logic programming
play

Logic programming inference (interpretation) based on SLD-resolution - PowerPoint PPT Presentation

Aug 13, 2023 •750 likes •951 views

Spring 2006 IA008, Prolog logic program : a finite set of Horn clauses Logic programming inference (interpretation) based on SLD-resolution declarativness: the specification of a program is equal to the program popel@fi.muni.cz 1/18

  1. Spring 2006 IA008, Prolog � logic program : a finite set of Horn clauses Logic programming � inference (interpretation) based on SLD-resolution � declarativness: the specification of a program is equal to the program popel@fi.muni.cz 1/18

  2. Spring 2006 IA008, Prolog � implementation of a logic programming language Prolog � strategy: depth-first search in the SLD-tree � historie: in 70th. – Colmerauer, Kowalski; D.H.D. Warren (WAM) popel@fi.muni.cz 2/18

  3. Spring 2006 IA008, Prolog Syntax of Prolog I � terms (constants, variables, compound terms) � constants: Data structures � variables ( N, – 0, 123, -12, 1.0, 4.5E7, -0.12e+8 , – atoms ( ’Bob Kowalski’, [], s1, ==, ’beaver’, atom ) � compound terms: functor(name, arity), arguments VYSLEDEK, Hodnota, A1, 12 ), anonymous variable ( ) point(X,Y,Z), tree(Value,tree(LV,LL,LR),tree(RV,RL,RR)) popel@fi.muni.cz 3/18

  4. Spring 2006 IA008, Prolog Syntax of Prolog II � an ordered set of program clauses (pravidla, fakta) � variables local in a clause Program � rule: head, body � fact: a rule with an empty body (body = true ) date(D,M,Y):- day(D), month(M), year(R). � a goal: ?- date(29,’January’,2001). date(14,’February’,2001). Explicit unification: = operator Ex.: X=Y, f(g(a,X))=f(Y) popel@fi.muni.cz 4/18

  5. Spring 2006 IA008, Prolog SLD-tree for a Prolog program 1. p(X,Y) :- q(X,Z), r(Z,Y). 5. q(X,a) :- r(a,X). 9. s(X) :- t(X,X). 2. p(X,X) :- s(X). 6. r(b,a). 10. t(a,b). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. q(X,b). . . . . . . 7. s(X) :- t(X,a). . . . . . . 11. t(b,a). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. q(b,a). . . . 8. s(X) :- t(X,b). . ?- p(X,X). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 . . . . . . 5 7 . . . . . . 9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 . . . . . . 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ?- p(X,X). . ?- q(X,Z), r(Z,X). . . . ?- s(X). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 . . . 11 . . 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 2 [ X = a ℄ [ X = b ℄ [ X = a ℄ ?- r(b,X). ?- r(a,b). ?- r(a,X), r(a,X). ?- t(X,a). ?- t(X,b). ?- t(X,X). fail fail fail popel@fi.muni.cz 5/18

  6. Spring 2006 IA008, Prolog SLD-resolution for a Prolog program : num(s(s(0)) : num(X) num(0). ?- num(0). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ex.: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . num(s(X)):- num(X). . . ?- num(s(s(0))). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . : num(s(0)) : num(X) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . num(s(X)), . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X/s(0) X/s(0) : num(0) : num(0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . num(s(X)), . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . X/0 X/0 2 2 num(0) num(0) popel@fi.muni.cz 6/18

  7. Spring 2006 IA008, Prolog Example: Incompletness equal(X,Y):- equal(Y,X). 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . equal(3+2,5). . . . . . . . . . . . . . . . . 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ?- equal(3+2,5). 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ?- equal(3+2,5). 1 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ?- equal(5,3+2). 2 1 . . . . ?- equal(3+2,5). 1 2 ?- equal(5,3+2). 1 popel@fi.muni.cz 7/18

  8. Spring 2006 IA008, Prolog � recursive data structure, ordered List � functor ./2 ; pr´ � .(Head,Tail) , the notation used: [Head|Tail] , Tail is a list y seznam [] azdn´ .(a,[]) [a] [a|[]] � can be represented as a tree .(a,.(b,.(c,[]))) [a,b,c] [a,b|[c]] [a|[b,c]] [a,b,c|[]] [a|[b,c|[]]] [a|[b|[c|[]]]] popel@fi.muni.cz 8/18

  9. Spring 2006 IA008, Prolog member/2 I 1) unification: member(X,[X|_]). member(X,[_|T]):- member(X,T). ?- member(a,[b,c,a]). yes ?- member(a,[X,b,c]). X=a yes popel@fi.muni.cz 9/18

  10. Spring 2006 IA008, Prolog member/2 II 2) identity: member(X,[Y|_]):- X == Y. member(X,[_|T]):- member(X,T). ?- member(a,[X,b,c]). % No ?- member(a,[a,b,a]),write(ok),nl,fail. ok ok No 3) without a multiple occurence: member(X,[Y|_]):- X == Y. member(X,[Y|T]):- X \== Y, member(X,T). ?- member(a,[a,b,a]),write(ok),nl,fail. ok No popel@fi.muni.cz 10/18

  11. Spring 2006 IA008, Prolog Example: Append two lists append([],L,L). append([H|T1],L2,[H|T]):- append(T1,L2,T). ------------------------------------------ ?- append([a,b],[c,d],L). L = [a, b, c, d] Yes ?- append(X,[c,d],[a,b,c,d]). X = [a, b] Yes ?- append(X,Y,[a,b,c]). X = [] Y = [a, b, c]; X = [a] Y = [b, c]; X = [a, b] Y = [c]; X = [a, b, c] Y = []; No popel@fi.muni.cz 11/18

  12. Spring 2006 IA008, Prolog reverse/2 reverse([],[]). reverse([H|T],L):- reverse(T,L1), append(L1,[H],L). --------------------------------------------------- ?- reverse([a,b,c],L). L = [c, b, a] Yes ?- reverse([a,b,c],[c,b,a]). Yes ?- reverse(L,[a,b,c]). L = [c, b, a] Yes delete, permutation, prefix, postfix, sublist . . . popel@fi.muni.cz 12/18

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Logic Programming 1 Logic Programming Instead of using functions as in imperative and

Logic Programming 1 Logic Programming Instead of using functions as in imperative and

Logic Programming 1 Logic Programming Instead of using functions as in imperative and functional programs We use predicates, as in predicate calculus Interpretation = proving theorems 2 Prolog Developed at Univ. of

663 views • 7 slides
Computational Logic A Motivational Introduction 1 Computational Logic programming algorithms

Computational Logic A Motivational Introduction 1 Computational Logic programming algorithms

Computational Logic A Motivational Introduction 1 Computational Logic programming algorithms logic lambda calculus verification logic and AI logic programming knowledge representation functional programming logic of programming

475 views • 25 slides
Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category

Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category

Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category - Theoretic Framework James Lipton (Wesleyan) Categories and Logic Overview...from Tarski to Lawvere Tarski Models for many-sorted logic Let M be

1.55k views • 132 slides
Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category

Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category

Categories and Logic Programming III & IV LSV, October-Dec 2016 Logic Programming A Category - Theoretic Framework James Lipton (Wesleyan) Categories and Logic Overview...from Tarski to Lawvere Tarski Models for many-sorted logic Let M be

1.58k views • 131 slides
Introduction to Logic Programming in Prolog 1 / 39 Outline Programming paradigms Logic

Introduction to Logic Programming in Prolog 1 / 39 Outline Programming paradigms Logic

Introduction to Logic Programming in Prolog 1 / 39 Outline Programming paradigms Logic programming basics Introduction to Prolog Predicates, queries, and rules Understanding the query engine Goal search and unification Structuring recursive

881 views • 39 slides
Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical logic

Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical logic

Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical logic language based on the logic programming paradigm. Main differences with pure logic programming: more control on the execution flow,

625 views • 45 slides
Computational Logic The Prolog Programming Language 1 Prolog A practical logic language

Computational Logic The Prolog Programming Language 1 Prolog A practical logic language

Computational Logic The Prolog Programming Language 1 Prolog A practical logic language based on the logic programming paradigm. Main differences with pure logic programming: more control on the execution flow, depth-first

814 views • 39 slides
Logic programming Logic program A set of logical formulas Logic programming

Logic programming Logic program A set of logical formulas Logic programming

Logic programming Logic program A set of logical formulas Logic programming Writing formulas, "describing rules of the game" Execution of a program Searching for a result that fulfills the rules Principles of

133 views • 10 slides
Outline for today Logic Programming Infix operators/declarations Logic programming with

Outline for today Logic Programming Infix operators/declarations Logic programming with

Outline for today Logic Programming Infix operators/declarations Logic programming with constraints Finite domain constraints Lecture 9: Constraint logic programming Real/rational constraints Course review outline James Cheney

211 views • 8 slides
Computational Logic WWW Programming Using LP/CLP Systems 1 LP/CLP , the Internet, and the WWW

Computational Logic WWW Programming Using LP/CLP Systems 1 LP/CLP , the Internet, and the WWW

Computational Logic WWW Programming Using LP/CLP Systems 1 LP/CLP , the Internet, and the WWW Logic and Constraint Logic Programming can be an attractive alternative for Internet/WWW programming. Shared with other net programming tools:

822 views • 51 slides
Logic Programming Programming paradigm based on symbolic logic First order predicate calculus

Logic Programming Programming paradigm based on symbolic logic First order predicate calculus

Atkins Logic Programming Programming paradigm based on symbolic logic First order predicate calculus using constants, predicates, functions, variables, connectives, quantifiers, punctuation Start with axioms Prove theorems

196 views • 5 slides
Clojure und core.logic ...hello to the world of logic programming christian.meichsner@xelog.com

Clojure und core.logic ...hello to the world of logic programming christian.meichsner@xelog.com

Clojure und core.logic ...hello to the world of logic programming christian.meichsner@xelog.com 25. Februar 2014 Visual Index Clojure and me Dark is life, dark is death What is logic programming LISP & Clojure Primer Logic programming

523 views • 39 slides
Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical

Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical

Computational Logic The (ISO-)Prolog Programming Language 1 (ISO-)Prolog A practical programming language based on the logic programming paradigm. Main differences with pure logic programming: depth-first search rule,

869 views • 62 slides
Computational Logic Distributed/Internet Programming 1 LP/CLP , the Internet, and the WWW

Computational Logic Distributed/Internet Programming 1 LP/CLP , the Internet, and the WWW

Computational Logic Distributed/Internet Programming 1 LP/CLP , the Internet, and the WWW Can Logic and Constraint Logic Programming be an attractive alternative for Internet/WWW programming? Shared with other net programming tools:

617 views • 27 slides
Concurrent Logic Programming This lecture will cover: synchronization through shared

Concurrent Logic Programming This lecture will cover: synchronization through shared

06 25433 Logic Programming Concurrent Logic Programming This lecture will cover: synchronization through shared variables; dont care non -determinism; reactive v. transformational systems. 06 25433 Logic Programming A new

711 views • 28 slides
Categories and Logic Programming LSV, October 2016 Logic Programming A Category - Theoretic

Categories and Logic Programming LSV, October 2016 Logic Programming A Category - Theoretic

Categories and Logic Programming LSV, October 2016 Logic Programming A Category - Theoretic Framework James Lipton (Wesleyan) I Categories A category is a directed graph whose nodes are called objects and whose edges are called arrows ,

1.05k views • 93 slides
Outline Indicative Conditionals, Strictly 1 Monotonic Patterns William Starr 2 New Data 3 A

Outline Indicative Conditionals, Strictly 1 Monotonic Patterns William Starr 2 New Data 3 A

Monotonic Patterns New Data A Strict Analysis Assorted Curiosities References Outline Indicative Conditionals, Strictly 1 Monotonic Patterns William Starr 2 New Data 3 A Strict Analysis Department of Philosophy will.starr@cornell.edu

563 views • 15 slides
Causal Premise Semantics Stefan Kaufmann Northwestern / University of Connecticut Perspectives

Causal Premise Semantics Stefan Kaufmann Northwestern / University of Connecticut Perspectives

Premise Semantics Implementation Causal Premise Semantics References Causal Premise Semantics Stefan Kaufmann Northwestern / University of Connecticut Perspectives on Modality Stanford, April 12, 2013 Premise Semantics Implementation

1.5k views • 124 slides
Storage Fabric CS6453 Summary Last week: NVRAM is going to change the way we thing about

Storage Fabric CS6453 Summary Last week: NVRAM is going to change the way we thing about

Storage Fabric CS6453 Summary Last week: NVRAM is going to change the way we thing about storage. Today: Challenges of storage layers (SSDs, HDs) that are created from massive data. Slowdowns in HDs and SSDs. Enforcing policies

637 views • 31 slides
Eric Horvitz Microsoft Research CCC RISES Washington DC, Feb. 2011 Transportation Work

Eric Horvitz Microsoft Research CCC RISES Washington DC, Feb. 2011 Transportation Work

Eric Horvitz Microsoft Research CCC RISES Washington DC, Feb. 2011 Transportation Work distribution Green computing Datacenter efficiencies Energy usage forecasting, tracking, controls Tools for others ~175,000 people ~55,000 in Puget

453 views • 20 slides
What is best for spoken langage understanding: small but task-dependent embeddings or huge but

What is best for spoken langage understanding: small but task-dependent embeddings or huge but

What is best for spoken langage understanding: small but task-dependent embeddings or huge but out-of-domain embeddings Sahar Ghannay, Antoine Neuraz, Sophie Rosset 1 Goal Focus on semantic evaluation of common word embeddings

360 views • 16 slides
The Battle for England Bedes Bias The Ecclesiastical History of the English People (731)

The Battle for England Bedes Bias The Ecclesiastical History of the English People (731)

The Battle for England Bedes Bias The Ecclesiastical History of the English People (731) Third-generation Christian Oblate who barely left his monastery Romanist Anglo-Saxon Writes history as a victor Knowledge of

304 views • 15 slides
From Interval Computations Constraint-Based Set . . . to Constraint-Related Set From Main Idea

From Interval Computations Constraint-Based Set . . . to Constraint-Related Set From Main Idea

Interval Part: Outline Straightforward . . . Discussion Reason for Excess Width From Interval Computations Constraint-Based Set . . . to Constraint-Related Set From Main Idea to . . . Implementing . . . Computations: Towards Limitations of

541 views • 21 slides
Childminder Agencies Sarah Read Early Years Manager, Action for Children Agenda 10am

Childminder Agencies Sarah Read Early Years Manager, Action for Children Agenda 10am

Childminder Agencies Sarah Read Early Years Manager, Action for Children Agenda 10am Arrival 10.30am Welcome and introductions 10.45am Childminder Agencies background and update 11.30am Tea/coffee and networking 11.45am

562 views • 29 slides

More recommend