Design Patterns in Dynamic Programming
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Artificial Intelligence? 1 V2.0 © J
TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Einführung in die Künstliche Intelligenz Dozenten Prof. Johannes Fürnkranz (Knowledge Engineering) Prof. Ulf Brefeld (Knowledge Mining and Assessment) Homepage http://www.ke.informatik.tu-darmstadt.de/lehre/ki/ Termine: Dienstag 11:40-13:20 S202/C205 Donnerstag 11:40-13:20 S202/C205 3 VO + 1 UE Vorlesungen und Übungen werden in Doppelstunden abgehalten Übungen Terminplan wird auf der Web-Seite aktualisiert Ü voraussichtlich: 30.10., 13.11., 27.11., 11.12., 15.1., 29.1. Tafelübungen What is Artificial Intelligence? 1 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Text Book The course will mostly follow Stuart Russell und Peter Norvig: Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition, 2003. Deutsche Ausgabe: Stuart Russell und Peter Norvig: Künstliche Intelligenz: Ein Moderner Ansatz. Pearson- Studium, 2004. ISBN: 978-3-8273-7089-1. 3. Auflage 2012 Home-page for the book: http://aima.cs.berkeley.edu/ Course slides in English (lecture is in German) will be availabe from Home-page What is Artificial Intelligence? 2 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz What is Artificial Intelligence Different definitions due to different criteria Two dimensions: Thought processes/reasoning vs. behavior/action Success according to human standards vs. success according to an ideal concept of intelligence: rationality. Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally What is Artificial Intelligence? 3 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Definitions of Artificial Intelligence What is Artificial Intelligence? 4 V2.0 © J. -
Ece351 Lab Manual
DEREK RAYSIDE & ECE351 STAFF ECE351 LAB MANUAL UNIVERSITYOFWATERLOO 2 derek rayside & ece351 staff Copyright © 2014 Derek Rayside & ECE351 Staff Compiled March 6, 2014 acknowledgements: • Prof Paul Ward suggested that we look into something with vhdl to have synergy with ece327. • Prof Mark Aagaard, as the ece327 instructor, consulted throughout the development of this material. • Prof Patrick Lam generously shared his material from the last offering of ece251. • Zhengfang (Alex) Duanmu & Lingyun (Luke) Li [1b Elec] wrote solutions to most labs in txl. • Jiantong (David) Gao & Rui (Ray) Kong [3b Comp] wrote solutions to the vhdl labs in antlr. • Aman Muthrej and Atulan Zaman [3a Comp] wrote solutions to the vhdl labs in Parboiled. • TA’s Jon Eyolfson, Vajih Montaghami, Alireza Mortezaei, Wenzhu Man, and Mohammed Hassan. • TA Wallace Wu developed the vhdl labs. • High school students Brian Engio and Tianyu Guo drew a number of diagrams for this manual, wrote Javadoc comments for the code, and provided helpful comments on the manual. Licensed under Creative Commons Attribution-ShareAlike (CC BY-SA) version 2.5 or greater. http://creativecommons.org/licenses/by-sa/2.5/ca/ http://creativecommons.org/licenses/by-sa/3.0/ Contents 0 Overview 9 Compiler Concepts: call stack, heap 0.1 How the Labs Fit Together . 9 Programming Concepts: version control, push, pull, merge, SSH keys, IDE, 0.2 Learning Progressions . 11 debugger, objects, pointers 0.3 How this project compares to CS241, the text book, etc. 13 0.4 Student work load . 14 0.5 How this course compares to MIT 6.035 .......... 15 0.6 Where do I learn more? . -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
Backpropagation with Callbacks
Backpropagation with Continuation Callbacks: Foundations for Efficient and Expressive Differentiable Programming Fei Wang James Decker Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN 47906 [email protected] [email protected] Xilun Wu Grégory Essertel Tiark Rompf Purdue University Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN, 47906 West Lafayette, IN, 47906 [email protected] [email protected] [email protected] Abstract Training of deep learning models depends on gradient descent and end-to-end differentiation. Under the slogan of differentiable programming, there is an increas- ing demand for efficient automatic gradient computation for emerging network architectures that incorporate dynamic control flow, especially in NLP. In this paper we propose an implementation of backpropagation using functions with callbacks, where the forward pass is executed as a sequence of function calls, and the backward pass as a corresponding sequence of function returns. A key realization is that this technique of chaining callbacks is well known in the programming languages community as continuation-passing style (CPS). Any program can be converted to this form using standard techniques, and hence, any program can be mechanically converted to compute gradients. Our approach achieves the same flexibility as other reverse-mode automatic differ- entiation (AD) techniques, but it can be implemented without any auxiliary data structures besides the function call stack, and it can easily be combined with graph construction and native code generation techniques through forms of multi-stage programming, leading to a highly efficient implementation that combines the per- formance benefits of define-then-run software frameworks such as TensorFlow with the expressiveness of define-by-run frameworks such as PyTorch. -
Design Pattern Interview Questions
DDEESSIIGGNN PPAATTTTEERRNN -- IINNTTEERRVVIIEEWW QQUUEESSTTIIOONNSS http://www.tutorialspoint.com/design_pattern/design_pattern_interview_questions.htm Copyright © tutorialspoint.com Dear readers, these Design Pattern Interview Questions have been designed specially to get you acquainted with the nature of questions you may encounter during your interview for the subject of Design Pattern. As per my experience good interviewers hardly plan to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer: What are Design Patterns? Design patterns represent the best practices used by experienced object-oriented software developers. Design patterns are solutions to general problems that software developers faced during software development. These solutions were obtained by trial and error by numerous software developers over quite a substantial period of time. What is Gang of Four GOF? In 1994, four authors Erich Gamma, Richard Helm, Ralph Johnson and John Vlissides published a book titled Design Patterns - Elements of Reusable Object-Oriented Software which initiated the concept of Design Pattern in Software development. These authors are collectively known as Gang of Four GOF. Name types of Design Patterns? Design patterns can be classified in three categories: Creational, Structural and Behavioral patterns. Creational Patterns - These design patterns provide a way to create objects while hiding the creation logic, rather than instantiating objects directly using new opreator. This gives program more flexibility in deciding which objects need to be created for a given use case. Structural Patterns - These design patterns concern class and object composition. Concept of inheritance is used to compose interfaces and define ways to compose objects to obtain new functionalities. -
Stuart Russell and Peter Norvig, Artijcial Intelligence: a Modem Approach *
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Artificial Intelligence ELSEVIER Artificial Intelligence 82 ( 1996) 369-380 Book Review Stuart Russell and Peter Norvig, Artijcial Intelligence: A Modem Approach * Nils J. Nilsson Robotics Laboratory, Department of Computer Science, Stanford University, Stanford, CA 94305, USA 1. Introductory remarks I am obliged to begin this review by confessing a conflict of interest: I am a founding director and a stockholder of a publishing company that competes with the publisher of this book, and I am in the process of writing another textbook on AI. What if Russell and Norvig’s book turns out to be outstanding? Well, it did! Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and thorough! End of review? No; we will go on for some pages-although not for as many as did Russell and Norvig. In their Preface (p. vii), the authors mention five distinguishing features of their book: Unified presentation of the field, Intelligent agent design, Comprehensive and up-to-date coverage, Equal emphasis on theory and practice, and Understanding through implementation. These features do indeed distinguish the book. I begin by making a few brief, summary comments using the authors’ own criteria as a guide. l Unified presentation of the field and Intelligent agent design. I have previously observed that just as Los Angeles has been called “twelve suburbs in search of a city”, AI might be called “twelve topics in search of a subject”. -
Behavioral Patterns
Behavioral Patterns 101 What are Behavioral Patterns ! " Describe algorithms, assignment of responsibility, and interactions between objects (behavioral relationships) ! " Behavioral class patterns use inheritence to distribute behavior ! " Behavioral object patterns use composition ! " General example: ! " Model-view-controller in UI application ! " Iterating over a collection of objects ! " Comparable interface in Java !" 2003 - 2007 DevelopIntelligence List of Structural Patterns ! " Class scope pattern: ! " Interpreter ! " Template Method ! " Object scope patterns: ! " Chain of Responsibility ! " Command ! " Iterator ! " Mediator ! " Memento ! " Observer ! " State ! " Strategy ! " Visitor !" 2003 - 2007 DevelopIntelligence CoR Pattern Description ! " Intent: Avoid coupling the sender of a request to its receiver by giving more than one object a chance to handle the request. Chain the receiving objects and pass the request along the chain until an object handles it. ! " AKA: Handle/Body ! " Motivation: User Interfaces function as a result of user interactions, known as events. Events can be handled by a component, a container, or the operating system. In the end, the event handling should be decoupled from the component. ! " Applicability: ! " more than one object may handle a request, and the handler isn't known a priori. ! " Want to issue a request to one of several objects without specifying the receiver !" 2003 - 2007 DevelopIntelligence CoR Real World Example ! " The Chain of Responsibility pattern avoids coupling the sender of a request to the receiver, by giving more than one object a chance to handle the request. ! " Mechanical coin sorting banks use the Chain of Responsibility. Rather than having a separate slot for each coin denomination coupled with receptacle for the denomination, a single slot is used. When the coin is dropped, the coin is routed to the appropriate receptacle by the mechanical mechanisms within the bank. -
Design Patterns (Part 3)
Design patterns (part 3) CSE 331 University of Washington Michael Ernst Outline Introduction to design patterns Creational patterns (constructing objects) Structural patterns (controlling heap layout) ⇒Behavioral patterns (affecting object semantics) Composite pattern Composite permits a client to manipulate either an atomic unit or a collection of units in the same way Good for dealing with part-whole relationships Composite example: Bicycle • Bicycle – Wheel • Skewer • Hub • Spokes • Nipples • Rim • Tape • Tube • Tire – Frame – Drivetrain – ... Methods on components class BicycleComponent { int weight (); float cost (); } class Skewer extends BicycleComponent { float price ; float cost () { return price; } } class Wheel extends BicycleComponent { float assemblyCost ; Skewer skewer ; Hub hub ; ... float cost () { return assemblyCost + skewer.cost() + hub.cost() + ...; } } Composite example: Libraries Library Section (for a given genre) Shelf Volume Page Column Word Letter interface Text { String getText (); } class Page implements Text { String getText () { ... return the concatenation of the column texts ... } } Traversing composites Goal: perform operations on all parts of a composite Representing Java code x = foo * b + c / d; = x + * / foo b c d Abstract syntax tree (AST) for Java code class PlusOp extends Expression { // + operation Expression leftExp ; Expression rightExp ; } class VarRef extends Expression { // variable reference String varname ; } class EqualOp extends Expression { // equality test a==b; Expression lvalue ; // -
The Dartmouth College Artificial Intelligence Conference: the Next
AI Magazine Volume 27 Number 4 (2006) (© AAAI) Reports continued this earlier work because he became convinced that advances The Dartmouth College could be made with other approaches using computers. Minsky expressed the concern that too many in AI today Artificial Intelligence try to do what is popular and publish only successes. He argued that AI can never be a science until it publishes what fails as well as what succeeds. Conference: Oliver Selfridge highlighted the im- portance of many related areas of re- search before and after the 1956 sum- The Next mer project that helped to propel AI as a field. The development of improved languages and machines was essential. Fifty Years He offered tribute to many early pio- neering activities such as J. C. R. Lick- leiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with James Moor perceptrons. Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Some of the best notes describing the AI project were taken by More, al- though ironically he admitted that he ■ The Dartmouth College Artificial Intelli- Marvin Minsky, Claude Shannon, and never liked the use of “artificial” or gence Conference: The Next 50 Years Nathaniel Rochester for the 1956 “intelligence” as terms for the field. (AI@50) took place July 13–15, 2006. The event, McCarthy wanted, as he ex- Ray Solomonoff said he went to the conference had three objectives: to cele- plained at AI@50, “to nail the flag to brate the Dartmouth Summer Research summer project hoping to convince the mast.” McCarthy is credited for Project, which occurred in 1956; to as- everyone of the importance of ma- coining the phrase “artificial intelli- sess how far AI has progressed; and to chine learning. -
Automatic Generation of Truffle-Based Interpreters for Domain
Automatic generation of Truffle-based interpreters for Domain-Specific Languages Manuel Leduc, Gwendal Jouneaux, Thomas Degueule, Gurvan Le Guernic, Olivier Barais, Benoit Combemale To cite this version: Manuel Leduc, Gwendal Jouneaux, Thomas Degueule, Gurvan Le Guernic, Olivier Barais, et al.. Automatic generation of Truffle-based interpreters for Domain-Specific Languages. The Journal of Object Technology, Chair of Software Engineering, 2020, 19 (2), pp.1-21. 10.5381/jot.2020.19.2.a1. hal-02395867v2 HAL Id: hal-02395867 https://hal.inria.fr/hal-02395867v2 Submitted on 21 Feb 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Journal of Object Technology Published by AITO — Association Internationale pour les Technologies Objets http://www.jot.fm/ Automatic generation of Truffle-based interpreters for Domain-Specific Languages Manuel Leduca Gwendal Jouneauxa Thomas Degueuleb Gurvan Le Guernicc Olivier Baraisa Benoit Combemalead a. Univ. Rennes, Inria, CNRS, IRISA, France b. Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, F-33400 Talence, France c. DGA MI, Bruz, France d. Univ. Toulouse, IRIT, France Abstract Numerous language workbenches have been proposed over the past decade to ease the definition of Domain-Specific Languages (DSLs). -
Artificial Intelligence: a Modern Approach (3Rd Edition)
[PDF] Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig - pdf download free book Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks Stuart Russell, Peter Norvig, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, Read Best Book Online Artificial Intelligence: A Modern Approach (3rd Edition), Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Full Popular Stuart Russell, Peter Norvig, I Was So Mad Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig Ebook Download, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, full book Artificial Intelligence: A Modern Approach (3rd Edition), online pdf Artificial Intelligence: A Modern Approach (3rd Edition), Download Free Artificial Intelligence: A Modern Approach (3rd Edition) Book, pdf Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (3rd Edition), the book Artificial Intelligence: A Modern Approach (3rd Edition), Download Artificial Intelligence: A Modern Approach (3rd Edition) E-Books, Read Artificial Intelligence: A Modern Approach (3rd Edition) Book Free, Artificial Intelligence: A Modern Approach (3rd Edition) PDF read online, Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks, Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Free Download, Artificial Intelligence: A Modern Approach (3rd Edition) Books Online, Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Books [E-BOOK] Artificial Intelligence: A Modern Approach (3rd Edition) Full eBook, CLICK HERE FOR DOWNLOAD The dorian pencil novels at the end of each chapter featured charts. makes us the meaning of how our trip and unk goes into trouble with others like land. -
Artificial Intelligence
Outline: Introduction to AI Artificial Intelligence N-ways Introduction Introduction to AI - Personal Information and - What is Intelligence? Introduction Background - An Intelligent Entity - The Age of Intelligent Machines Course Outline: - Definitions of AI - Requirements and Expectation - Behaviourist’s View on Intelligent - Module Assessment Machines - Recommended Books - Turing’s Test - Part 1 & 2 - Layout of Course (14 lessons) - History of AI Lecture 1 - Examples of AI systems - Office Hours Course Delivery Methods General Reference for the Course Goals and Objectives of module 2 Course Outline: General Reference for the Course Recommended Books Artificial Intelligence by Patrick Henry Winston AI related information. Whatis.com (Computer Science Dictionary) Logical Foundations of Artificial Intelligence by Michael R. http://whatis.com/search/whatisquery.html Genesereth, Nils J. Nislsson, Nils J. Nilsson General computer-related news sources. Technology Encyclopedia Artificial Intelligence, Luger, Stubblefield http://www.techweb.com/encyclopedia/ General Information Artificial Intelligence : A Modern Approach by Stuart J. Russell, Technology issues. Peter Norvig Computing Dictionary http://wombat.doc.ic.ac.uk/ Artificial Intelligence by Elaine Rich, Kevin Knight (good for All web links in my AI logic, knowledge representation, and search only) website. Webster Dictionary http://work.ucsd.edu:5141/cgi- bin/http_webster 3 4 Goals and Objectives of module Introduction to AI: What is Intelligence? Understand motivation, mechanisms, and potential Intelligence, taken as a whole, consists of of Artificial Intelligence techniques. the following skills:- Balance of breadth of techniques with depth of 1. the ability to reason understanding. 2. the ability to acquire and apply knowledge Conversant with the applicational scope and 3. the ability to manipulate and solution methodologies of AI.