The Eighth International Workshop on Agents and Data Mining Interaction

Organizational Control for Data Mining with Large Numbers of Agents

Victor Lesser, University of Massachusetts Amherst, USA

Victor Lesser

Talk Outline

Over the last few years, my research group has begun exploring the issues involved in learning when there are hundreds to thousands of agents. We have been using the idea of organization control as a low overhead way of coordinating the learning of such large agent collectives. In this lecture, the results of this research will be discussed and its relationship to issues in distributed data mining.

Victor Lesser Bio

Victor R. Lesser is a Distinguished Professor Emeritus of Computer Science at University of Massachusetts - Amherst. His major research focus is on the control and organization of complex AI systems, and he is considered one of the founders of the multi-agent field having worked in this area since the early 1970s when he did the first work on blackboard systems with the Hearsay-II speech understanding system. His recent work has focused on the use of organization control in multi-agent systems. He has worked in application areas such as sensor networks for vehicle tracking and weather monitoring, speech and sound understanding, information gathering on the internet, peer-to-peer information retrieval, intelligent user interfaces, distributed task allocation and scheduling, and virtual agent enterprises. Professor Lesser was the recipient of the IJCAI-09 Award for Research Excellence and is also a Founding Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and an IEEE Fellow.

Competitive Benchmarking: Lessons learned from the Trading Agent Competition

Wolfgang Ketter, Erasmus University Rotterdam, Netherlands

Talk Outline

Many important developments in artificial intelligence have been stimulated by organized competitions that tackle interesting, difficult challenge problems, such as chess, robot soccer, poker, robot navigation, stock trading, and others. Economics and artificial intelligence share a strong focus on rational behavior. Yet the real-time demands of many domains do not lend hemselves to traditional assumptions of rationality. This is the case in many trading environments, where self-interested entities need to operate subject to limited time and information. With the web mediating an ever broader range of transactions and opening the door for participants to concurrently trade across multiple markets, there is a growing need for technologies that empower participants to rapidly evaluate very large numbers of alternatives in the face of constantly changing market conditions. AI and machine-learning techniques, including neural networks and genetic algorithms, are already routinely used in support of automated trading scenarios. Yet, the deployment of these technologies remains limited, and their proprietary nature precludes the type of open benchmarking that is critical for further scientific progress.

The Trading Agent Competition was conceived to provide a platform for study of agent behavior in competitive economic environments. Research teams from around the world develop agents for these environments. During annual competitions, they are tested against each other in simulated market environments. Results can be mined for information on agent behaviors, and their effects on agent performance, market conditions, and the performance and behavior of competing agents. After each competition, competing agents are made available for offline research. We will discuss results from various competitions (Travel, Supply-Chain Management, Market Design, Sponsored Search, and Power Markets).

Wolfgang Ketter Bio

Wolfgang Ketter is Associate Professor of Information Systems at the Department of Decision and Information Sciences at the Rotterdam School of Management of the Erasmus University. He received his Ph.D. in Computer Science from the University of Minnesota in 2007. He founded and runs the Learning Agents Research Group at Erasmus (LARGE) and the Erasmus Center for Future Energy Business. The primary objective of LARGE is to research, develop, and apply autonomous and mixed-initiative intelligent agent systems to support human decision making in the area of business networks, electronic markets, energy grids, and supply-chain management. The energy research enables the robust, intelligent, efficient, and sustainable energy networks of the future. He was co-chairing the TADA workshop at AAAI 2008, the general chair of the Trading Agent Competition (TAC) 2009, is member of the board of directors of the Association for Trading Agent Research (ATAR) since 2009, and its chair since 2010. He is leading Power TAC, a new TAC competition on energy retail markets; the pilot competition was held at International Joint Conference of Artificial Intelligence (IJCAI) 2011 in Barcelona and the first inaugural competition will be held at AAMAS 2012 in Valencia. Since 2011 Wolfgang also serves as the chair of the IEEE Task Force on Energy Markets. He was the program co-chair of the International Conference of Electronic Commerce (ICEC) 2011. His research has been published in various information systems, and computer science journals such as AI Magazine, Decision Support Systems, Electronic Commerce Research and Applications, Energy Policy, European Journal of Information Systems, INFORMS OR/MS Today, INFORMS Information Systems Research, and International Journal of Electronic Commerce. He serves on the editorial board of Electronic Commerce Research and Applications.