Machine Learning is a science that enables machines (especially computers) to learn from environments and make own decisions.
At Machine Learning Laboratory, we carry out research and develop different theoretical foundations for machine learning such as:
How machines should help in planning activities by learning from environments.
How machines should learn in the presence of noisy environment.
How learning gets affected if different machine learning algorithms are trying compete instead of cooperating.
Role of deep learning in planning, reinforcement learning and game theory.
Artificial Intelligence and Multi Agent Systems
Intelligent Transportation, Vehicle Dynamics, Real Time Systems
As the world increasingly moves towards automated transportation newer algorithms and protocols need to be developed for such systems to be widely adopted.This work looks at a variety of aspects of intelligent transportation without necessarily getting into hardware issues. Some of the aspects examined so far include how to route an ambulance or a specific vehicle better given that vehicles are equipped to communicate, how to model vehicles with different characteristics as agents and study their behaviors in variety of circumstances and others.
Type of Work
Algorithm, Software system
Current State of work
New solutions developed and further research in progress
1. Planning for Intelligent vehicles
2. Handling medical emergency
1. Effect of human behavior on traffic patterns during an emergency, ITSC 2016
2. Analysis of Lane Level Dynamics for Emergency Vehicle Traversal, AAMAS 2016