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Multi-agent Systems

Code: PRODEI012     Acronym: SMA

Classification Keyword
OFICIAL Intelligent Systems

Instance: 2018/2019 - 1S Ícone do Moodle

Active? Yes
Web Page: http://paginas.fe.up.pt/~eol/SMA/sma.html
Responsible unit: Department of Informatics Engineering
Course/CS Responsible: Doctoral Program in Informatics Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
PRODEI 4 Syllabus 1 - 6 28 162

Teaching Staff - Responsibilities

Teacher Responsibility
Henrique Daniel de Avelar Lopes Cardoso
Luís Paulo Gonçalves dos Reis

Teaching - Hours

Recitations: 2,00
Type Teacher Classes Hour
Recitations Totals 1 2,00
Henrique Daniel de Avelar Lopes Cardoso 1,00
Luís Paulo Gonçalves dos Reis 1,00

Teaching language



Global perspective about the techniques assotiated to the specification and design of computational software agents and Multi-agent systems (MAS). To understand the practical importance of MAS applications.

The main Goals involve: the recognition of when to use this distributed and decentralised approach, as well as to know how to do it; To specify models of agents' architectures and multi-Agent Systems interaction.

To look at Agent oriented programming paradigm as a new metaphor for designing distributed computer systems. To know how to formalize through intentional logics agents knowledge and functionalities.

Learning outcomes and competences

To recognize the main characteristics of the class of problems that are more suitable for the use of MAS.

Students should have the capability of using apropriate software tools to develop MAS applications.

Small projects have to be specified and implemented to illustrate the importance of both agents and multi-agent systems

Working method


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

It is important to be knowledgeable about Artificial Intelligent topics (Knowledge Representation, problem Solving through Search)


1. Distributed Artificial Imtelligence and Multi-Agent Systems * Motivation and main Objectives 2. Agents * Defenitions, basic Architectures * Knowledge Representation and Logic for Agents. * Advanced Agents' Architectures o Subsumption and Reactive Agents o mentalistic-like architectures and Deliberative Agents * Learning Agents o Reinforcement Learning o Non-supervised Learning (Clustering) 3. Interaction in MAS * Coordination and Cooperation o Strategies for Cooperation o Knowledge for Cooperation * Supporting Communication o Agents communication Languages: (KQML and) ACL o Ontologies: concepts, languages (XML, RDF),Tools o Plataforms for agents communication: JADE, JADEX, REPAST, (JINI) o Agents mobility (AGLETS) 4. Agents-Oriented Software Engeneering o Improving GAIA methodology 5. Agents' Negotiation * Contract Net and Market-based protocols * Electronic Commerce o Open and Closed Auctions o MAS and Electronic Commerce o Learning strategies for trading *Game Theory and Negotiaition Domains o Concepts from Economics o Characterizing Negotiation Domains: TOD and WOD * Negotiation techniques and Game Theory o Agents Joint Planning o Agreements, Coalitions and Utility measure * Argumentation and Dialog Systems. * Normative Environments; Trust and Reputation Computational Models. 6. Emotion-like based Agent architectures. 7. MAS Application examples * ARCHON Model * Resources management application * "Truth maintenance" Distributed System * Electronic Institutions (ANTE) * E-Brokering - BIAS * Emotion-like based Agents example.

Mandatory literature

Russell, Stuart; Artificial intelligence. ISBN: 0-13-360124-2
Wooldridge, Michael; An Introduction to multiagent systems. ISBN: 0-471-49691-X

Complementary Bibliography

Shoham, Yoav ; Leyton-Brown, Kevin; Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009. ISBN: 0-13-360124-2
Rosenschein, J.S.; Zlotkin, G. ; Rules of Encounter, MIT Press, 1994
Weiss, Gerhard (Ed.); Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence, MIT Press, 1999
Endriss, U.; Maudet,N.; Sadri,F.; Toni F. ; Negotiating Socially Optimal Allocations of Resources, Journal of Artificial Intelligence Research, 25:315-348, 2006 (Paper published in a Journal)

Teaching methods and learning activities

Teorethical Cocepts are conveyed during classes. Small projects on multi-agent systems are assigned to groups of students.




Physical sciences > Computer science > Computer architecture > Distributed computing
Technological sciences > Technology > Information technology
Technological sciences > Technology > Information technology > Virtual organisations
Technological sciences > Technology > Information technology > E-business
Technological sciences > Technology > Information technology > Trust technology
Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 50,00
Participação presencial 0,00
Trabalho laboratorial 50,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 47,00
Total: 47,00

Eligibility for exams

Submission of a written report together with a dermo about the assigned project. Grading must be >= 35%

Calculation formula of final grade

- Assigned Project: 60% to 40% : . 20 to 30% demo . 20 to 30% final report)

- Final Exam: 40 to 60%

Examinations or Special Assignments

Assigned Project using MAS

Special assessment (TE, DA, ...)

Assigned project demo and Exam

Classification improvement

Through exam or/and through project results improvement

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