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Multi-Agent Systems and Simulation of Organizations

Code: 2MADSAD07     Acronym: SMASO

Keywords
Classification Keyword
OFICIAL Information Technology
OFICIAL Management Studies

Instance: 2015/2016 - 2S Ícone do Moodle

Active? Yes
Responsible unit: Agrupamento Científico de Matemática e Sistemas de Informação
Course/CS Responsible: Master in Modeling, Data Analysis and Decision Support Systems

Study cycles/ courses

Acronym No. of students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MADSAD 29 Bologna Official Syllabus 1 - 7,5 56 202,5

Teaching - Responsibilities

Teacher Responsibility
Pedro José Ramos Moreira de Campos

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
Pedro José Ramos Moreira de Campos 3,00

Teaching language

English

Objectives

Provide knowledge about systems of computational agents, models of communication, cooperation and decision. Demonstrate how these techniques can be used in the modeling of organizational dynamics.

Learning outcomes and competences

Knowledge about systems of computational agents, models of communication, cooperation and decision. Knowledge concerning how these techniques can be used in the modeling of organizations.

Working method

Presencial

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

Basic knowledge of programming languages (ex. R) are important, although it is not essential for simulation and modelling processes.

Programme

Introduction to agents to multi-agent systems (MAS). Characteristics and applications. Electronic commerce. Communication, interaction, cooperation and negotiation. Knowledge representation, ontologies and protocols. Learning actions. Reinforcement learning in MASs. Examples of systems MAS. MAS in the area of credit. MAS that simulates a market by producers and consumers. SMAs in the area of technological cooperation between companies. Platforms and software used in the programming of MAS's. NetLogo. Models of other agents. Networks and games. Coordination games. Iterative prisoner dilemma. Prisoner's dilemma and duopoly. Space games. Dynamic interactions. Validation of MAS's. Learning in dynamic environments.

Mandatory literature

Jaque Ferber; Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence, Addison Wesley, 1999

Teaching methods and learning activities

Theoretical-practical sessions using computer for simulation purposes

Software

R, NetLogo.

Type of assessment

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Trabalho escrito 100,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (Hours)
Elaboração de projeto 5,00
Frequência das aulas 21,00
Trabalho de investigação 10,00
Trabalho laboratorial 10,00
Total: 46,00

Eligibility for exams

Presence in 75% of classes

Calculation formula of final grade

Final grade is calculated on the baseis of grades of different practical assignments.

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