Constraint Logic Programming

Code:

M.IA032

Acronym:

PLR

Keywords
Classification	Keyword
OFICIAL	Computer Science
OFICIAL	Informatics Engineering

Instance: 2024/2025 - 2S

Active?	No
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Artificial Intelligence

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M.IA	0	Syllabus	1	-	6	39	162

Teaching language

English

Objectives

This course addresses the Logic Programming (LP) and Constraint Programming (CP) paradigms, specifically Constraint Logic Programming (CLP).

The LP paradigm presents a declarative approach to programming, based on formal reasoning processes, more appropriate to the resolution of certain types of problems.

CLP allows for an efficient approach to constraint satisfaction problems and optimization problems, modeling them in a direct and elegant manner.

Learning outcomes and competences

At the end of this course, students should:

- Be familiar with declarative programming paradigms, namely LP and CLP.

- Identify classes of problems where LP and CLP are particularly relevant.

- Possess abstract reasoning skills and the ability to solve problems in a declarative manner.

- Be able to correctly apply LP and CLP techniques.

- Be able to build full Prolog applications, with and without constraints.

Working method

Presencial

Program

1. Logic Programming (LP)
   
   
   
   • Propositional and predicate logic. Horn clauses. Unification. Resolution.
   
   
   • Clauses. Predicates. Facts. Queries. Rules. Logic variables. Instantiation.
   
   
   • LP and databases. Recursion. Lists. Trees. Symbolic expressions.
   
   
   • Computation model. Unification. Abstract interpreter. Traces. Search trees. Negation.
   
   
   
   


2. The Prolog Language
   
   
   
   • Language Elements.
   
   
   • Execution model. Backtracking. Termination.
   
   
   • Arithmetic. Iteration. Term processing. Operators.
   
   
   • Meta- and extra-logical predicates.
   
   
   • Non-deterministic programming. Incomplete structures. Meta-interpreters. Search techniques.
   
   
   
   


3. Constraint Programming
   
   
   
   • Combinatorial problems. Mathematical, linear, and integer programming.
   
   
   • Constraints, satisfaction, propagation, and consistency maintenance.
   
   
   • Constraints in Boolean, finite and real domains.
   
   
   • Optimization. Solution search. Complete and incomplete methods.
   
   
   • Languages.
   
   
   
   


4. Constraint Logic Programming (CLP)
   
   
   
   • Modeling problems in CLP.
   
   
   • CLP using SICStus Prolog.
   
   
   
   

Mandatory literature

RISE Research Institutes of Sweden AB; SICStus Prolog User’s Manual, 2019
Apt, K.; Principles of Constraint Programming., Cambridge University Press, 2003
Sterling, L. and Shapiro, E. ; The Art of Prolog: Advanced Programming Techniques, MIT Press, 1994

Teaching methods and learning activities

Classes are used both for the presentation of the main (constraint) logic programming concepts, along with presentation and discussion of practical examples, as well as to solve programming exercises and to assist students on their practical assignments.

Software

SICStus Prolog - http://www.sics.se/sicstus/
Google OR-Tools
IBM ILOG CP Optimizer

keywords

Technological sciences > Engineering > Knowledge engineering
Physical sciences > Computer science > Programming
Physical sciences > Mathematics > Mathematical logic
Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Teste	50,00
Trabalho laboratorial	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	42,00
Trabalho laboratorial	60,00
Total:	162,00

Eligibility for exams

Enrolled students are admitted to exam if they do not exceed the allowed number of non-attendance to lab classes (maximum 25% of non-attendance).

Calculation formula of final grade

Final Grade = 50% * A + 50% * T

 

A = 50% * A1 + 50% * A2
T = 50% * T1 + 50% * T2

 

A: Final grade for both Assignments
A1: Evaluation grade for Assignment 1 (Report, source code and demo) (min. 7 values)
A2: Evaluation grade for Assignment 2 (Report, source code and demo) (min. 7 values)

 

T: Final grade for both Tests
T1: Evaluation grade for Test 1 (min. 7 values)
T2: Evaluation grade for Test 2 (min. 7 values)

 

Special assessment (TE, DA, ...)

All assessment components are required to all students. Students enrolled using special frequency modes, without obligation to attend to the practical classes, must arrange with the teachers appropriate consultation and evaluation sessions. They should also attend the scheduled evaluation points.

Classification improvement

The improvement of classification can only be obtained in the next edition of the course.