To conceive, analyze and design complex systems, we investigate new approaches that are founded on a fundamental notion of “adaptation”. Adaptation is realized by bringing together nature inspired principles of computation, rigorous engineering design methods, and machine learning.
ADAMS Lab is also looking for exceptional Masters and Undergraduate students who have the motivation to excel in the below areas of scholarly research. A successful student in these areas must have a solid background in mathematics, engineering, and computer programming. Good communication skills and previous research experience are a plus. For further information, click here, or contact Dr. Chowdhury: soumacho@buffalo.edu .
Fundamental areas of research in the ADAMS lab include:
Major areas of application include:
Urban Air Mobility Vertiport ManagementUrban Air Mobility (UAM) promises a new dimension to decongested, safe, and fast travel in urban and suburban hubs. These UAM aircraft are conceived to operate from small airports called vertiports each comprising multiple take-off/landing and battery-recharging spots. Since they might be situated in dense urban areas and need to handle many aircraft landings and take-offs each hour, managing this schedule in real-time becomes challenging for a traditional air-traffic controller but instead calls for an automated solution. We are working on a novel approach to this problem of Urban Air Mobility - Vertiport Schedule Management (UAM-VSM), which leverages graph reinforcement learning to generate decision-support policies. Here the designated physical spots within the vertiport's airspace and the vehicles being managed are… Read More | Neuro-evolutionAdaptive Genomic Evolution of Neural-Network Topologies (AGENT) Topology and weight evolving artificial neural network (TWEANN) algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This paper presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issuespresent in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the Adaptive Genomic Evolution of Neural Network Topologies (AGENT), builds on the neuroevolution ofaugmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and… Read More | Graph Learning Based solution for MRTAAuthor: Steve Paul Efficient multi-robot task allocation (MRTA) is fundamental to various time-sensitive applications such as disaster response, warehouse operations, and construction. MRTA involves using a team of robots to perform tasks that are spatially distributed, and present time deadlines and different workloads that may require multiple visits by robots to complete the task. https://www.youtube.com/watch?v=X-M12l3M43s Here, we proposed encoder-decoder graph neural network involving Capsule networks and multi-head attention mechanism, and innovatively add topological descriptors (TD) as new features to improve transferability to unseen problems of similar and larger size. Persistent homology is used to derive the TD, and proximal policy optimization is used to train our TD-augmented graph neural network. The resulting policy model compares favorably to state-of-the-art non-learning baselines… Read More |