Métodos de Computação Inteligentes 2005.1

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General information:

§                     Lecturer: Jacques Robin

§                     Teaching assistants: Fábio Moura, Jairson Vitorino & Marco Aurélio da Silva

§                     When: 3a 08:00-10:00 & 5a 10:00-12:00

§                     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., Lyons, B. and Fado, D. 2004. Wiley.

§                     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

Nome

Login

Curso

Área

Orientador

Tema

Java

UML

OCL

Lógica

Disciplinas de IA 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,
Redes Neurais e Aprendizagem de Máquina

Gláucya Boechat

gcb

M

Comp. Int.

Edson Carvalho

 

+

+

-

+

IA

Humberto Brandão

hcbo

M

Otimização IA

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 IA, IA Simbólica

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 IA, IA Simbólica, Redes Neurais, Mineração de Dados

Luiz Gustavo Carvalho

lgcc

M

Comp. Int.

Edson Carvalho

 

+

+

-

+

IA

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

 

 

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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

§         Readings:

§                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

§         Readings:

§                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

§         Readings:

§                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

 

 

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Unit 2: Search

Lecture 4: Problem Solving through Search 05/05

§         João Fárias advised by Jairson

§         Readings: Section 3 of Russell & Norvig

§         Outline:

§                Agent reasoning as navigating a space of possibilities

§                Exhaustive search strategies

§                Avoiding repeated states

§                Searching with partial information

§         Slides: AIasSearch.ppt

Lecture 5: Heuristic Search Algorithms 10/05

§         Humberto César Brandão advised by Jairson

§         Readings: Section 4 of Russell & Norvig

§         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

§         Readings:

§                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

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Unit 3: Knowledge Representation

Lecture 7:  Object-Oriented Knowledge Representation 17/05

§         Fábio

§         Readings:

§                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

§         Readings:

§                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

§         Slides: ProjectTopics.ppt

 

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Unit 4: Logical Knowledge Formalization and Automated Reasoning

Lecture 11: Rule-Based Constraint Programming 02/06

§         Jairson

§         Readings:

§                Chapter 7 of Frühwirth & Abdennadher

§                Chapters 2 of Marriott & Stuckey

§                Chapter 5 of Rule-Based Constraint Programming: Theory and Practice: http://www.cs.guc.edu.eg/faculty/sabdennadher/Publikationen/habil-schriftNew.ps.gz

§         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: CHR.ppt

Lecture 12:  CHREK: a Java Rule-Based Constraint Programming Platform 07/06

§         Jairson & Marco Aurélio

§         Readings: The CHREK user manual http://www.cin.ufpe.br/~orcas/kjchr/

§         Slides:  CHREK.ppt

Lecture 13: Revision 14/06

§         Jacques

Lecture 14:  Theorem Proving 23/06, 30/06

§          Jacques

§         Readings:

§                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

§         Readings:

§                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: Clark’s completion

§   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

§         Readings:

§                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

§         Readings:

§                 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 28/07

§ Jacques

§         Readings:

§                 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

§                 Clark’s completion

§                 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

§         Readings:

§                 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 e projetos

Lecture 21:  XSB and Flora: a Versatile Logic Programming Platform 30/07

§         Marco Aurélio & Jacques

§         Readings:

§                 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

Lecture 22:  Description Logics and the Semantic Web 02/08

§         Daniela Cargnin advised by Jacques

§         Readings:

§                 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

 

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Unit 5: Probabilistic and Decision-Theoretic Reasoning

Lecture 23:  Propositional Bayesian Reasoning 02/08

§         Tarcisio Gurgel advised by Jacques

§         Readings:

§                  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

§                     Deadline for Second Project Deliverable:

§    Modeling Teams: UML/OCL Ontology of Monotonic Logic-Based Automated Reasoning

Programming Teams: Java/CHREK Implementation of CLP(FD)

§           Orientação: seminário de João Paulo e de Eduardo

 

Lecture 24:  Presentation, Feedback and Discussion of 2nd Project Deliverable 04/08

§         Jacques and Jairson

§           Orientação: de projeto

§          

Lecture 25:  Propositional Decision-Theoretic Reasoning 04/08

§         Eduardo Dominoni advised by Jacques

§         Readings:

§                 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

Lecture 26:  First-Order Bayesian Reasoning 09/08

§         Alexandre Maciel advised by Jacques

§         Readings:

§                 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

§                     Deadline for Third Project Deliverable:

§    Modeling Teams: UML/OCL Ontology of Non-Monotonic Logic-Based Automated Reasoning

§    Programming Teams: Java/CHREK Implementation of Object-Oriented CLP(FD)

§           Orientação: seminário de João Paulo e de projeto

 

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Unit 6: Conclusion

Lecture 28: Artificial Intelligence Paradigms 09/08

§         João Paulo Rolim advised by Jacques

§         Readings:

§                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 29:  Final Exam 11/08

§         Jacques

Lecture 30: 18/08

§                     Deadline for Fourth Project Deliverable:

§                Modeling Teams: UML/OCL Ontology of Bayesian and Decision-Theoretic Automated Reasoning

§                Programming Teams: Java/CHREK or Flora/CHR/XSB Implementation of Object-Oriented Bayesian Networks

Lecture 31:  Presentation, Feedback and Discussion of 4th Project Deliverable 23/08

Jairson and Fábio

 

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