Saltar al contingut Saltar a navegacio
Map
  • Home
  • Information
  • Contact
  • Map

Distributed Intelligent Systems ( SID )

Change to no-tab version

ECTS Credits Department Type Requirements Teaching Languages
6.0 LSI
  • Specialization Complement (Computing)
 Pre-requisit IA
  • Catalan   
  • Spanish   
  • English   
Center where the class is given: Facultat d'Informàtica de Barcelona (FIB) - Universitat Politècnica de Catalunya - BarcelonaTECH

Description

The goals of this course are two-fold: first, to provide students with a sufficient mathematical and computational background to analyze distributed intelligent systems through appropriate models, and second, to illustrate several coordination strategies and show how to concretely implement and optimize them. The course is a well-balanced mixture of theory and laboratory exercises using simulation and real hardware platforms. It involves the following topics: 1) introduction to key concepts such as self-organization and software and hardware tools used in the course, 2) examples of natural, artificial, and hybrid distributed intelligent systems, 3) machine-learning methods: single- and multi-agent techniques, and 4) coordination strategies and distributed control.

Professors

Person in charge:   Ulises Cortés García (ia@lsi.upc.edu)
Other: Albert Atserias Peri (atserias@lsi.upc.edu)
Alberto Rubio Gimeno (albert@lsi.upc.edu)
Javier Béjar Alonso (bejar@lsi.upc.edu)
Ramon Sangüesa I Sole (sanguesa@lsi.upc.edu)
Weekly hours dedication T : 2.0 P : 1.0 L : 1.0 AA : 5.6 AD : 0.4

Generic Competences

Transversal Competences

  • TEAMWORK

  • G5 - To be capable to work as a team member, being just one more member or performing management tasks, with the finality of contributing to develop projects in a pragmatic way and with responsibility sense; to assume compromises taking into account the available resources.
  • G5.3 - To identify the roles, skills and weaknesses of the different members of the group. To propose improvements in the group structure. To interact with efficacy and professionalism. To negotiate and manage conflicts in the group. To recognize and give support or assume the leader role in the working group. To evaluate and present the results of the tasks of the group. To represent the group in negotiation involving other people. Capacity to collaborate in a multidisciplinary environment. To know and apply the techniques for promoting the creativity.

  • AUTONOMOUS LEARNING

  • G7.1 - Directed learning: perform the assigned tasks in the planned time, working with the indicated information sources according to the guidelines of the teacher or tutor. To identify the progress and accomplishment grade of the learning goals. To identify strong and weak points.
  • G7 - To detect deficiencies in the own knowledge and overcome them through critical reflection and choosing the best actuation to extend this knowledge. Capacity for learning new methods and technologies, and versatility to adapt oneself to new situations.

  • REASONING

  • G9 - Capacity of critical, logical and mathematical reasoning. Capacity to solve problems in her study area. Abstraction capacity: capacity to create and use models that reflect real situations. Capacity to design and perform simple experiments and analyse and interpret its results. Analysis, synthesis and evaluation capacity.
  • G9.1 - Critical, logical and mathematical reasoning capacity. Capacity to understand abstraction and use it properly.


Technical Competences

  • COMPUTER SCIENCE SPECIALIZATION

  • CCO2 - To develop effectively and efficiently the adequate algorithms and software to solve complex computation problems.
  • CCO2.1 - To demonstrate knowledge about the fundamentals, paradigms and the own techniques of intelligent systems, and analyse, design and build computer systems, services and applications which use these techniques in any applicable field.
  • CCO2.2 - Capacity to acquire, obtain, formalize and represent human knowledge in a computable way to solve problems through a computer system in any applicable field, in particular in the fields related to computation, perception and operation in intelligent environments.

Goals

  1. To master the basic concepst of Artificial Intelligence

    Related Competences
  2. To master the concept of intelligent agent and its role in the development of Multi Agent Systems

    Related Competences
  3. To Master the specific logics for Artificial Intelligence and Multiagent systems

    SubObjectius
    • To master the BDI formalism
    • To master the temporal logics formalism

    Related Competences
  4. To manage the basic of concepts of Ontologies and their application to real-world problems

    SubObjectius
    • To master the description logics formalism
    • To master the ontology languages proposed by the W3C
    • Understand and apply methods for developing ontologies

    Related Competences
  5. To manage and apply development methologies of Multiagent systems

    Related Competences
  6. To manage interaction protocols for agents' communication

    Related Competences
  7. Be able to analyze communication needs for a Multiagent system and be able to implement a pertinent communication protocol

    Related Competences
  8. To understand the foundations of game theory and decision theory and their relation with Multiagent systems

    Related Competences
  9. To understand negotation mechanisms for Multiagent Systems

    Related Competences
  10. Be able to understand applications of MAS to robotics

    Related Competences

Contents

1. Artificial Intelligence perspectives

Introduction to those real fields in which AI is or can be successfully applied.

2. An introduction to intelligent agents

What is an agent? Agents as basic building blocks. Agent types. Agent-building architectures and methodologies.

3. Ontologies

What is an Ontology? Methods for constructing Ontologies. Description logics. Ontological languages.

4. Logic systems for Artificial Intelligence

Reasoning for AI applications. Modal logics. Temporal logics. Reasoning under uncertainty.

5. Communication

The need for communication between agents. Speech Act Theory. Languages for establishing communication between agents.

6. Coordination

Need for co-ordination in multi-agent systems. Cooperation Negotiation between intelligent agents.

7. Introduction to Physical Agents

Agents for the real world: robotics, domotics, machine vision, control

Activities

Legend

ActivityEvaluative act activity T P L AA AD
Activity Evaluative act activity Theory hours Problem hours Lab hours Independent Learning Hours Directed Learning Hours

Artficial Intelligence Perspectives T      P      L      AA    AD    Total 
2.0 1.0 1.0 6.0 0.0 10.0

Alumn: The student will learn about the origins and foundations of Artificial Intelligence as well as some of the areas of application. To enhance student's learning he should read and understand the material assigned by the teacher.

Goals:

Contents
  • 1. Artificial Intelligence perspectives

Hours type description
T     What is an agent? The agent as a basic element of building systems. Types of agents. Architectures and methodologies for building agents
L     Introduction to the Laboratory. Theory session explaining how trends in software development lead to multi-agent systems. Contrast between the concept of object and the concept of agent. Introduction to agents and multi-agent systems (including, views, and small influences definitions).
AA    Organization of course materials
An introduction to intelligent agents T      P      L      AA    AD    Total 
4.0 2.0 2.0 14.0 0.0 22.0

Alumn: (-)

Goals:

Contents
  • 2. An introduction to intelligent agents

Hours type description
T     What is an agent? Agents as basic building blocks. Agent types. Agent-building architectures and methodologies.
P     Problems to consolidate the concepts of agent and type of environment. Identification of the type of environment and their properties and try to find the best agent for this type of environment.
L     Analysis of software design methodologies. Identification of problems presented and how these methodologies can be addressed. Introduction to design methodologies of agents, in contrast to traditional software design methodologies (object oriented).
Ontologies T      P      L      AA    AD    Total 
4.0 2.0 3.0 15.0 0.0 24.0

Alumn: Students not only should attend the lectures, but also do exercises on the use of Ontologies techniques and discuss with the teacher and other students on when is best to use each technique. In the laboratory students will apply what they learned in a moderate problem.

Goals:

Contents
  • 3. Ontologies

Hours type description
T     What is an Ontology? Methods for constructing Ontologies. Description logics. Ontological languages.
P     Session which resolves an issue robot explorers on Mars.
L     Introduction to ontologies. Explains the model of OWL ontology. It uses a sample ontology (PizzaOntology) to explain the basics: classes, object properties and property data. Advanced concepts of ontologies. Explains how a reasoner working on an OWL ontology and how to infer classes specifying the properties that must be met by members belonging to the class. Explains the different options for data and object properties (functional, transitive, inverse ...) . Ontology design methodologies. Introduction to Protégé tool for developing ontologies. Introduced the statement of the practice.
Logic systems for Artificial Intelligence T      P      L      AA    AD    Total 
5.0 3.0 3.0 15.0 0.0 26.0

Alumn: (-)

Goals:

Contents
  • 4. Logic systems for Artificial Intelligence

Hours type description
T     Reasoning for AI applications. Modal logics. BDI Temporal logics. Reasoning under uncertainty
P     Problems consolidate concepts of reasoning. It is used to demonstrate the problem box world performance of each type of reasoning, and planning is better than a random behavior, and re-consider how it can be counterproductive. We introduce the concepts and OverCommitment UnderCommitment.
L     Introduction to the Jade agent platform. Explanation of basic concepts about the platform, the services provided and how they develop players for the platform. Students develop their first JADE agent. The encoding and inject the agent platform.
Partial Exam T      P      L      AA    AD    Total 
2.0 - - 0.0 - 2.0

The partial exam will be done during standard class hours. People who do not pass the partial will be evaluated again on the final exam.

Setmana 8
Tipus Examen: Theory exam

Goals:
Communication T      P      L      AA    AD    Total 
4.0 2.0 2.0 12.0 0.0 20.0

Alumn: Students not only attend lectures, but also do exercises on the use of mechanisms for communication between autonomous agents and discuss with the teacher and other students when it is best to use each technique. In the laboratory the students apply what they learned in a problem.

Goals:

Contents
  • 3. Ontologies
  • 4. Logic systems for Artificial Intelligence
  • 5. Communication

Hours type description
P     Session where students presented a first sketch of the design of their ontologies. In this way, the teacher and classmates can make recommendations and constructive criticism to the design.
L     JADE. Refreshed JADE concepts. Introduced in depth the use of platform services and methods to boot it and inject agents. Students do exercises on the concepts introduced under the supervision of the teacher. Introduction of the latest concepts on JADE. Passing parameters, behavior, protocols and message passing between agents. Students do exercises on the concepts introduced under the supervision of Professor
Coordination T      P      L      AA    AD    Total 
5.0 3.0 3.0 15.0 0.0 26.0

Alumn: (-)

Goals:

Contents
  • 3. Ontologies
  • 4. Logic systems for Artificial Intelligence
  • 5. Communication
  • 6. Coordination

Hours type description
T     Need for co-ordination in multi-agent systems. Cooperation Negotiation between intelligent agents.
L     Introduction to the use of Jena to manipulate the knowledge base of ontological concepts. The teacher introduces an example using a JENA ontology example (PizzaOntology) and proposed minor changes to the students. The teacher supervises the execution of the amendment.
Introduction to Physical Agents T      P      L      AA    AD    Total 
4.0 2.0 1.0 7.0 0.0 14.0

Alumn: Students not only should attend the lessons, but also read the proposed papers

Goals:

Contents
  • 4. Logic systems for Artificial Intelligence
  • 5. Communication
  • 6. Coordination
  • 7. Introduction to Physical Agents

Hours type description
T     Introduction to Physical Agents
L     Sessions devoted entirely to the development of practice. The teacher provides support to students and answer their questions
Evaluation of practical exercises T      P      L      AA    AD    Total 
- - - 0.0 3.0 3.0

Delivery of the report on the practical works (3 or 4 four) made along the laboratory sessions.

Setmana 15-18
Tipus Examen: Interview/Practical delivery

Goals:
Final Exam T      P      L      AA    AD    Total 
- - - 0.0 3.0 3.0

Final exam for all the course contents.

Setmana 15-18
Tipus Examen: Final exam

Goals:
Total per type T      P      L      AA    AD    Total 
30.0 15.0 15.0 84.0 6.0 150.0

Teaching methods

The teaching methodology consists exposure theory classes in theory and application of concepts in classes and laboratory problems.
The examination will the same for all groups.

Assessment

Evaluation type

The subject is avaluated in exam period

Evaluation is based on a final exam and a part exam, grading of course assignments, and a grade for lab work. The final and part exams will test the theoretical knowledge and the methodology acquired by students during the course. The grade for course assignments will be based on submissions of small problems set during the course. Lab grades will be based on students" reports and lab practical work carried out throughout the course.

At about half of the 4-moth term there will be an exemptive exam, testing the first half of the course (exemptive only if the grade is 5 or more). The final exam will test both the first and the second part of the course. The first half is compulsory for those students who did not pass the part exam, and optional for the rest. The maximum of both grades (or only the one for the midterm exam) will stand as the grade for the first part.


The final grade will be calculated as follows:





GPar = part exam grade

GEx1 = 1st half of the final exam grade

GEx2 = 2nd half of the final exam grade



Total Exams grade = [max(Gpar, GEx1) + GEx2]/2



Final grade= Total Exams grade * 0.5 + Exercises grade * 0.2 + lab grade * 0.3


Competences' Assessment

The assessment of the competence on teamwork is based on work done during the laboratory assignments.

Generic competences weight in the evaluation specific part

  • 0.0 % - To identify the roles, skills and weaknesses of the different members of the group. To propose improvements in the group structure. To interact with efficacy and professionalism. To negotiate and manage conflicts in the group. To recognize and give support or assume the leader role in the working group. To evaluate and present the results of the tasks of the group. To represent the group in negotiation involving other people. Capacity to collaborate in a multidisciplinary environment. To know and apply the techniques for promoting the creativity.
  • 0.0 % - Directed learning: perform the assigned tasks in the planned time, working with the indicated information sources according to the guidelines of the teacher or tutor. To identify the progress and accomplishment grade of the learning goals. To identify strong and weak points.
  • 0.0 % - Critical, logical and mathematical reasoning capacity. Capacity to understand abstraction and use it properly.

Basics bibliography

  • Russell, Stuart. & Norvig, Peter , Artificial Intelligence: A Modern Approach. Third Edition , Prentice-Hall , 2011 , ISBN:0558881173.
    • http://aima.cs.berkeley.edu/

  • Minsky, Marvin , The Emotion Machine , Simon & Schuster , 2006 , ISBN:978-0-7432-7663-4.


  • Wooldridge, Michael , Reasoning about Rational Agents , MIT Press , 2000 , ISBN:0-262-23213-8.


  • Wooldridge, Michael , An Introduction to MultiAgent Systems - Second Edition , John Wiley & Sons , 2000 , ISBN: ISBN-10: 0470519460.


  • d'Inverno, Mark & Luck, Michael , Understanding Agent Systems , Springer Series on Agent Technology , 2010 , ISBN:3642073824.


Complementary bibliography

  • MURPHY, Robin , Introduction to AI Robotics , MIT Press , 2000 , ISBN:978-0-262-13383-8.


Links

  1. Obrir nova finestra http://aima.cs.berkeley.edu/
    (Third edition) by Stuart Russell and Peter Norvig
    The leading textbook in Artificial Intelligence.
    Used in over 1200 universities in over 100 countries.
    The 25th most cited publication on Citeseer (and 2nd most cited publication of this century).
Compartir
Share

 
logo FIB © Barcelona school of informatics - Contact - RSS