Exercises on the Fundamentals of Prolog

Size: px
Start display at page:

Download "Exercises on the Fundamentals of Prolog"

Transcription

1 1 Introduction Exercises on the Fundamentals of Prolog These exercises are intended to help reinforce material taught in the lectures of CIS335 course in Prolog. They do not contribute any marks to the total for that module, and they are meant to be self-assessed. This work should take around 6 hours, including the self-assessment. No material is to be handed in. The work is not meant to be particularly difficult, but merely to remind you of what we have covered in the lectures don t be surprised if the answers are obvious! On the other hand, don t be downhearted if you find some of the material hard to follow there s plenty of time yet. If you have problems with the material, please feel free to discuss it with fellow students, or with me (g.wiggins@gold.ac.uk). Your labs are based around this material, so you will be able to discuss it there too. Your feedback is welcome, and if you feel the exercises could be made more useful, please let me know how. 2 Prolog Syntax Identify the errors in the following Prolog program syntax. If you are unsure, type your answers into a file to see if they load correctly. NB these are syntax errors, not logical program errors. % Problem 2.1 simple( program ) :-. % Problem 2.2 simple( Program ) :- Small( Program ). % Problem 2.3 simple( program1, X ) :- X = 2, simple( program2, X ) :- X = 1. % Problem 2.4 simple( simple program ). % Problem 2.5 simple( %program% ). % Problem 2.6 6thprogram( program ) :- too many( Errors ). 1

2 % Problem 2.7 simple program :- program(simple) % Problem 2.8 simple program :- program(simple). 3 List Processing The two example predicates append/3 and flatten/2 can be found in examples for practical2.pl on the Web in the course s prolog code directory. 3.1 Joining lists together Here is a program which takes two lists and sticks them end-to-end, to make a third: % append( List1, List2, BothLists ) append( [], List, List ). append( [Head Tail], Rest, [Head All] ) :- append( Tail, Rest, All ). Think about how the program works, and then answer the following questions (in your head not on the computer!). Remember that some queries may return more than one answer work out what all the possibilities are, and what order they appear in. Then check it out on the computer. % Problem 3.1.1?- append( [a], [b], [c] ). % Problem 3.1.2?- append( [a], [b], X ). % Problem 3.1.3?- append( [a], [], X ). % Problem 3.1.4?- append( [a], b, X ). % Problem 3.1.5?- append( [a], X, Y ). % Problem 3.1.6?- append( X, [b,c], Y ). 2

3 % Problem 3.1.7?- append( X, Y, [a,b,c] ). 3.2 Unifying lists What answers (including any substitutions) are produced by the following queries? % Problem 3.2.1?- [A B] = [a,a]. % Problem 3.2.2?- [A,b,C D] = [d,c,b,a]. % Problem 3.2.3?- [a,[a []]] = X. % Problem 3.2.4?- [A,g,d,A] = [x,d,g,z]. 3.3 Building an answer Here is a program with a programmer error in it. It is an error that appears commonly in code writen by beginning Prolog students. The program is supposed to take a list of pairs of items (eg pair( X, Y )) and flatten them out into a list of items. So the list should come out as [pair( a, b ),pair( c, d )] [a,b,c,d] The programmer types in the code, runs it with the query shown, and the answer is no. Why? (Hint: think about how unification works how many values can a variable be unified with at once?) 3

4 % Problem % append( List1, List2, BothLists ) append( [], List, List ). append( [Head Tail], Rest, [Head All] ) :- append( Tail, Rest, All ). % flatten( ListOfPairs, List ). flatten( [], [] ). % deal with empty lists flatten( [pair( A, B ) Pairs], Ans ) :- flatten( Pairs, Ans ), append( [A, B], Ans, Ans ). % And here s the query?- flatten( [pair( a, b ), pair( c, d )], Answer ). How would you correct the code? There is one choice of input for which the incorrect program would produce the correct answer. What is it? How many times will it be produced? To check, try running the program with the query?- flatten( A, B ). (Note: look carefully at how this program uses the list to control recursion. The two clauses represent two mutually exclusive cases where the input is an empty list and where it is a nonempty list.) 4 Unification This, the main section of the practical, is aimed understanding unification. The idea is to build a basic unification algorithm. This is really quite hard, so do not be dismayed if you find it tricky. Feel free to ask for help from me, the lab demonstrator or your fellow students. Those who don t have problems remember that explaining things to others is a valuable part of the learning process. We are going to use a special representation for variables, so that we don t have to worry about what is a Prolog variable and what is one of our own variables. So we use in an operator, +, to mark a variable, and we make its name a Prolog constant, like this: +variable note the lower case v. We will also use a special notation for terms, like this: functor-[term1,term2,...] so a term with no arguments is written noargs-[]; and we will restrict ourselves to symbols with only lower case letters in them. Choosing these notations will make life very much simpler. We want a predicate called unify/3 which will take two terms, and try to unify them. If it succeeds, the third argument will be a list of binary unifiers, which are pairs like this: u( +a, v-[] ) 4

5 which (in this example) means that variable a has been unified with term v. You can find a file called unification partial.pl on the CIS335 module web page. It contains some of the code needed for this assignment. You are required to add in the parts which are missing; to do this, you will need to understand all the code which is present. Missing parts are marked /* MISSING N */ where N is a number between 1 and 10 inclusive; fill in the missing parts in order of the numbers. Make up around 10 goals to test your program, deciding in advance what the answers should be. Remember to check that you don t get too many answers, and that non-unifiable terms don t unify! Here are some example questions and answers:?- unify( f-[+x,+y], f-[+y,+x], B ). B = [u(+(x),+(y))]??- unify( f-[g-[h-[],+y],+x], f-[+x,g-[+a,+a]], B ). B = [u(+(y),+(a)),u(+(a),h-[]),u(+(x),g-[h-[],+(y)])]??- unify( f-[], +x, B ). B = [u(+(x),f-[])]??- unify( f-[+x,+y], f-[g-[],+x], B ). B = [u(+(y),+(x)),u(+(x),g-[])]??- unify( f1-[], f2-[], B ). no?- Feel free to queries to me. 5

Advanced Prolog Programming

Advanced Prolog Programming 1 Introduction CIS335, Assignment 4 Advanced Prolog Programming Geraint Wiggins November 12, 2004 This practical is the second self-assessed exercise for MSc students on the Prolog module. It is intended

More information

Week 5 Tutorial Structural Induction

Week 5 Tutorial Structural Induction Department of Computer Science, Australian National University COMP2600 / COMP6260 Formal Methods in Software Engineering Semester 2, 2016 Week 5 Tutorial Structural Induction You should hand in attempts

More information

Announcements. The current topic: Scheme. Review: BST functions. Review: Representing trees in Scheme. Reminder: Lab 2 is due on Monday at 10:30 am.

Announcements. The current topic: Scheme. Review: BST functions. Review: Representing trees in Scheme. Reminder: Lab 2 is due on Monday at 10:30 am. The current topic: Scheme! Introduction! Object-oriented programming: Python Functional programming: Scheme! Introduction! Numeric operators, REPL, quotes, functions, conditionals! Function examples, helper

More information

COMP2411 Lecture 20: Logic Programming Examples. (This material not in the book)

COMP2411 Lecture 20: Logic Programming Examples. (This material not in the book) COMP2411 Lecture 20: Logic Programming Examples (This material not in the book) There are several distinct but often equivalent ways to think about logic programs 1. As computing logical consequences of

More information

Discussion 4. Data Abstraction and Sequences

Discussion 4. Data Abstraction and Sequences Discussion 4 Data Abstraction and Sequences Data Abstraction: The idea of data abstraction is to conceal the representation of some data and to instead reveal a standard interface that is more aligned

More information

3 Lists. List Operations (I)

3 Lists. List Operations (I) 3 Lists. List Operations (I) The list is the simplest yet the most useful Prolog structure. A list is a sequence of any number of objects. Example 3.1: L = [1, 2, 3], R = [a, b, c], T = [john, marry, tim,

More information

Processing lists in Prolog - 2

Processing lists in Prolog - 2 Processing lists in Prolog - 2 This lecture shows that techniques introduced before (analysing terminating conditions and recursive programming) can be used to develop more complex procedures. This lecture

More information

6.001 Notes: Section 6.1

6.001 Notes: Section 6.1 6.001 Notes: Section 6.1 Slide 6.1.1 When we first starting talking about Scheme expressions, you may recall we said that (almost) every Scheme expression had three components, a syntax (legal ways of

More information

COP4020 Programming Languages. Logical programming with Prolog Prof. Xin Yuan

COP4020 Programming Languages. Logical programming with Prolog Prof. Xin Yuan COP4020 Programming Languages Logical programming with Prolog Prof. Xin Yuan Topics Logic programming with Prolog COP4020 Spring 2013 2 Definitions: Prolog Terms Terms are symbolic expressions that are

More information

Sets 1. The things in a set are called the elements of it. If x is an element of the set S, we say

Sets 1. The things in a set are called the elements of it. If x is an element of the set S, we say Sets 1 Where does mathematics start? What are the ideas which come first, in a logical sense, and form the foundation for everything else? Can we get a very small number of basic ideas? Can we reduce it

More information

An Interesting Way to Combine Numbers

An Interesting Way to Combine Numbers An Interesting Way to Combine Numbers Joshua Zucker and Tom Davis October 12, 2016 Abstract This exercise can be used for middle school students and older. The original problem seems almost impossibly

More information

Difference lists in Prolog

Difference lists in Prolog tmcs-csenki 2010/4/12 23:38 page 73 #1 8/1 (2010), 73 87 Difference lists in Prolog Attila Csenki Abstract. Prolog is taught at Bradford University within the two-semester module Symbolic and Declarative

More information

CS 360: Programming Languages Lecture 10: Logic Programming with Prolog

CS 360: Programming Languages Lecture 10: Logic Programming with Prolog CS 360: Programming Languages Lecture 10: Logic Programming with Prolog Geoffrey Mainland Drexel University Section 1 Administrivia Midterm Tuesday Midterm is Tuesday, February 14! Study guide is on the

More information

The current topic: Prolog. Announcements. Meaning of a Prolog rule. Prolog syntax. Reminder: The deadline for Lab 2 re-mark requests is Friday.

The current topic: Prolog. Announcements. Meaning of a Prolog rule. Prolog syntax. Reminder: The deadline for Lab 2 re-mark requests is Friday. The current topic: Prolog! Introduction! Object-oriented programming: Python! Functional programming: Scheme! Python GUI programming (Tkinter)! Types and values Logic programming: Prolog! Introduction

More information

Recursion, Structures, and Lists

Recursion, Structures, and Lists Recursion, Structures, and Lists Artificial Intelligence Programming in Prolog Lecturer: Tim Smith Lecture 4 04/10/04 30/09/04 AIPP Lecture 3: Recursion, Structures, and Lists 1 The central ideas of Prolog

More information

Lecture 9: A closer look at terms

Lecture 9: A closer look at terms Lecture 9: A closer look at terms Theory Introduce the == predicate Take a closer look at term structure Introduce strings in Prolog Introduce operators Exercises Exercises of LPN: 9.1, 9.2, 9.3, 9.4,

More information

Prolog. Intro to Logic Programming

Prolog. Intro to Logic Programming Prolog Logic programming (declarative) Goals and subgoals Prolog Syntax Database example rule order, subgoal order, argument invertibility, backtracking model of execution, negation by failure, variables

More information

60-265: Winter ANSWERS Exercise 4 Combinational Circuit Design

60-265: Winter ANSWERS Exercise 4 Combinational Circuit Design 60-265: Winter 2010 Computer Architecture I: Digital Design ANSWERS Exercise 4 Combinational Circuit Design Question 1. One-bit Comparator [ 1 mark ] Consider two 1-bit inputs, A and B. If we assume that

More information

Prolog - 3 Prolog search trees + traces

Prolog - 3 Prolog search trees + traces Prolog - 3 Prolog search trees + traces 1 Review member(a, [A B]). member(a, [B C]) :- member (A,C). goal-oriented semantics: can get value assignment for goal member(a,[b C]) by showing truth of subgoal

More information

About these slides Prolog programming hints

About these slides Prolog programming hints About these slides Prolog programming hints COMP9414-2008 Semester 1 Ronnie Taib (ronniet@cse.unsw.edu.au)! Practical notes on using Prolog! Some hints for better code! LECTURE NOTES ALWAYS PREVAIL!! The

More information

6.001 Notes: Section 15.1

6.001 Notes: Section 15.1 6.001 Notes: Section 15.1 Slide 15.1.1 Our goal over the next few lectures is to build an interpreter, which in a very basic sense is the ultimate in programming, since doing so will allow us to define

More information

1 of 5 5/11/2006 12:10 AM CS 61A Spring 2006 Midterm 2 solutions 1. Box and pointer. Note: Please draw actual boxes, as in the book and the lectures, not XX and X/ as in these ASCII-art solutions. Also,

More information

Fundamentals of Prolog

Fundamentals of Prolog Fundamentals of Prolog Prof. Geraint A. Wiggins Centre for Cognition, Computation and Culture Goldsmiths College, University of London Contents Summary of Lecture 1 What makes a good Prolog program? What

More information

Programming Languages and Techniques (CIS120)

Programming Languages and Techniques (CIS120) Programming Languages and Techniques (CIS120) Lecture 3 September 5, 2018 Value-Oriented Programming (continued) Lists and Recursion CIS 120 Announcements Homework 1: OCaml Finger Exercises Due: Tuesday

More information

Lecture 9. Exercises. Theory. Solutions to exercises LPN 8.1 & 8.2. Patrick Blackburn, Johan Bos & Kristina Striegnitz

Lecture 9. Exercises. Theory. Solutions to exercises LPN 8.1 & 8.2. Patrick Blackburn, Johan Bos & Kristina Striegnitz Lecture 9 Exercises Solutions to exercises LPN 8.1 & 8.2 Theory Solution to Exercise 8.1 Suppose we add the noun ``men'' (which is plural) and the verb ``shoot''. Then we would want a DCG which says that

More information

CMSC 331 Final Exam Section 0201 December 18, 2000

CMSC 331 Final Exam Section 0201 December 18, 2000 CMSC 331 Final Exam Section 0201 December 18, 2000 Name: Student ID#: You will have two hours to complete this closed book exam. We reserve the right to assign partial credit, and to deduct points for

More information

Logic-Oriented Programming (5/11/2004)

Logic-Oriented Programming (5/11/2004) 1 Logic-Oriented Programming (5/11/2004) Daniel P. Friedman, David W. Mack, William E. Byrd Computer Science Department, Indiana University Bloomington, IN 47405, USA Oleg Kiselyov Fleet Numerical Meteorology

More information

Wan Hussain Wan Ishak

Wan Hussain Wan Ishak September 1 st Session 2014/2015 (A141) Wan Hussain Wan Ishak School of Computing UUM College of Arts and Sciences Universiti Utara Malaysia (P) 04-9285150 (E) hussain@uum.edu.my (U) http://wanhussain.com

More information

Logic Programming. Efficiency Issues. Temur Kutsia

Logic Programming. Efficiency Issues. Temur Kutsia Logic Programming Efficiency Issues Temur Kutsia Research Institute for Symbolic Computation Johannes Kepler University of Linz, Austria kutsia@risc.uni-linz.ac.at Efficiency Issues in Prolog Narrow the

More information

Computational Psycholinguistics. Tutorials. Garance PARIS. Tutorials. Winter Semester 2011/2012 1/18

Computational Psycholinguistics. Tutorials. Garance PARIS. Tutorials. Winter Semester 2011/2012 1/18 Winter Semester 2011/2012 1/18 Roadmap Theories of sentence processing: modularity, parsing strategies, information sources, reanalysis Symbolic parsing models: incremental parsing, ambiguity resolution,

More information

Programming Languages 3. Definition and Proof by Induction

Programming Languages 3. Definition and Proof by Induction Programming Languages 3. Definition and Proof by Induction Shin-Cheng Mu Oct. 22, 2015 Total Functional Programming The next few lectures concerns inductive definitions and proofs of datatypes and programs.

More information

An Explicit Continuation Evaluator for Scheme

An Explicit Continuation Evaluator for Scheme Massachusetts Institute of Technology Course Notes 2 6.844, Spring 05: Computability Theory of and with Scheme February 17 Prof. Albert R. Meyer revised March 3, 2005, 1265 minutes An Explicit Continuation

More information

Prolog (cont d) Remark. Using multiple clauses. Intelligent Systems and HCI D7023E

Prolog (cont d) Remark. Using multiple clauses. Intelligent Systems and HCI D7023E Intelligent Systems and HCI D703E Lecture : More Prolog Paweł Pietrzak Prolog (cont d) 1 Remark The recent version of SWI- Prolog displays true and false rather than and no Using multiple clauses Different

More information

Lists. Michael P. Fourman. February 2, 2010

Lists. Michael P. Fourman. February 2, 2010 Lists Michael P. Fourman February 2, 2010 1 Introduction The list is a fundamental datatype in most functional languages. ML is no exception; list is a built-in ML type constructor. However, to introduce

More information

We ve done. Now. Next

We ve done. Now. Next We ve done Matroid Theory Task scheduling problem (another matroid example) Dijkstra s algorithm (another greedy example) Dynamic Programming Now Matrix Chain Multiplication Longest Common Subsequence

More information

SOFTWARE ENGINEERING DESIGN I

SOFTWARE ENGINEERING DESIGN I 2 SOFTWARE ENGINEERING DESIGN I 3. Schemas and Theories The aim of this course is to learn how to write formal specifications of computer systems, using classical logic. The key descriptional technique

More information

This lecture covers: Prolog s execution strategy explained more precisely. Revision of the elementary Prolog data types

This lecture covers: Prolog s execution strategy explained more precisely. Revision of the elementary Prolog data types This lecture covers: Prolog s execution strategy explained more precisely The execution strategy has been presented as simply placing all matching clauses onto the stack. In fact, Prolog is slightly more

More information

Tests, Backtracking, and Recursion

Tests, Backtracking, and Recursion Tests, Backtracking, and Recursion Artificial Intelligence Programming in Prolog Lecture 3 30/09/04 30/09/04 AIPP Lecture 3: Rules, Results, and Backtracking 1 Re-cap A Prolog program consists of predicate

More information

Reasoning About Programs Panagiotis Manolios

Reasoning About Programs Panagiotis Manolios Reasoning About Programs Panagiotis Manolios Northeastern University April 2, 2016 Version: 95 Copyright c 2016 by Panagiotis Manolios All rights reserved. We hereby grant permission for this publication

More information

Reasoning About Programs Panagiotis Manolios

Reasoning About Programs Panagiotis Manolios Reasoning About Programs Panagiotis Manolios Northeastern University March 1, 2017 Version: 101 Copyright c 2017 by Panagiotis Manolios All rights reserved. We hereby grant permission for this publication

More information

Derived from PROgramming in LOGic (1972) Prolog and LISP - two most popular AI languages. Prolog programs based on predicate logic using Horn clauses

Derived from PROgramming in LOGic (1972) Prolog and LISP - two most popular AI languages. Prolog programs based on predicate logic using Horn clauses Prolog Programming Derived from PROgramming in LOGic (1972) Good at expressing logical relationships between concepts Prolog and LISP - two most popular AI languages Execution of a Prolog program is a

More information

Prolog-2 nd Lecture. Prolog Predicate - Box Model

Prolog-2 nd Lecture. Prolog Predicate - Box Model Prolog-2 nd Lecture Tracing in Prolog Procedural interpretation of execution Box model of Prolog predicate rule How to follow a Prolog trace? Trees in Prolog use nested terms Unification Informally Formal

More information

n! = 1 * 2 * 3 * 4 * * (n-1) * n

n! = 1 * 2 * 3 * 4 * * (n-1) * n The Beauty and Joy of Computing 1 Lab Exercise 9: Problem self-similarity and recursion Objectives By completing this lab exercise, you should learn to Recognize simple self-similar problems which are

More information

CS115 - Module 10 - General Trees

CS115 - Module 10 - General Trees Fall 2017 Reminder: if you have not already, ensure you: Read How to Design Programs, Sections 15 and 16. Arithmetic Expressions Recall with binary trees we could represent an expression containing binary

More information

2.2.2.Relational Database concept

2.2.2.Relational Database concept Foreign key:- is a field (or collection of fields) in one table that uniquely identifies a row of another table. In simpler words, the foreign key is defined in a second table, but it refers to the primary

More information

Reasoning About Programs Panagiotis Manolios

Reasoning About Programs Panagiotis Manolios Reasoning About Programs Panagiotis Manolios Northeastern University February 26, 2017 Version: 100 Copyright c 2017 by Panagiotis Manolios All rights reserved. We hereby grant permission for this publication

More information

Agenda. CS301 Session 20. A logic programming trick. A simple Prolog program. Introduction to logic programming Examples Semantics

Agenda. CS301 Session 20. A logic programming trick. A simple Prolog program. Introduction to logic programming Examples Semantics CS301 Session 20 Introduction to logic programming Examples Semantics Agenda 1 2 A logic programming trick A two-way translator in two lines of code: translate([],[]). translate([word Words],[Mot Mots])

More information

The object level in Prolog. Meta-level predicates and operators. Contents. The flow of computation. The meta level in Prolog

The object level in Prolog. Meta-level predicates and operators. Contents. The flow of computation. The meta level in Prolog Lecture 8 Meta-level predicates and operators Contents Object level vs. meta level Controlling flow of computation Checking and dismantling expressions Comparison operators The object level in Prolog Prolog

More information

Introduction to Prolog Paper Refs. Prolog tutor. Julian Verdurmen. Seminar Softw. tech. for teaching and learning. September 30, 2009

Introduction to Prolog Paper Refs. Prolog tutor. Julian Verdurmen. Seminar Softw. tech. for teaching and learning. September 30, 2009 Seminar Softw. tech. for teaching and learning September 30, 2009 Outline 1 Introduction to Prolog Basics Simple example 2 Basics Simple example Outline 1 Introduction to Prolog Basics Simple example 2

More information

Topic B: Backtracking and Lists

Topic B: Backtracking and Lists Topic B: Backtracking and Lists 1 Recommended Exercises and Readings From Programming in Prolog (5 th Ed.) Readings: Chapter 3 2 Searching for the Answer In order for a Prolog program to report the correct

More information

Objectives: 1- Bolean Algebra. Eng. Ayman Metwali

Objectives: 1- Bolean Algebra. Eng. Ayman Metwali Objectives: Chapter 3 : 1- Boolean Algebra Boolean Expressions Boolean Identities Simplification of Boolean Expressions Complements Representing Boolean Functions 2- Logic gates 3- Digital Components 4-

More information

l Determine if a number is odd or even l Determine if a number/character is in a range - 1 to 10 (inclusive) - between a and z (inclusive)

l Determine if a number is odd or even l Determine if a number/character is in a range - 1 to 10 (inclusive) - between a and z (inclusive) Final Exam Exercises Chapters 1-7 + 11 Write C++ code to: l Determine if a number is odd or even CS 2308 Fall 2016 Jill Seaman l Determine if a number/character is in a range - 1 to 10 (inclusive) - between

More information

This Lecture. We will first introduce some basic set theory before we do counting. Basic Definitions. Operations on Sets.

This Lecture. We will first introduce some basic set theory before we do counting. Basic Definitions. Operations on Sets. Sets A B C This Lecture We will first introduce some basic set theory before we do counting. Basic Definitions Operations on Sets Set Identities Defining Sets Definition: A set is an unordered collection

More information

GOALS [HTDP/2e Chapters 1 through 3.5]

GOALS [HTDP/2e Chapters 1 through 3.5] Lab 1 GOALS [HTDP/2e Chapters 1 through 3.5] This week's lab will help you to practice: Using Dr.Racket: Interactions vs. Definitions; Stepper; Error messages; Indentation Simple image functions; using

More information

Lecture 3: Recursion; Structural Induction

Lecture 3: Recursion; Structural Induction 15-150 Lecture 3: Recursion; Structural Induction Lecture by Dan Licata January 24, 2012 Today, we are going to talk about one of the most important ideas in functional programming, structural recursion

More information

Math 187 Sample Test II Questions

Math 187 Sample Test II Questions Math 187 Sample Test II Questions Dr. Holmes October 2, 2008 These are sample questions of kinds which might appear on Test II. There is no guarantee that all questions on the test will look like these!

More information

Building a system for symbolic differentiation

Building a system for symbolic differentiation Computer Science 21b Structure and Interpretation of Computer Programs Building a system for symbolic differentiation Selectors, constructors and predicates: (constant? e) Is e a constant? (variable? e)

More information

a little more on macros sort of like functions, but..

a little more on macros sort of like functions, but.. a little more on macros 1 sort of like functions, but.. one of the most interesting but tricky aspects of Lisp. unlike functions, macros don't evaluate their arguments; they compute on unevaluated expressions

More information

A Walkthrough of the Learning Aids

A Walkthrough of the Learning Aids x Walkthrough A Walkthrough of the Learning Aids The pedagogical elements in this book work together to focus on and reinforce key concepts and fundamental principles of programming, with additional tips

More information

UNIVERSITY OF TORONTO AT MISSISSAUGA April 2006 Examination CSC 324H5 S Instructor: Richard Krueger Duration 3 hours No Aids Allowed

UNIVERSITY OF TORONTO AT MISSISSAUGA April 2006 Examination CSC 324H5 S Instructor: Richard Krueger Duration 3 hours No Aids Allowed UNIVERSITY OF TORONTO AT MISSISSAUGA April 2006 Examination CSC 324H5 S Instructor: Richard Krueger Duration 3 hours No Aids Allowed Student Number: Last (Family) Name(s): First (Given) Name(s): SOLUTIONS

More information

Map coloring example

Map coloring example Map coloring example A B C D E F Database for map coloring coloring(a,b,c,d,e,f) :- different(a,b), different(a,c), different(a,d), different(a,f), different(b,c), different(b,e), different(c,d), different(c,e),

More information

Homework 6: Higher-Order Procedures Due: 11:59 PM, Oct 16, 2018

Homework 6: Higher-Order Procedures Due: 11:59 PM, Oct 16, 2018 Integrated Introduction to Computer Science Klein Homework 6: Higher-Order Procedures Due: 11:59 PM, Oct 16, 2018 Contents 1 Fun with map (Practice) 2 2 Unfold (Practice) 3 3 Map2 3 4 Fold 4 5 All You

More information

CSCE 314 Programming Languages

CSCE 314 Programming Languages CSCE 314 Programming Languages Haskell: Higher-order Functions Dr. Hyunyoung Lee 1 Higher-order Functions A function is called higher-order if it takes a function as an argument or returns a function as

More information

Plan of the lecture. G53RDB: Theory of Relational Databases Lecture 1. Textbook. Practicalities: assessment. Aims and objectives of the course

Plan of the lecture. G53RDB: Theory of Relational Databases Lecture 1. Textbook. Practicalities: assessment. Aims and objectives of the course Plan of the lecture G53RDB: Theory of Relational Databases Lecture 1 Practicalities Aims and objectives of the course Plan of the course Relational model: what are relations, some terminology Relational

More information

Programming Language Concepts, cs2104 Lecture 01 ( )

Programming Language Concepts, cs2104 Lecture 01 ( ) Programming Language Concepts, cs2104 Lecture 01 (2003-08-15) Seif Haridi Department of Computer Science, NUS haridi@comp.nus.edu.sg 2002-08-15 S. Haridi, CS2104, L01 (slides: C. Schulte, S. Haridi) 1

More information

Shell CSCE 314 TAMU. Higher Order Functions

Shell CSCE 314 TAMU. Higher Order Functions 1 CSCE 314: Programming Languages Dr. Dylan Shell Higher Order Functions 2 Higher-order Functions A function is called higher-order if it takes a function as an argument or returns a function as a result.

More information

Lecture 6: More Lists

Lecture 6: More Lists Lecture 6: More Lists Theory Define append/3, a predicate for concatenating two lists, and illustrate what can be done with it Discuss two ways of reversing a list A naïve way using append/3 A more efficient

More information

CS61A Notes 02b Fake Plastic Trees. 2. (cons ((1 a) (2 o)) (3 g)) 3. (list ((1 a) (2 o)) (3 g)) 4. (append ((1 a) (2 o)) (3 g))

CS61A Notes 02b Fake Plastic Trees. 2. (cons ((1 a) (2 o)) (3 g)) 3. (list ((1 a) (2 o)) (3 g)) 4. (append ((1 a) (2 o)) (3 g)) CS61A Notes 02b Fake Plastic Trees Box and Pointer Diagrams QUESTIONS: Evaluate the following, and draw a box-and-pointer diagram for each. (Hint: It may be easier to draw the box-and-pointer diagram first.)

More information

Intro. Scheme Basics. scm> 5 5. scm>

Intro. Scheme Basics. scm> 5 5. scm> Intro Let s take some time to talk about LISP. It stands for LISt Processing a way of coding using only lists! It sounds pretty radical, and it is. There are lots of cool things to know about LISP; if

More information

LING/C SC/PSYC 438/538. Lecture 20 Sandiway Fong

LING/C SC/PSYC 438/538. Lecture 20 Sandiway Fong LING/C SC/PSYC 438/538 Lecture 20 Sandiway Fong Today's Topics SWI-Prolog installed? We will start to write grammars today Quick Homework 8 SWI Prolog Cheatsheet At the prompt?- 1. halt. 2. listing. listing(name).

More information

Difference Between Dates Case Study 2002 M. J. Clancy and M. C. Linn

Difference Between Dates Case Study 2002 M. J. Clancy and M. C. Linn Difference Between Dates Case Study 2002 M. J. Clancy and M. C. Linn Problem Write and test a Scheme program to compute how many days are spanned by two given days. The program will include a procedure

More information

Lecture 7: Primitive Recursion is Turing Computable. Michael Beeson

Lecture 7: Primitive Recursion is Turing Computable. Michael Beeson Lecture 7: Primitive Recursion is Turing Computable Michael Beeson Closure under composition Let f and g be Turing computable. Let h(x) = f(g(x)). Then h is Turing computable. Similarly if h(x) = f(g 1

More information

Combining Lists & Built-in Predicates

Combining Lists & Built-in Predicates Combining Lists & Built-in Predicates Artificial Intelligence Programming in Prolog Lecturer: Tim Smith Lecture 6 11/10/04 11/10/04 AIPP Lecture 6: Built-in Predicates 1 Collecting Results Last time we

More information

Haske k ll An introduction to Functional functional programming using Haskell Purely Lazy Example: QuickSort in Java Example: QuickSort in Haskell

Haske k ll An introduction to Functional functional programming using Haskell Purely Lazy Example: QuickSort in Java Example: QuickSort in Haskell Haskell An introduction to functional programming using Haskell Anders Møller amoeller@cs.au.dk The most popular purely functional, lazy programming language Functional programming language : a program

More information

Excel Basics Fall 2016

Excel Basics Fall 2016 If you have never worked with Excel, it can be a little confusing at first. When you open Excel, you are faced with various toolbars and menus and a big, empty grid. So what do you do with it? The great

More information

Baby Steps Toward an Implementation of Axiomatic Language

Baby Steps Toward an Implementation of Axiomatic Language Baby Steps Toward an Implementation of Axiomatic Language Extended Abstract Walter W. Wilson Lockheed Martin, P.O. Box 748, Fort Worth TX 76101, USA wwwilson@acm.org Abstract. This paper describes an initial

More information

Prolog lecture 5. Data structures Difference lists Appendless append

Prolog lecture 5. Data structures Difference lists Appendless append Prolog lecture 5 Data structures Difference lists Appendless append Appending two Lists Predicate definition is elegantly simple: append([],l,l). append([x T],L,[X R]) :- append(t,l,r). Run-time performance

More information

Module 1 Topic C Lesson 14 Reflections

Module 1 Topic C Lesson 14 Reflections Geometry Module 1 Topic C Lesson 14 Reflections The purpose of lesson 14 is for students to identify the properties of reflection, to use constructions to find line of reflection, get familiar with notations

More information

COMPUTER SCIENCE TRIPOS

COMPUTER SCIENCE TRIPOS CST.2011.3.1 COMPUTER SCIENCE TRIPOS Part IB Monday 6 June 2011 1.30 to 4.30 COMPUTER SCIENCE Paper 3 Answer five questions. Submit the answers in five separate bundles, each with its own cover sheet.

More information

An introduction introduction to functional functional programming programming using usin Haskell

An introduction introduction to functional functional programming programming using usin Haskell An introduction to functional programming using Haskell Anders Møller amoeller@cs.au.dkau Haskell The most popular p purely functional, lazy programming g language Functional programming language : a program

More information

/99/$ IEEE

/99/$ IEEE A Multiparadigm Language Approach to Teaching Principles of Programming Languages D. Suzanne Westbrook Computer Science and Electrical Engineering Northern Arizona University Flagstaff, AZ 86011 Abstract

More information

Problem Set 3 CMSC 426 Due: Thursday, April 3

Problem Set 3 CMSC 426 Due: Thursday, April 3 Problem Set 3 CMSC 426 Due: Thursday, April 3 Overview: This problem set will work a little different. There will be a standard part, and an advanced part. Students may choose to do either part. Alternately,

More information

RACKET BASICS, ORDER OF EVALUATION, RECURSION 1

RACKET BASICS, ORDER OF EVALUATION, RECURSION 1 RACKET BASICS, ORDER OF EVALUATION, RECURSION 1 COMPUTER SCIENCE 61AS 1. What is functional programming? Give an example of a function below: Functional Programming In functional programming, you do not

More information

Homework: More Abstraction, Trees, and Lists

Homework: More Abstraction, Trees, and Lists Homework: More Abstraction, Trees, and Lists COMP 50 Fall 2013 This homework is due at 11:59PM on Monday, November 18. Submit your solutions in a single file using the COMP 50 Handin button on DrRacket;

More information

Module 5: Lists. Readings: HtDP, Sections 9, 10.

Module 5: Lists. Readings: HtDP, Sections 9, 10. Module 5: Lists Readings: HtDP, Sections 9, 10. Lists are the main tool used in Racket to work with unbounded data. As with conditional expressions and structures, the data definition for lists leads naturally

More information

Prolog Programming. Lecture Module 8

Prolog Programming. Lecture Module 8 Prolog Programming Lecture Module 8 Prolog Language Prolog is unique in its ability to infer facts from the given facts and rules. In Prolog, an order of clauses in the program and goals in the body of

More information

Partitioning for Better Synthesis Results

Partitioning for Better Synthesis Results 3 Partitioning for Better Synthesis Results Learning Objectives After completing this lab, you should be able to: Use the group and ungroup commands to repartition a design within Design Analyzer Analyze

More information

CS 321 Programming Languages and Compilers. Prolog

CS 321 Programming Languages and Compilers. Prolog CS 321 Programming Languages and Compilers Prolog Prolog PROgramming LOGic Algorithm = Logic + Control Logic programming deals with computing relations rather than functions. To understand Prolog one must

More information

Logic Programming and Resolution Lecture notes for INF3170/4171

Logic Programming and Resolution Lecture notes for INF3170/4171 Logic Programming and Resolution Lecture notes for INF3170/4171 Leif Harald Karlsen Autumn 2015 1 Introduction This note will explain the connection between logic and computer programming using Horn Clauses

More information

Module 7. Knowledge Representation and Logic (Rule based Systems) Version 2 CSE IIT, Kharagpur

Module 7. Knowledge Representation and Logic (Rule based Systems) Version 2 CSE IIT, Kharagpur Module 7 Knowledge Representation and Logic (Rule based Systems) Lesson 18 Rule based Systems - II 7.2.5 Programs in PROLOG These minimal notes on Prolog show only some of its flavor. Here are facts plays(ann,fido).

More information

CPSC 230 Extra review and solutions

CPSC 230 Extra review and solutions Extra review questions: the following questions are meant to provide you with some extra practice so you need to actually try them on your own to get anything out of it. For that reason, solutions won't

More information

INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 6

INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 6 INTRODUCTION TO ALGEBRAIC GEOMETRY, CLASS 6 RAVI VAKIL Contents 1. Loose ends 1 2. Playing around with the structure sheaf of the plane 2 3. Defining affine varieties and prevarieties 3 At the end, I collected

More information

Previously. Iteration. Date and time structures. Modularisation.

Previously. Iteration. Date and time structures. Modularisation. 2017 2018 Previously Iteration. Date and time structures. Modularisation. Today File handling. Reading and writing files. In order for a program to work with a file, the program must create a file object

More information

Research Report AI A Numerical Equation Solver in Prolog Michael A. Covington Artificial Intelligence Programs The University of Georgia

Research Report AI A Numerical Equation Solver in Prolog Michael A. Covington Artificial Intelligence Programs The University of Georgia Research Report AI 1989 02 A Numerical Equation Solver in Prolog Michael A. Covington Artificial Intelligence Programs The University of Georgia Athens, Georgia 30602 U.S.A. A Numerical Equation Solver

More information

Solutions to Homework 10

Solutions to Homework 10 CS/Math 240: Intro to Discrete Math 5/3/20 Instructor: Dieter van Melkebeek Solutions to Homework 0 Problem There were five different languages in Problem 4 of Homework 9. The Language D 0 Recall that

More information

Fundamental mathematical techniques reviewed: Mathematical induction Recursion. Typically taught in courses such as Calculus and Discrete Mathematics.

Fundamental mathematical techniques reviewed: Mathematical induction Recursion. Typically taught in courses such as Calculus and Discrete Mathematics. Fundamental mathematical techniques reviewed: Mathematical induction Recursion Typically taught in courses such as Calculus and Discrete Mathematics. Techniques introduced: Divide-and-Conquer Algorithms

More information

CS 104 (Spring 2014) Final Exam 05/09/2014

CS 104 (Spring 2014) Final Exam 05/09/2014 CS 104 (Spring 2014) Final Exam 05/09/2014 G o o d L u c k Your Name, USC username, and Student ID: This exam has 8 pages and 8 questions. If yours does not, please contact us immediately. Please read

More information

Prolog Introduction. Gunnar Gotshalks PI-1

Prolog Introduction. Gunnar Gotshalks PI-1 Prolog Introduction PI-1 Physical Symbol System Hypothesis A physical symbol system has the necessary and sufficient means for general intelligent action. Allen Newell and Herbert A. Simon PI-2 Physical

More information

SIMPLIFYING Judo Math Inc.

SIMPLIFYING Judo Math Inc. SIMPLIFYING 2013 Judo Math Inc. 6 th grade Ratios and Expressions Discipline: Black Belt Training Order of Mastery: Simplifying Expressions (6EE3-4) 1. Terms and combining like terms 2. Order of Operations

More information

Putting the fun in functional programming

Putting the fun in functional programming CM20167 Topic 4: Map, Lambda, Filter Guy McCusker 1W2.1 Outline 1 Introduction to higher-order functions 2 Map 3 Lambda 4 Filter Guy McCusker (1W2.1 CM20167 Topic 4 2 / 42 Putting the fun in functional

More information