Her primary areas of investigation include Multi-agent system, Simulation, Traffic flow, Artificial intelligence and Operations research. Her study in Multi-agent system is interdisciplinary in nature, drawing from both Domain, Intelligent transportation system, Distributed computing, Autonomous agent and Negotiation. Her Simulation research includes themes of Control theory and Cellular automaton.
Her Traffic flow research is multidisciplinary, relying on both Traffic generation model, Key and Social nature. Her studies examine the connections between Artificial intelligence and genetics, as well as such issues in Machine learning, with regards to Sequence. Her study looks at the relationship between Operations research and topics such as Traffic simulation, which overlap with Intelligent agent, The Internet, Component, Heuristics and Adaptation.
Her main research concerns Artificial intelligence, Multi-agent system, Reinforcement learning, Machine learning and Mathematical optimization. Her Artificial intelligence study integrates concerns from other disciplines, such as Class, Nash equilibrium, Task and Adaptation. Her work in Multi-agent system tackles topics such as Distributed computing which are related to areas like Domain.
Her Reinforcement learning research integrates issues from Control and Simulation. Her work often combines Order and Operations research studies. Operations research is often connected to Traffic flow in her work.
Ana L. C. Bazzan mainly investigates Reinforcement learning, Artificial intelligence, Mathematical optimization, Multi-agent system and Modeling and simulation. Her Reinforcement learning research incorporates themes from Control, Traffic signal, Metaheuristic and Traffic congestion. Her Artificial intelligence research includes elements of Machine learning, Argumentation theory and Set.
Her research integrates issues of Outcome, Regret, Task and Selection in her study of Mathematical optimization. Ana L. C. Bazzan combines Multi-agent system and Scale in her studies. Her Intelligent transportation system research is multidisciplinary, incorporating perspectives in The Internet and Traffic flow.
Ana L. C. Bazzan mostly deals with Reinforcement learning, Artificial intelligence, Mathematical optimization, Traffic congestion and Machine learning. Her study in the fields of Q-learning under the domain of Reinforcement learning overlaps with other disciplines such as Order. Her work focuses on many connections between Artificial intelligence and other disciplines, such as Task, that overlap with her field of interest in Heuristic, Complement and Heuristic.
Her study in Traffic congestion is interdisciplinary in nature, drawing from both Travel time, Traffic simulation, Supply and demand, Adaptation and Multiagent learning. The concepts of her Machine learning study are interwoven with issues in State, Function and Action. Ana L. C. Bazzan works mostly in the field of Regret, limiting it down to topics relating to Hindsight bias and, in certain cases, Multi-agent system, as a part of the same area of interest.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A Distributed Approach for Coordination of Traffic Signal Agents
Ana L. Bazzan.
Autonomous Agents and Multi-Agent Systems (2005)
A review on agent-based technology for traffic and transportation
Ana L. C. Bazzan;Franziska Klügl.
Knowledge Engineering Review (2014)
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Ana L. Bazzan.
Autonomous Agents and Multi-Agent Systems (2009)
Decision dynamics in a traffic scenario
Joachim Wahle;Ana Lúcia C Bazzan;Franziska Klügl;Michael Schreckenberg.
Physica A-statistical Mechanics and Its Applications (2000)
The impact of real-time information in a two-route scenario using agent-based simulation
Joachim Wahle;Ana Lúcia C Bazzan;Franziska Klügl;Michael Schreckenberg.
Transportation Research Part C-emerging Technologies (2002)
Balancing Training Data for Automated Annotation of Keywords: a Case Study.
Gustavo E. A. P. A. Batista;Ana L. C. Bazzan;Maria Carolina Monard.
WOB (2003)
Traffic light control in non-stationary environments based on multi agent Q-learning
Monireh Abdoos;Nasser Mozayani;Ana L. C. Bazzan.
international conference on intelligent transportation systems (2011)
Dealing with non-stationary environments using context detection
Bruno C. da Silva;Eduardo W. Basso;Ana L. C. Bazzan;Paulo M. Engel.
international conference on machine learning (2006)
Agent-Based Modeling and Simulation
Franziska Klügl;Ana Lucia Bazzan.
Ai Magazine (2012)
Using BDI agents to improve driver modelling in a commuter scenario
Rosaldo J.F Rossetti;Rafael H Bordini;Ana L.C Bazzan;Sergio Bampi.
Transportation Research Part C-emerging Technologies (2002)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
Pontifical Catholic University of Rio Grande do Sul
University of Massachusetts Amherst
Free University of Bozen-Bolzano
Technical University of Berlin
University of Illinois at Chicago
University of Duisburg-Essen
University of Technology Sydney
UNSW Sydney
Nanyang Technological University
Federal University of Rio Grande do Sul
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: