ?- name('pasha', P).
P = [112, 97, 115, 104, 97].
• ?- parent(a,X) = .. L.
L = [parent, a, _X001]
• ?- P=..[parent,pasha, abdul].
P= parent(pasha,abdul)
16. Associativity
• associativity (or fixity) of an operator is a
property that determines how operators of
the same precedence are grouped in the
absence of parentheses.
• Operators may be left-associative (meaning
the operations are grouped from the left),
right-associative (meaning the operations are
grouped from the right) or non-associative
(meaning there is no defined grouping).
18. Prolog operators
• :- xfx, fx
• ?- fx
• ; xfy
• , xfy
• not fy
• is, =.. , < xfx
• +, - yfx, fx
• *, / yfx
• ^ xfy
19. • :- op(P,T,O).
Declare an operator symbol. For example, with source
program ...
• :- op(500,xfx,'has_color').
a has_color red.
b has_color blue.Then ...
• ?- b has_color C.
C = red
?- What has_color red.
What = aP is precedence, an integer. Larger P has less
precedence (ability to group). Precedence number values
for built-ins depend upon the actual Prolog system. User
needs to find out what these values are. (See the reference
materials or use the help facility with keyword 'operator').
20. Finding all answers
• findall(Things,GoalExpression,Bag)
Compute all Things which satisfy the GoalExpresssion and
collect them in the list Bag. If the GoalExpression fails, Bag
will be the empty list []. findall treats all variables in
GoalExpression as if they are existentially quantified.
• bagof(Things,GoalExpression,Bag)
Compute all Things which satisfy the GoalExpresssion and
collect them in the list Bag. bagof fails if GoalExpression
fails. Free variables in GoalExpression could be
bound, yielding many bags.
• setof(Things,GoalExpression,Bag)
Compute all Things which satisfy the GoalExpresssion and
collect them in the list Bag. Similar to bagof except that Bag
will not contain duplicates and it will be sorted.
22. p(1,3,5).
p(2,4,1).
p(3,5,2).
p(4,3,1).
p(5,2,4).
?- bagof(Z,p(X,Y,Z),Bag).
X = 1, Y = 3, Bag = [5] ; The predicates bagof and setof yield
X = 2,Y = 4,Bag = [1] ; collections for individual bindings of the
X = 3,Y = 5,Bag = [2] ; free variables in the goal.
X = 4,Y = 3,Bag = [1] ; setof yields a sorted version of the
X = 5,Y = 2,Bag = [4]. collection without duplicates.
?- findall(Z,p(X,Y,Z),Bag).
Bag = [5, 1, 2, 1, 4].
23. Output Predicates
• write(X) Write the single value X to the current
output file.
• writeq(X) Write X with quotes as needed so it can
be read in again.
• tab(N) Write N blanks to the current output file.
• nl Write a newline to the current output file.
• put(X) Write the character whose ASCII value is X
to the current output file.
• tell(File) Open File as the current output file.
• told Close the current output file.
24. Input predicates
• read(X) Read one clause from the current input
and unify it with X. If there is no further input, X
is unified with end_of_file.
• get(X) Read one printing character from the
current input file and unify the ASCII code of that
character (an integer) with X.
• get0(X) Read one character from the current
input file and unify the ASCII code of that
character with X.
• see(File) Open File as the current input file.
• seen Close the current input file.
25. To avoid binding variables, use an existential quantifier
expression. For example the goal bagof(Z,X^Y^p(X,Y,Z),Bag) asks
for "the Bag of Z's such that there exists an X and there exists a Y
such that p(X,Y,Z)".
?-setof(Z,X^Y^p(X,Y,Z),Bag).
Bag = [1, 2, 4, 5].
findall acts like bagof with all free variables automatically
existentially quantified. In addition findall returns an empty list []
there is no goal satisfaction, whereas bagof fails.
?- bagof(Z,(p(X,Y,Z),Z>5),Bag).
False.
?- findall(Z,(p(X,Y,Z),Z>5),Bag).
Bag = []
26. Listing and Debugging Predicates
• listing(P)
Display predicate P. P may be a predicate name, a structure of the form
Name/Arity, or a bracked list of the above.
• trace
Turn on tracing.
• notrace
Turn off tracing.
• spy P
Turn on tracing when predicate P is called. P may be a predicate name, a structure
of the form Name/Arity, or a non-bracked list of the above.
• nospy P
Turn off spying for P.
• nospyall
Turn off all spypoints.
• Debug
Enable spypoints (allow them to initiate tracing.).
• Nodebug
Disable spypoints (without removing them).