Multi-Agent Systems and Simulation of Organizations

Code:

2MADSAD07

Acronym:

SMASO

Keywords
Classification	Keyword
OFICIAL	Information Technology
OFICIAL	Management Studies

Instance: 2017/2018 - 2S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MADSAD	20	Bologna Official Syllabus	1	-	7,5	56	202,5

Teaching language

English

Objectives

Provide knowledge about systems of computational agents, models of distributed 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.

Program

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, R, and Python. Models of other agents. Networks and games. Coordination games. Iterative prisoner dilemma. Prisoner's dilemma and duopoly. Space games. Dynamic interactions. Swarm Intelligence. Validation of MAS's. Learning in dynamic environments. Applications.

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.
Python

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	10,00
Teste	30,00
Trabalho prático ou de projeto	60,00
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

NF=0.3*NT+0.6*NE+0.1*PIC

where:
NF=Final grade
NT=Test grade
NE=Practical Assessments grade
PIC=Classroom participation