Métodos de Computação Inteligentes 2005.1
General
information:
§
Lecturer: Jacques Robin
§ Teaching assistants: Fábio Moura, Jairson Vitorino & Marco Aurélio da Silva
§
When:
3a
§
Where:
M1
§
This
page: www.cin.ufpe.br/~in1006/2005/
§
Newsgroup:
depto.courses.posgrad.in1006
Bibliography:
§
Artificial
Intelligence a Modern Approach (2nd Ed.), S. Russell & P. Norvig, 2002,
Prentice-Hall. Site: aima.cs.berkeley.edu
§
Introduction
to Multi-Agent Systems, M. Wooldridge, 2002, Wiley. Site: www.csc.liv.ac.uk/~mjw/pubs/imas/
§
UML 2
Toolkit. Eriksson, H.E., Penker, M.,
§
The
Object Constraint Language: Getting Your Models Ready for MDA (2nd Ed.) Warmer,
J. & Kleppe, A. 2003. Addison-Wesley.
§
Logic,
Programming and Prolog (2nd Ed). Nilsson, U. & Maluszynski. 2000. Site: http://www.ida.liu.se/~ulfni/lpp/
§
Constraint
Programming: an Introduction. Marriott, K. & Stuckey, P. 1998. MIT Press.
§
Essentials
of Constraint Programming. Frühwirth, T. & Abdennhader, S. 2003. Springer.
§
Constraint
Processing. Dechter, R. Morgan-Kaufmann. 2003.
§
Ontologies:
a Silver Bullet for Knowledge Management and Electronic Commerce. Fensel, D.
2003. Springer.
Evaluation:
§
Individual multiple-answer final exam: 30% of the grade
§
Oral seminar: 20% of the grade
§
Group project: 50% of the grade
§
10% for each stage deliverable
§
10% for final revised and assembled deliverables
§
Each deliverable:
§ 6% for artifact (model
or code) quality
§ 4% for report quality
Grades
breakdown:
§
Oral seminar:
§
Meeting first advising deadline: 1 point
§
Conciseness: 1 point
§
Breadth completeness: 1 point
§
Depth completeness: 1 point
§
Correctness: 1 point
§
Command/understanding of material: 1 point
§
Clarity: 1 point
§
Examples: 1 point
§
Oral skills: 1 point
§
Questions and interactions with students: 1 point
§
Project deliverable presentation:
§
Presentation itself:
§
Command/understanding of material: 1 point
§
Design choice motivation: 1 point
§
Clarity: 1 point
§
Oral skills: 1 point
§
For Models:
§
Breath completeness: 1 point
§
Depth completeness: 1 point
§
Correctness: 1 point
§
Design quality: 1 point
§
Conciseness: 1 point
§
Explanations in written report: 1 point
§
For Code:
§
Breadth of functionalities: 1 point
§
Functional tests and robustness: 1 point
§
Performance tests and efficiency: 1 point
§
Interface and user-friendliness: 1 point
§
Modularity and reusability: 1 point
§
Documentation and comments: 1 point
Seminar
advising:
§
Two compulsory meeting with adviser: the first two weeks before seminar’s date, the
second one week before.
§
For the first meeting, the student have ready a detailed outline of the
seminar that indicates the title and planned content of each slide
§
For the second meeting, the student must have the presentation ready to
rehearse it with the adviser
§
Failure to meet the first deadline will result in loosing one points
from the seminar’s grade
§
Failure to meet the second deadline will result in the cancellation of
the seminar, with the adviser presenting the lecture and the student getting the grade zero for the
seminar
Roster:
|
Aluno |
Pesquisa |
Conhecimento Prévio |
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|
Login |
Curso |
Área |
Orientador |
Tema |
Java |
UML |
OCL |
Lógica |
Disciplinas de IA já cursadas
|
|
Alexandre Maciel |
amam |
M |
Comp. Int. |
Edson Carvalho |
|
+ |
+ |
- |
+ |
IA |
|
Alexsandro Farias |
ajmf |
M |
Comp. Int. |
Jacques Robin |
|
+ |
+ |
- |
+ |
IA |
|
Daniela Cargnin |
dc2 |
M |
Comp. Int. |
Germano
Vasconcelos |
|
- |
+ |
- |
+ |
IA |
|
Eduardo Dominoni |
ecgd |
M |
Agentes Autônomos |
Patrícia Tedesco |
|
+ |
+ |
- |
+ |
Sistemas
Inteligentes, Agentes Autônomos, |
|
Gláucya Boechat |
gcb |
M |
Comp. Int. |
Edson Carvalho |
|
+ |
+ |
- |
+ |
IA |
|
Humberto Brandão |
hcbo |
M |
|
Germano Vasconcelos |
|
+ |
+ |
- |
+ |
IA, Otimização Comb., Recup. Informação |
|
Jeneffer Ferreira |
jcf |
M |
Comp. Int. |
Edson Carvalho |
|
+ |
+ |
- |
+ |
IA |
|
João Farias |
jpf2 |
Isolada |
|
|
|
+ |
+ |
- |
+ |
IA |
|
João Paulo Rolim |
jpcr |
M |
Arquitetura de Comp. |
Sérgio Calvacante |
|
+ |
+ |
- |
+ |
Fundamento de |
|
José Alexandrino |
jla |
M |
Comp. Int. |
Edson Carvalho |
|
+ |
+ |
- |
+ |
IA |
|
Leandro Almeida |
lma3 |
M |
Comp. Int. |
Teresa Ludermir |
|
+ |
+ |
- |
+ |
IA |
|
Luiz Francisco Lacerda |
lfblj |
M |
Comp. Int. |
Jacques Robin |
Refactoring do Replay
– MaracatuRFC |
+ |
+ |
- |
+/- |
Fundamentos de |
|
Luiz Gustavo
Carvalho |
lgcc |
M |
Comp. Int. |
Edson Carvalho |
|
+ |
+ |
- |
+ |
IA |
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Marcio Carvalho |
mrl2 |
M |
Comp. Int. |
Teresa Ludermir |
Otimização de Redes Neurais |
+ |
+ |
- |
+ |
IA |
|
Tarcisio Gurgel |
tbg |
M |
Mineração de Dados |
Paulo Adeodato |
Mineração de Dados Aplicada à Epidemiologia |
+ |
+ |
- |
+ |
Fundamento de IA |
Unit 1:
Introduction to Intelligent Agents
Lecture 0:
Course Overview 19/04
§
Jairson
§
Outline:
this page
Lecture 1:
Artificial Intelligence and Intelligent Agents 26/04
§
Jairson
§
Sections
1, 2.1, 2.2, 7.2 of Russell & Norvig
§
Chapters
1, 2 of Wooldridge
§
Outline:
§
What
is artificial intelligence?
§
What
is an agent?
§
What
is an intelligent agent?
§
Applications
of intelligent agents
§
Slides:
IntelligentAgents.ppt
Lecture 2:
Agent Environments and Architectures 28/04
§
Fábio
§
Sections
2.3-2.4 of Russell & Norvig
§
Chapter
5 of Wooldridge
§
Outline:
§
Classifying
dimensions of agent environments
§
Internal
architectures of agents
§
Slides:
AgentEnvironmentsArchitectures.ppt
Lecture 3:
Knowledge-Based Agents 03/05
§
Jairson
§
Sections
7.1, 8.4 & 10.1 of Russell & Norvig
§
Learning,
Bayesian Probability, Graphical Models and Abduction: http://www.cs.ubc.ca/spider/poole/papers/indab.pdf
§
Outline:
§
Inference
engines and declarative knowledge bases
§
Classifying
dimensions of knowledge base elements
§
Commitments
of knowledge representation languages
§
Automated
reasoning tasks
§ Monotonic deduction
§ Belief revision
§ Constraint solving
§ Optimization
§ Abduction
§ Inheritance
§ Induction
§ Analogy
§
Internal
architecture of knowledge-based agents
§
Knowledge
acquisition
§
Slides:
KnowledgeBasedAgent.ppt
Unit 2:
Search
Lecture
4: Problem Solving through Search 05/05
§
João Fárias advised by Jairson
§
Outline:
§
Agent reasoning as navigating a space of possibilities
§
Exhaustive search strategies
§
Avoiding repeated states
§
Searching with partial information
§
Slides: ExhaustiveSearch.ppt
Lecture
5: Heuristic Search Algorithms 10/05
§
Humberto César Brandão advised by Jairson
§
Outline:
§
Heuristic global search
§
Domain-dependent heuristic function design
§
Local search
§
Online search
Slides: HeuristicSearch.ppt
Lecture 6:
Constraint Satisfaction Search 12/05
§
Leandro
Maciel advised by Jairson
§
Section
5 of Russell & Norvig
§
Sections
6.1, 6.2 and 6.4 of Dechter
§
Outline:
§
Constraint
Satisfaction Problems
§
Exhaustive
Global CSP search
§
Domain-independent
heuristics for global CSP search
§
Local
CSP search
Slides: CSP.ppt
Unit 3: Knowledge
Representation
Lecture
7: Object-Oriented Knowledge
Representation 17/05
§
Fábio
§
Sections
10.2, 10.6 of Russell & Norvig
§
Sections
1, 2, 4 and pp. 145-172 of Eriksson & al.
§
Outline:
§
Overview
of object-oriented knowledge representation
§
Semantic
networks
§
Frames
§
Overview
of UML
§
UML
class diagrams
§
UML
object diagrams
§
UML
activity diagrams
§
Slides:
OOKRUML.ppt
Lecture 8: Ontologies
and Logical Constraints on Object-Oriented Knowledge 19/05
§
Fábio
§
Section
10.1 of Russell & Norvig
§
Section
2 of Fensel
§
Sections
1.3-1.5, 3.1-3.3, 3.6, 6.1-6.5, 6.7, 7-10 of Warmer & Kleppe
§
Outline:
§
What
is an ontology?
§ Minimal definition
§ Purposes and origins of ontologies
§ Elements and diversity of ontologies
§
Overview
of OCL
§
OCL to
adorn UML class diagrams
§ OCL expressions
§ OCL basic types
§ OCL enumerations and collections
§
OCL to
adorn UML activity diagrams
§
Slides:
OntologiesOCL.ppt
Lecture
9: UML and OCL Knowledge Representation Tools 24/05
§
Fábio
§
Outline:
§
Class diagrams with Rational Rose
§
Activity diagrams with Rational Rose
§
OCL constraints with Poseidon
§
Slides: UMLOCLTools.ppt
Lecture
10: Project Topics 31/05
§
Jairson
§
Outline:
§
Divide students in two halves: one for the programming project and one
for the modeling project
§
Programming project: one large team
§
Modeling project: two teams of equal size
§
Modeling teams project: UML and OCL ontology of selected topics presented in lectures and in the
reading material
§
Programming team project: Java implementation of a selected topics presented in lectures and in
the reading material (fewer topics than for the modeling teams project)
§
Both projects:
§ Divided in four stages
§ Partial deliverable at the end of
each stage
§ Final overall result correcting each
deliverable and assembling them together
Unit 4:
Logical Knowledge Formalization and Automated Reasoning
Lecture 11:
Rule-Based Constraint Programming 02/06
§
Jairson
§
Chapter
7 of Frühwirth & Abdennadher
§
Chapters
2 of Marriott & Stuckey
§
Chapter
5 of
§
Outline:
§
Limitations
of CSP
§
Simplification,
Propagation, Optimization and Implication
§
Overview
of Constraint Handling Rules (CHR)
§
CHR
syntax
§
CHR
declarative logical semantics
§
CHR
operational semantics
§
CHRD:
extension with disjunctive bodies
§
Advantages
and limitation of CHRD as a knowledge representation language
§
Slides:
CHRBasics.ppt
Lecture
12: CHREK: a Java Rule-Based Constraint
Programming Platform 07/06
§
Jairson & Marco Aurélio
§
Lecture 13:
Revision 14/06
§
Jacques
Lecture
14: Theorem Proving 23/06, 30/06
§
Jacques
§
Sections
7.2-7.7, 8, 9.1-9.2, 9.5 of Russell & Norvig
§
Outline:
§
Propositional
logic syntax
§
Propositional
logic semantics
§
Theorem
proving using propositional logic
§
Advantages
and limitations of propositional logic as a knowledge representation language
§
First-order
logic syntax
§
First-order
logic semantics
§
Unification
§
Theorem
proving using full first-order logic
§
Advantages
and limitations of full first-order logic as a knowledge representation
language
§
Comparison
with CHRD
§
Slides:
TheoremProving.ppt
Lecture
15: Tabled Monotonic Logic Programming
30/06, 05/07
§ Jeneffer Cristine Ferreira & Glaucya
Carreiro Boechat advised by Jacques
§
Sections
9.3-9.4 of Russell & Norvig
§
Sections
2 and 3 (skip the proofs) of Nilsson & Maluszynski
§
Section
3.1 of Tabled
Evaluation with Delaying for General Logic Programs:
§
Sections
2.1, 2.2, 3.3, 4.1-4.2, 4.4 and 5 of HiLog: a
Foundation for High-Order Logic Programming:
§
Outline:
§
The
metaphors of logic programming
§
Definite
logic programs (DLP) syntax
§
DLP
declarative semantics:
§ Closed-World Hypothesis
§ Intentional DLP declarative
semantics:
§ Extensional DLP declarative
semantics: least Herbrand model
§
DLP
operational semantics:
§
SLD
resolution for DLP (goal-driven backward chaining)
§
Immediate
consequence operator fixed point (data driven forward chaining)
§
SLG
resolution for DLP (both goal and data driven backward chaining)
§
Advantages
and limitation of DLP as a knowledge representation language
§
HiLog
§
Comparison
with CHRD
§
Comparison
with full first-order logic
§
Slides:
TMLP.ppt
Lecture
16: Reasoning about Actions and Change
05/07
§
Marcio Ribeiro de Carvalho advised by Jacques
§
Sections
10.3, 10.4, 10.7 and 10.8 of Russell & Norvig
§
Sections
2, 7 of Transaction Logic Programming, A Logic of Procedural and Declarative
Knowledge: http://citeseer.ist.psu.edu/10586.html
§
The
Event Calculus Explained: http://casbah.ee.ic.ac.uk/~mpsha/ECExplained.pdf
§
Outline:
§
The
frame, qualification and ramification problem
§
Situation
calculus
§
Event
calculus
§
Transaction
logic
§
Belief
revision and truth-maintenance systems
§
Comparison
§
Slides:
ActionsChange.ppt
Lecture
17: Constraint Logic Programming 07/07,
14/07
§
José
Lima Alexandrino advised by Jacques
§
p. 294 of Russel & Norvig
§
Chapters 4, 7 of Marriott & Stuckey
§
Outline:
§
Limitations of CSP
§
Limitations of Prolog
§
Goal and constraint evaluation in CLP
§
CLP as a reasoning service family
§
Slides:
CLP.ppt
Deadline for First Project Deliverable:
§
Modeling Teams: UML/OCL Ontology
of CSP Problems and Algorithms
§
Programming Teams: Java Implementation
of Conflict-Directed Backjumping Search for Finite Domain Constraint
Satisfaction
Lecture
18: Presentation, Feedback and
Discussion 1st Project Deliverable 26/07
§
Jacques
§
Orientação:
seminário de Daniela e Tarcisio
Lecture
19: Abduction and Negation as Failure
09/08
§ Jacques
§
Sections 10.7 of Russell & Norvig
§
Section 4 (skip the proofs) of Nilsson &
Maluszynski
§
Sections 1-4,8 of The Role of Abduction in
Logic Programming: http://citeseer.ist.psu.edu/kakas98role.html
§
Outline:
§
Limitations of monotonic reasoning with
incomplete knowledge
§
Negation as failure in Prolog: negative
hypothetical reasoning with knowledge gap
§
§
SLDNF
§
Well-founded models
§
Answer set programming
§
Default logic
§
Abductive frameworks: positive hypothetical
reasoning with knowledge gap
§
Applications of abduction
§
Abduction and default logic
§
Abduction and negation as failure
§
Abduction, belief revision and
truth-maintenance
§
Slides:
AbductionNAF.ppt
Lecture
20: Object-Oriented Rule-Based
Programming 28/07
§ Luiz Francisco Lacerda advised by Jacques
§
Sections 1-4 of Logical Foundations of
Object-Oriented and Frame-Based Languages: ftp://ftp.cs.sunysb.edu/pub/TechReports/kifer/flogic.pdf
§
Chapter 2 of A Model Theory for Non-Monotonic
Multiple Value and Code Inheritance in Object-Oriented Knowledge Base: http://www.cse.buffalo.edu/faculty/gzyang/papers/yangPhDdissertation.pdf
§
Sections 6, 7 of Flora-2: User’s Manual: http://flora.sourceforge.net/docs/floraManual.pdf
§
ILOG JRules Whitepaper: http://www.ilog.com/products/rules/whitepapers/index.cfm
§
Outline:
§
Limitations of Object-Oriented Programming
for AI
§
Limitations of Logic, Constraint and
Rule-Based Programming for AI
§
Embedding rules into objects
§
Embedding objects into rules
§
JRules: object-oriented production system
§
Frame Logic: object-oriented logic
programming
§
Structural and behavioral inheritance
§
Value and code inheritance
§
Monotonic and non-monotonic inheritance
§
Interaction between inheritance and deduction
§
Slides: OOLP.ppt
§
Orientação:
seminário de Eduardo
Lecture
21: Description Logics and the Semantic
Web 28/07, 02/08
§
Daniela Cargnin advised by Jacques
§
pp. 353-354 do Russell &
Norvig
§
An introduction to description logics: http://www.inf.unibz.it/~franconi/dl/course/dlhb/dlhb-01.pdf
§
pp. 142-148, 154-158, 172-183 of Description
logics: comparison with other formalisms
§
Chapters 1-4 of A
semantic web primer: http://bbs.sjtu.edu.cn/file/SemanticWeb/1096520972228120.pdf
§
Outline:
§
Description logics representation languages
§
Reasoning services of inference engines for description logics
§
Comparison between description logics and transaction frame logic
§
The semantic web vision
§
XML
§
RDF
§
RDFS
§
OWL
§
Inference engines for OWL
§
Semantic web ontology editors
§
Slides: DescritionLogicsSemanticWeb.ppt
§
Orientação:
seminário de Alexandre Maciel e de projetos
Lecture
22: XSB and Flora: a Versatile Logic
Programming Platform 30/07
§
Marco Aurélio & Jacques
§
The XSB Manual
§
The Flora-2 Manual
§
Outline:
§
Starting XSB
§
Loading and Compiling an XSB program
§
Compiling an XSB program
§
Submitting XSB queries
§
Tracing an XSB query
§
Debugging an XSB programs
§
Pitfalls in XSB programming
§
Starting Flora
§
Loading and Compiling a Flora program
§
Submitting Flora queries
§
Tracing a Flora query
§
Debugging a monotonic Flora program
§
Debugging a Flora program with updates
§
Interaction between updates and tabling
§
Pitfalls of Flora programming
§
Slides:
XSBFlora.ppt
Unit 5:
Probabilistic and Decision-Theoretic Reasoning
Lecture
23: Propositional Bayesian Reasoning
02/08
§
Tarcisio Gurgel advised by Jacques
§
Chapter 13 and Sections
14.1-14.5 of Russell & Norvig
§
Outline:
§
Reasoning with uncertain
knowlegde
§
Probability theory
§
Inference with full joint
probability distribution
§
Inference with Bayes’ rule
§
Bayesian networks
§
Exact inference in Bayesian networks
of discrete variables
§
Handling continuous variable in
Bayesian network inference
§
Approximate inference in
Bayesian networks
Slides: BayesianReasoning.ppt
§
Orientação:
seminário de João Paulo e de Eduardo
Lecture
24: Propositional Decision-Theoretic
Reasoning 04/08
§
Eduardo Dominoni advised by Jacques
§
Chapters 16, 17 of Russell &
Norvig
§
Outline:
§
From pursing a single goal to
trade-off and weight multiple goals
§
Utility theory and one-shot
decision problems
§
Decision networks
§
The utility of information
§
Sequential decision problems
§
Policy iteration
§
Partially observable markov
decision processes
§
Decision theoretic reasoning
§
Slides: DecisionTheoreticReasoning.ppt
§
Orientação:
seminário de João Paulo e de projeto
Lecture
25: First-Order Bayesian Reasoning 09/08
§
Alexandre Maciel advised by Jacques
§
Section 14.6 of Russell &
Norvig
§
Bayesian Logic Programs: ftp://ftp.informatik.uni-freiburg.de/documents/reports/report151/report00151.ps.gz
§
CLP(BN), Constraint Logic
Programming for Probabilistic Knowledge: http://www.cos.ufrj.br/~vitor/Yap/clpbn/
§
Probabilistic space partitioning
in constraint logic programming http://www-users.cs.york.ac.uk/~nicos/pbs/Asian04.ps.gz
§
Outline:
§
Limitations of Bayesian Networks
§
Extending Bayesian Networks with
database relations
§
Extending Logic Programs with
Conditional Probability Tables
§
Extending Logic Programs with
Probabilistic Constraints
§
Slides: FirstOrderProbabilisticReasoning.ppt
§
Unit 6:
Conclusion
Lecture 26: Artificial
Intelligence Paradigms 09/08
§
João
Paulo Rolim advised by Jacques
§
Sections
3.1, 5.1, 13.1, 14.1, 18.1-2 of Russell & Norvig
§
Sub-section
4.3 about genetic algorithms of Russell & Norvig
§
Sub-section
14.7 about fuzzy logic of Russell & Norvig
§
pp.
736-739, 744-748 of Sub-section 20.5 of Russell & Norvig
§
Section
9 of Wooldridge
§
Outline:
§
AI as
search (navigation metaphor)
§
AI as
symbolic processing (logic metaphor)
§ AI as numerical processing
§ Probabilistic processing
§ Fuzzy processing (linguistic
metaphor)
§
AI as
interaction (sociology and economics metaphor)
§
AI as
network activation (neurology metaphor)
§
AI as
evolution (genetic metaphor)
§
Hybrid
paradigms
§
Slides:
AIParadigms.ppt
Lecture
27: Final Exam 11/08
§
Jacques
§
Orientação:
de projeto
Lecture 28: 16/08
§
Jacques and Jairson
§
Orientação:
de projeto
Lecture 29: 18/08
§
Deadline for Second Project Deliverable:
§
Modeling Teams: UML/OCL Ontology
of Search and CHR
§
Programming Teams: Integration of CDBJ with
CHREK
Lecture
30: Presentation, Feedback and
Discussion of 2rd Project Deliverable 23/08
§
Jairson and Fábio