Artificial Intelligence

Code:

EIC0029

Acronym:

IART

Keywords
Classification	Keyword
OFICIAL	Artificial Intelligence

Instance: 2007/2008 - 2S

Active?	Yes
Web Page:	http://www.fe.up.pt/~eol/IA/ia0708.html
Responsible unit:	Informatics Section
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
LEIC	0	Plano de estudos de transição para 2006/07	3	-	6	56	162
MIEIC	133	Syllabus since 2006/2007	3	-	6	56	162

Teaching language

Portuguese

Objectives

To know what characterizes and distinguishes AI and how to apply it.
To understand the notion of computational agent.
To know how to automatically represent, acquire, manipulate and apply knowledge:
-How to represent inexact knowledge.
-To compare heuristic and systematic methods in the search for solutions.
- To apply a logic-based language:
--To develop interfaces in Natural Language and inference engines for Expert Systems.
-To get acquainted with inductive and deductive learning algorithms.
-To develop a small project using Prolog and AI techniques.

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
Search by trying: backtracking; graph search
“Branch and Bound” algorithm
Heuristic search: "Best-first"
A* algorithm and graceful decay of admissibility
Means-Ends Analysis
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

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; Certainty Factors; Dempster- Schafer model; Fuzzy logic
Logic: revision of the Resolution procedure and Unification algorithm

V KNOWLEDGE ENGINEERING
Knowledge-Based Systems
Expert Systems: Definition; Structure; Knowledge Representation and Meta-Knowledge; Inference engine and Explanation building;
Expert System cases example: ORBI; SMYCIN; ARCA
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

VII AUTOMATIC LEARNING
Types of 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
Basic principles (processing element; network structure; learning laws)
Fundamental Algorithms (perceptron, back-propagation)
Application example

Mandatory literature

Stuart Russel and Peter Norvig; Artificial Intelligence: A Modern Appraoch, Prentice-Hall, 2nd Ed., 2003

Complementary Bibliography

Bratko, Ivan; Prolog programming for artificial intelligence, N. ISBN: 0-201-40375-7
Ernesto Costa, Anabela Simões; Inteligência Artificial, FCA, 2004
E. Rich; K. Knight; Artificial Intelligence, MCGraw-Hill

Teaching methods and learning activities

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

Software

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

keywords

Technological sciences > Engineering > Knowledge engineering

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Subject Classes	Participação presencial	56,00
	Trabalho escrito	50,00
	Total:	-	0,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
	Estudo autónomo	50
	Total:	50,00

Eligibility for exams

- Frequency evaluation: weight=50% ; minimum=3,75 (maximum 10) marks including (% related to the frequency evaluation):
Assignment’s quality and presentation performance (50%);
Final Report (20%);
Intercalary Report (20%);
Evaluation during classes (10%).

Calculation formula of final grade

- Frequency evaluation: weight=50% (10 marks); minimum=3,375marks including (% related to the frequency evaluation):
Assignment’s quality and presentation performance (50%);
Final Report (20%);
Intercalary Report (20%);
Evaluation during classes (10%).

- Test/ Exam: weight=50%; minimum= 3,3 marks
(2h30m test with consultation).

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, re-sit exam or both.

Observations

Pre-requirements:
Knowledge of object-oriented and logic-based programming.