Ana Bazzan

Ana Bazzan received her PhD in 1997 from the University of Karlsruhe (Germany), and an MSc. in Computer Science from the Institute of Informatics at the University of Rio Grande do Sul (UFRGS) in Porto Alegre (Brazil). From 1997 to 1998, she had a postdoc research associate position in the Multi-Agent Systems Laboratory at the University of Massachusetts in Amherst, under the supervision of Prof. Victor Lesser. In 1999 she joined the Institute for Informatics at UFRGS as a Professor and got tenure 3 years later. During 2006 and 2007 she had a fellowship from the Alexander von Humboldt Foundation at the University of Würzburg (Germany). She is affiliated with the research groups on Artificial Intelligence and Multi-Agent Systems at UFRGS. Her research interests include: Game-Theoretic Paradigms for Coordination of Agents, Multiagent Learning, Coordination and Cooperation in MAS, Agent-Based Simulation, RoboCup Rescue, and Traffic Simulation and Control. Other professional activities: associate editor of the journal Advances in Complex Systems, chair of program committee for the 17th Braz. Symp. on Artificial Intelligence (2004), and co-organizer of workshop series on Agents in Traffic and Transportation.

Publications

Multiagent Truth Maintenance Applied to a Tourism Recommender System
Fabiana Lorenzi, Ana L.C. Bazzan, Mara Abel. © 2010. 19 pages.
This chapter presents a multiagent recommender system applied to the tourism domain. The multiagent approach is able to deal with distributed expert knowledge to support travel...
Multi-Agent Systems for Traffic and Transportation Engineering
Ana Bazzan, Franziska Klügl. © 2009. 446 pages.
Our increasing societal demand for mobility now challenges researchers to devise more efficient traffic and transportation systems. Multi-Agent Systems for Traffic and...
Multiagent Learning on Traffic Lights Control: Effects of Using Shared Information
Denise de Oliveira, Ana L.C. Bazzan. © 2009. 15 pages.
In a complex multiagent system, agents may have different partial information about the system’s state and the information held by other agents in the system. In a distributed...
Task Allocation in Case-Based Recommender Systems: A Swarm Intelligence Approach
Fabiana Lorenzi, Daniela Scherer dos Santos, Denise de Oliveira, Ana L.C. Bazzan. © 2007. 12 pages.
Case-based recommender systems can learn about user preferences over time and automatically suggest products that fit these preferences. In this chapter, we present such a...