What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Programming language
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 most cited work include:
- A Distributed Approach for Coordination of Traffic Signal Agents (203 citations)
- Decision dynamics in a traffic scenario (190 citations)
- A review on agent-based technology for traffic and transportation (152 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (31.58%)
- Multi-agent system (21.43%)
- Reinforcement learning (20.30%)
What were the highlights of her more recent work (between 2016-2021)?
- Reinforcement learning (20.30%)
- Artificial intelligence (31.58%)
- Mathematical optimization (10.53%)
In recent papers she was focusing on the following fields of study:
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.
Between 2016 and 2021, her most popular works were:
- Optimal Electric Vehicle Fast Charging Station Placement Based on Game Theoretical Framework (70 citations)
- Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence (26 citations)
- A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems (14 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
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.
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