Agents and Distributed Artificial Intelligence
Keywords |
Classification |
Keyword |
OFICIAL |
Artificial Intelligence |
Instance: 2010/2011 - 1S
Cycles of Study/Courses
Acronym |
No. of Students |
Study Plan |
Curricular Years |
Credits UCN |
Credits ECTS |
Contact hours |
Total Time |
MIEIC |
134 |
Syllabus since 2009/2010 |
4 |
- |
6 |
56 |
162 |
Teaching language
Suitable for English-speaking students
Objectives
1- BACKGROUND
We assume in this course a technological-oriented approach to Software Agents design and applications. Students are expected to acquire a technological perspective on the subject.
2- SPECIFIC AIMS
Agents Oriented Programming is introduced as a new metaphore for designing and implementing distributed computer systems.
However, students will be able to deal with agents, as well as multi-agent systems, design through the support of formalization tools, including logics (intentional, BDI...).
Through small projects, students will be able to illustrate agents and MAS concepts in their practical aspects and importance.
3- PREVIOUS KNOWLEDGE
Knowledge of Artificial Intelligence techniques.
4- PERCENT DISTRIBUTION
Scientific component:50%
Technological component:50%
5- LEARNING OUTCOMES
At the end of the course, the student is expected to:
- Know the specificity of software agents;
- Recognize and describe the classes of problems more appropriate to use Agents and Multi-Agent Systems;
-Specify, by logical formalisms, the behavior of agents;
- Explore tools for building Agents and Multi-Agent Systems;
- Define and include intelligent decision strategies for Software Agents.
Program
1. Distributed Artificial Intelligence and Multi-Agent Systems
* Motivation and main Objectives
2. Agents
* Definitions, 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
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 Platforms for agents communication: (JATLite), JADE, Brahms
o Agents mobility (AGLETS)
4. Agents-Oriented Software Engineering
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 Negotiation 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.
6. Emotion-like based Agent architectures.
7. MAS Application examples
* Modelo ARCHON
* Resources management application
* "Truth maintenance" Distributed System
* Electronic Institutions (ForEV)
* E-Brokering - BIAS
Mandatory literature
M.Wooldridge; 'Introduction to MultiAgent Systems', John Wiley &Sons, 2002
Eugénio Oliveira ; 'Cópias dos quadros tópicos das Aulas',
Complementary Bibliography
Eds.M.Luck et al; Multi-Agent Systems and Applications, Springer, 2001
S. Russel and P. Norvig; 'Artificial Intelligence: A Modern Approach', Prentice Hall, 2003
Teaching methods and learning activities
Theoretical classes will be based on the oral presentation of the themes of the course, as well as interaction with students. Methods of implementation of applications will be taught (tools of specification and platforms for communication). Practical classes will be based on the supervision of students’ assignments. Reports are mandatory (in the middle and at the end of the semester).
Project oriented learning.
Software
Plataforma BRAHMS
Ambiente de Simulação REPAST
Plataforma de Sistemas Multi-Agente JADE
keywords
Technological sciences > Engineering > Knowledge engineering
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Description |
Type |
Time (hours) |
Weight (%) |
End date |
Attendance (estimated) |
Participação presencial |
56,00 |
|
|
Intermediate Report |
Defesa pública de dissertação, de relatório de projeto ou estágio, ou de tese |
6,00 |
|
|
Final Report |
Defesa pública de dissertação, de relatório de projeto ou estágio, ou de tese |
12,00 |
|
|
Project Final presentation and demonstration |
Exame |
1,00 |
|
|
Final Exam |
Exame |
3,00 |
|
|
Project development and implementation |
Trabalho escrito |
41,00 |
|
|
|
Total: |
- |
0,00 |
|
Amount of time allocated to each course unit
Description |
Type |
Time (hours) |
End date |
Study out of the classroom (Theoretical) |
Estudo autónomo |
42 |
|
|
Total: |
42,00 |
|
Eligibility for exams
Not exceed the absence limit allowed and have a minimum of 35% in the evaluation assignments (distributed classification - DC)
Calculation formula of final grade
FC = 0.5*DC + 0.5*EC
EC : Exam Classification (the use of pre-existing written material is allowed)
DC : Distributed Classification, includes:
- Intermediate Report plus demo: 15%
- Final Report:10%
- Project implementation and Demo: 20%
- Participation in class: 5%
To pass, the student must have a minimum of 35% in each of the two evaluation components, distributed and final exam.
Special assessment (TE, DA, ...)
The distributed evaluation is for all the students, regardless of their enrollment regime.
Students enrolled under special arrangements without attending practical classes, must agree with teachers the work being undertaken and the dates of assessment (intermediate and final). These students are not subjected to the evaluation of "Participation in class"
Classification improvement
The classification improvement can be done by improving the exam AND/OR improving the project