Code:

BERM.ASC.013

Study year:

2008/2009

Long name:

Multi Agent Systems Research

ECTS:

5

Language:

English

Lecturer(s):

Eric van Heck, Wolfgang Ketter

Faculty:

RSM Erasmus University

Number of lectures:

Hours per lecture:

Goal:

Expose students to the state of the art in research on multi-agent systems to gather information and facilitate decision making in business and economics environments. The course is designed to help students to develop and deepen their own research ideas and proposals.

Course contents:

This exciting new research course is designed to familiarize MPhil and Ph.D. students with a wide variety of issues in the domain of intelligent multi-agents systems. The course is designed to help students to develop and deepen their own research ideas. The study of agents presents a unique opportunity to integrate results from many diverse areas of research, such as artificial intelligence, behavioral science, computer science, economics, information systems, operations research, social sciences, and software engineering. Thus the aim of this course is to expose students to the state of the art in research on multi-agent systems to gather information and to facilitate decision making in business and economic environments. In addition to providing students with knowledge in the area of multi-agents systems, students will get familiarized with the methods and paradigms used in the area.

Modern business networks and markets are highly dynamic and exhibit a high degree of uncertainty. Under these conditions business managers are routinely faced with complex strategic, tactical, and operational decision, decisions ranging from the macroscopic (i.e. which markets should we enter and when?) to the microscopic (i.e. which products should be packed on which pallet?). Also customers are faced with multi attribute decisions, such as from whom can I book a travel under certain constraints (money, time, quality, etc)? Within this workshop we investigate how learning agents may be disgned to support humans in these decision making processes. We define learning agents as software.

Entities that carry out some set of operation on behalf of a user or another program with some degree of independence or autonomy, improve their performance from experience and in so doing employed some knowledge or representation of the user’s goal or needs.

This workshop provides a broad introduction to autonomous agents with an emphasis on multiagent systems. Topics include:

The second main emphasis of the workshop lies on applications of multi-agent system to gathering information and facilitate decision making. Topics may include:

Call routing and billing entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, improve their performance from experience and in so doing employed some knowledge or representation of the user’s goal: or needs.

Examination:

Contribute to the discussion of the workshop lectures and assigned articles. Present research papers to the rest of the class. Write a original research proposal due at the end of the course and present it to the class

Literature:

Academic articles

Recommended reading: “Multiagent systems. A modern approach to Distributed Artificial Intelligence.” Edited by Gerhard. Weiss. The MIT Press, 1999. ISBN 0262731312

Additional information: