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.
Person in charge
Maite López (
Generic Technical Competences
CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.
Technical Competences of each Specialization
CEA1 - Capability to understand the basic principles of the Multiagent Systems operation main techniques , and to know how to use them in the environment of an intelligent service or system.
CEA7 - Capability to understand the problems, and the solutions to problems in the professional practice of Artificial Intelligence application in business and industry environment.
CEA9 - Capability to understand Multiagent Systems advanced techniques, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.
CEP3 - Capacity for applying Artificial Intelligence techniques in technological and industrial environments to improve quality and productivity.
CEP4 - Capability to design, write and report about computer science projects in the specific area of ??Artificial Intelligence.
CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.
CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.
Analisis y sintesis
CT7 - Capability to analyze and solve complex technical problems.
To be familiar with alternative agents' social models and interaction behaviours.
To be aware of possible applications of multi-agent technologies and agent-based simulation.
To be introduced to Multi-Agent Reinforcement Learning methods.
To be familiar with the ethical/moral aspects associated with autonomous systems.
To understand how multi-agent systems use norms to coordinate, and how they can be adapted.
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.
They will also become able to develop simulations of multi-agent systems and analyse how they perform globally.
Objectives referring to attitudes, values and norms:
Students will develop teamwork skills and will reflect on ethical / moral aspects associated to autonomous systems.
Introduction to multi-agent systems
* Cooperative vs competitive agents, * Social models, * Organizations * Institutions * Applications
* Individual modelling, * Social analysis, * Tools & case studies
Adaptation and coordination
* Normative Multi-Agent systems * Moral agents * Multi-Agent Reinforcement Learning.
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.
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.
The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) sponsors the annual International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Conference proceedings are linked here. http://www.ifaamas.org/index.html