Artificial Intelligence

Code:

EIC0029

Acronym:

IART

Keywords
Classification	Keyword
OFICIAL	Artificial Intelligence

Instance: 2017/2018 - 2S

Active?	Yes
Web Page:	http://www.fe.up.pt/~eol/IA/ia1718.html
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Informatics and Computing Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIEIC	170	Syllabus since 2009/2010	3	-	6	56	162

Teaching language

Portuguese

Objectives

This course provides a set of subjects (topics) that are the core for the Intelligent System area.

Aims:

• To know what characterizes and distinguishes AI and how to apply it.
• To know how to automatically represent, acquire, manipulate and apply knowledge using Computational Algorithms and Systems.
• To develop a small project using AI techniques.

Percentual Distribution: Scientific component: 60%; Technological component: 40%

Learning outcomes and competences

At the end of the course, the student is expected to know how to automatically represent, acquire, manipulate and apply knowledge. Namely, the student should be able to:

• Know how to represent inexact knowledge.
• Compare heuristic and systematic methods in the search for solutions.
• Develop interfaces in Natural Language and inference engines for Expert Systems.
• Get acquainted with imachine learning algorithms following different paradigms ( Induction, evolucionar, neural, deep learning, etc).

 

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of object-oriented and logic-based programming.

Program

I INTRODUCTION

• Objectives
• Methodology (teaching and evaluation)
• Artificial Intelligence evolution
• Documentation

II BASIC NOTIONS

• Definitions: what is AI?
• Applications: in what domains?
• Basic notions of Agent
• Agent architectures: from Reactive to Cognitive Agents

III PROBLEM SOLVING METHODS

• Production Systems
• Control Strategies of Systematic Search
• Backward and Forward chaining
• Depth-first and Breadth-first
• Hill climbing
• Simulated Anealing
• Search by trying: backtracking
• Graph search
• “Branch and Bound” algorithm
• Heuristic search: "Best-first"
• A* algorithm and graceful decay of admissibility
• Means-Ends Analysis
• Evolutionary Computaition (Genetic Algorithms) (ML)
• Constraint satisfaction methods: relaxation principles
• Game-playing as Search Problems: Minmax, Alpha-Beta pruning
• Examples in Prolog of basic strategies: depth-first and breath-first
• Evolutionary algorithms (Genetic Algorithms)

IV INTRODUCTION TO KNOWLEDGE REPRESENTATION

• Definition of a Knowledge Representation System
• Knowledge representation structures: Production rules; Associative Networks - Frames; Scripts
• Predicate Logic and other logics
• Uncertain reasoning: Probabilistic Model (Bayes Theorem); Certainty Factors; Dempster-Schafer model; Fuzzy logic

V KNOWLEDGE ENGINEERING

• Knowledge-Based Systems
• Expert Systems: Definition; Structure; Knowledge Representation and Meta-Knowledge; Inference engine and Explanation building; Expert System cases example
• Demonstrations
• Generic Systems: "Shells"

VI INTRODUCTION TO NATURAL LANGUAGE PROCESSING

• Objectives and difficulties
• Syntactic and Semantic Analysis
• ATN; Semantic Grammars; Case Grammars
• Classical approach and the use of Logics
• Definite Clause Grammar; some examples in portuguese
• Extraposition Grammars
• Statistics-based approach to NL understanding

VII AUTOMATIC LEARNING (ML)

• Types of Automatic Learning
• Learning concepts; learning by example; learning by analogy; Explanation-Based Learning: Algorithms for EBG, mEBG e IOL; Examples.
• Inductive Learning: ID3 and C4.5, advantages and drawbacks
• Application examples

VIII INTRODUCTION TO NEURAL NETWORKS (ML)

• Basic principles (processing element; network structure; learning laws)
• Fundamental Algorithms (perceptron, back-propagation)
• Application example

 IX   Other Learning Algorithms

• Support Vector MAchines
• Deep Learning

Mandatory literature

Stuart Russell, Peter Norvig; Artificial intelligence. ISBN: 978-0-13-207148-2

Complementary Bibliography

Bratko, Ivan; Prolog programming for artificial intelligence, N. ISBN: 0-201-40375-7
J. Ross Quinlan; Programs for Machine Learning, Morgan Kaufmann Publishers, 1998
Ernesto Costa e Anabela Simões; Inteligência artificial. ISBN: 972-722-269-2
Elaine Rich, Kevin Knight; Artificial intelligence. ISBN: 0-07-100894-2
Pedro Domingos; The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books, 2015. ISBN: ISBN-13: 978-0465065707

Teaching methods and learning activities

Theoretical classes: exposition with interaction. Theoretical-practical classes: programming exercises in Prolog and project development.

Software

Prolog: SWI, Sicstus, LPA
Java, C/C++

keywords

Technological sciences > Engineering > Knowledge engineering

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Teste	15,00
Trabalho escrito	15,00
Trabalho laboratorial	20,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	50,00
Frequência das aulas	56,00
Trabalho laboratorial	56,00
Total:	162,00

Eligibility for exams

Not exceed the absence limit allowed and have a minimum of 37,5% in the distributed classification (DC)

Calculation formula of final grade

Distributed Classification (DC): weight=50%, including (% related to the distributed classification):

• Interim Report + Intercim work (15%);
• Assignment’s quality and presentation performance (40%);
• Final Report (15%);
• Evaluation during classes: mini-exams (30%)

Test/Exam (EC): weight=50% (2h30m test with consultation).

 To pass, the student must have a minimum of 37.5% in each of the two evaluation components, distributed (DC) and exam (EC).

Examinations or Special Assignments

One practical assignment and the respective report (weight=50%) and an exam (weight=50%).

To get approved, the student must have a grade equal or higher to 37,5% in each of the evaluation items.

Special assessment (TE, DA, ...)

One practical assignment and the respective report (weight=50%) and an exam (weight=50%).

To be approved, the student must have a grade equal or higher to 37,5% in each of the evaluation items.

Classification improvement

Assignment, exam or both.

Mini-exams' grades are not taken into account for this purpose. Its valuation will be added to the Exam component.