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Electronic Business Technologies

Code: EIC0078     Acronym: TNEL

Classification Keyword
OFICIAL Artificial Intelligence

Instance: 2014/2015 - 2S Ícone do Moodle

Active? Yes
E-learning page: https://moodle.fe.up.pt/
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 26 Syllabus since 2009/2010 4 - 6 56 162

Teaching - Hours

Recitations: 3,00
Type Teacher Classes Hour
Recitations Totals 1 3,00
Ana Paula Cunha da Rocha 1,50
Henrique Daniel de Avelar Lopes Cardoso 1,50

Teaching language

Suitable for English-speaking students


Conducting business electronically is an increasingly ubiquitous approach. This course addresses this concept from a scientific-technological point of view.

Specific aims are: To understand the importance and opportunities of Electronic Business; To explain Electronic Business Models; To present Support Technologies for Electronic Business

Scientific component:50% Technological component:50%

Learning outcomes and competences

At the end of this course, students should:

  • understand and model a simple Electronic Business process
  • use intelligent technologies that support simple Electronic Business processes, related to negotiation, e-contracting and computational trust.

Working method


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

Knowlege of Distributed Artificial Intelligence technologies is useful, as is understanding the notion of Computational Agent.


1. Electronic Business

1.1. Definition of electronic business process.

1.2. Electronic Business Models (B2B, B2C)

1.3. Platforms for electronic business

2. Electronic Institutions and Virtual Enterprises:

2.1. Concepts

2.2. Selection of business partners: automatic negotiation and Learning

2.3 Ontologies: specification, creation, integration and exploration; automatic ontology-mapping techniques

2.4. Automatic e-contracting: specification and monitoring of e-contracts with normative environments

2.5. Computational trust and reputation for Electronic Business

3. Inter-organizational workflow

Mandatory literature

Maria Fasli; Agent technology for e-commerce. ISBN: 978-0-470-03030-1

Complementary Bibliography

Sarit Kraus; Strategic negotiation in multiagent environments. ISBN: 0-262-11264-7

Teaching methods and learning activities

Project and research-oriented learning. Implementation assignments. Theoretical presentation of course unit themes. Presentation of case studies.


JADE (Java Agent Development Environment)


Technological sciences > Technology > Information technology > E-business
Technological sciences > Technology > Information technology > Virtual organisations

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Participação presencial 10,00
Trabalho escrito 15,00
Trabalho laboratorial 75,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 20,00
Frequência das aulas 42,00
Trabalho de investigação 40,00
Trabalho laboratorial 60,00
Total: 162,00

Eligibility for exams

Student may not exceed the allowed limit of absences.

Calculation formula of final grade

Distributed Evaluation comprises:

  • individual presentation of a case-study: 25%
  • group project implementation: 50%
  • scientific article documenting the project: 15%
  • student performance assessment along the semester: 10%

To successfully complete the course, students have to reach a minimum grade of 35% in each of the components listed above.

Special assessment (TE, DA, ...)

Students with special status have to fulfill all the assessment components.

The assessment at a special season will be based on:

  • individual presentation of a case-study: 25%
  • project implementation and presentation: 60%
  • scientific article documenting the project: 15%

To successfully complete the course unit, students have to reach a minimum grade of 35% in each of the components listed above.

Classification improvement

Students may improve their grades in the next course edition.

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