Multi Agent Systems Research

Information

This exciting 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, behavioural 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 decisions; decisions ranging from the macroscopic (i.e. which markets should we enter and when?) to the microscopic (i.e. how should be design our tariffs?). Also managers are faced with multi attribute decisions, such as from whom can I purchase products under certain constraints (money, time, quality, etc) to form a balanced portfolio? Within this workshop we investigate how learning agents may be designed to support humans in these decision making processes. We define learning agents as software 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 employ some knowledge or representation of the user’s goals or needs.

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

• Agent architectures and modeling 
• Competitive simulations, especially trading scenarios
• Inter-agent communication and trust
• Collective intelligence and cognitive collaboration
• Teamwork and distributed rational decision making
• Mechanism design and auctions
• Multiagent learning
• Negotiations

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

• Supply-chain management
• Procurement, Production, Sales agents
• Electronic markets
• Auction markets (Ads, Flowers, eBay, …)
• Recommender agents (Amazon, Shop bots, Dell, ...)
• Energy markets/Smart Grids
• Portfolio management
• Trader and market maker modeling
• Policy guidance
• Financial Markets
• Portfolio management
• Trader and market maker modeling
• Online Marketing
• Customer preference selection
• Transportation
• Dynamic fleet management
• Public and goods transport