Sistemas Multiagentes Autoorganizados

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Créditos
4.5
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos, pero tiene capacidades previas
Departamento
UB;CS
Mail
Autonomic Computing is an initiative started by IBM in 2001. Its ultimate aim is to create selfmanaging computer systems to overcome their rapidly growing complexity and to enable their further growth. This course approaches this area from the Multi-Agent Systems and Self-Organization point of view:
· A multi-agent system is one composed of multiple interacting software components known as agents, which are typically capable of cooperating to solve problems that are beyond the abilities of any individual member.
· Self-organization is a process in which the internal organization of a system, normally an open system, increases in complexity without being guided or managed by an outside source.
The main objective of this course is to provide an insight of the autonomic capabilities of different multi-agent systems. As a result, students will acquire the capability to discern what applications are suitable for applying agent-oriented solutions, and how these solutions can adapt to eventual changes automatically.

Profesores

Responsable

  • Maite López ( )

Horas semanales

Teoría
1
Problemas
1
Laboratorio
1
Aprendizaje dirigido
0.12
Aprendizaje autónomo
5.55

Competencias

Competencias Técnicas Genéricas

Genéricas

  • CG3 - Capacidad para la modelización, cálculo, simulación, desarrollo e implantación en centros tecnológicos y de ingeniería de empresa, particularmente en tareas de investigación, desarrollo e innovación en todos los ámbitos relacionados con la Inteligencia Artificial.

Competencias Técnicas de cada especialidad

Académicas

  • CEA1 - Capacidad de comprender los principios básicos de funcionamiento de las técnicas principales de los Sistemas Multiagentes, y saber utilizarlas en el entorno de un sistema o servicio inteligente.
  • CEA7 - Capacidad de comprender la problemática, y las soluciones a los problemas en la práctica profesional de la aplicación de la Inteligencia Artificial en el entorno empresarial e industrial.
  • CEA9 - Capacidad de comprender las técnicas avanzadas de Sistemas Multiagentes, y saber diseñar, implementar y aplicar estas técnicas en el desarrollo de aplicaciones, servicios o sistemas inteligentes.

Profesionales

  • CEP2 - Capacidad de resolver los problemas de toma de decisiones de las diferentes organizaciones, integrando herramientas inteligentes.
  • CEP3 - Capacidad de aplicación de las técnicas de Inteligencia Artificial en entornos tecnológicos e industriales para la mejora de la calidad y la productividad.
  • CEP4 - Capacidad para disenar, redactar y presentar informes sobre proyectos informaticos en el area especifica de Inteligencia Artificial.

Competencias Transversales

Trabajo en equipo

  • CT3 - Ser capaz de trabajar como miembro de un equipo interdisciplinar ya sea como un miembro mas, o realizando tareas de direccion con la finalidad de contribuir a desarrollar proyectos con pragmatismo y sentido de la responsabilidad, asumiendo compromisos teniendo en cuenta los recursos disponibles.

Uso solvente de los recursos de información

  • CT4 - Gestionar la adquisicion, la estructuracion, el analisis y la visualizacion de datos e informacion en el ambito de la especialidad y valorar de forma critica los resultados de esta gestion.

Analisis y sintesis

  • CT7 - Capacidad de analisis y resolucion de problemas tecnicos complejos.

Objetivos

  1. Learning objectives referring to knowledge:
    Autonomic Computing is an initiative that was started by IBM in 2001. Its ultimate aim is to create self-managing computer systems to overcome their rapidly growing complexity and to enable their further growth. This course unit introduces students to the major concerns in this emerging field, focusing on multi-agent systems and self-organization capabilities.

    - A multi-agent system is composed of multiple interacting software components, or agents, which are typically
    capable of cooperating to solve problems that are beyond the abilities of any individual member.

    - Self-organization is a process in which the internal organization of a system, normally an open system, increases in complexity without being guided or managed by an outside source.

    The main objective of this course unit is to provide an insight into the autonomic capabilities of different multi-agent systems.
    Competencias relacionadas: CEA7, CEA9, CEA1, CEP2, CEP4, CT4, CT7,
  2. Objectives referring to abilities, skills: Students will acquire the capacity to determine which applications are compatible with the implementation of agent-oriented solutions and how these solutions can adapt automatically to periodic changes.
    Competencias relacionadas: CG3, CEP3,
  3. Objectives referring to attitudes, values and norms: Students will develop strong teamwork skills.
    Competencias relacionadas: CT3,

Contenidos

  1. Introduction to multi-agent systems
    * Social models
    * Cooperative vs competitive agents
    * Contract networks
    * Coalitions
    * Organizations
  2. Agent-based simulation
    * Individual modelling
    * Social analysis
    * Tools & case studies
  3. Adaptation and coordination
    * Coalitions
    * Organizations
    * Autonomic electronic institutions
    * Coordination within virtual institutions
    * Multiple institutions
  4. Normative Multi-Agent Systems
    Norms as a coordination mechanism

Actividades

Actividad Acto evaluativo


Presentation and discussion of a research paper


Objetivos: 1
Contenidos:
Teoría
6h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
24h

Course practical assessment


Objetivos: 1 2 3
Contenidos:
Teoría
2h
Problemas
0h
Laboratorio
6h
Aprendizaje dirigido
0h
Aprendizaje autónomo
60h

Teoría
8h
Problemas
14h
Laboratorio
0h
Aprendizaje dirigido
0.1h
Aprendizaje autónomo
10h

Metodología docente

The course unit will be taught through a series of theory and practical sessions:

- Participatory theory sessions in which new concepts are introduced and discussed between students. Group discussion is strongly encouraged. Textbook chapters and research papers will be provided to facilitate debate and exchange of ideas.

- Practical sessions in which students put into practice previously introduced concepts to gain further insight. This objective will be achieved by solving problems, designing systems, and developing prototypes.

Método de evaluación

Students will be assessed on in-class oral presentations and/or their work in practical assignments. Typically, marks for oral presentations will be awarded on an individual basis, whereas marks for practical assignments will be based on an assessment of the whole group. The weighting of the final grade will be proportional to the respective workloads of the two tasks.

Examination-based assessment: Students will submit a practical exercise for assessment at the end of the course unit.

Bibliografía

Básica:

  • An Introduction to Multiagent Systems - Michael Wooldridge, John Wiley & Sons, 2002. ISBN: 0 7149691X
  • Multiagent Systems, A Modern Approach to Distributed Artificial Intelligence - Gerhard Weiss, MIT Press, 1999. ISBN: 0-262-23203-0
  • Complex Adaptive Systems: An Introduction to Computational Models of Social Life - John H. Miller, Scott E. Page,
  • Developing Multi-Agent Systems with JADE - Fabio Luigi Bellifemine, Giovanni Caire, Dominic Greenwood, Wiley Series in Agent Technology,

Complementaria:

  • Programming Multi-Agent Systems in AgentSpeak using Jason - Rafael H. Bordini, Jomi Fred Hübner, Michael Wooldridge, Wiley Series in Agent Technology , .

Web links

Capacidades previas

It will help to know about MAS (Multi-Agent Systems)