15 citations · 42 across the 44 of their papers we have counts for
45 papers
Planning, Scheduling, and Behavior in EV Charging Systems: A Critical Survey and Trilemma Framework
Peiyan Xiao, Yuheng Li, Ayan Mukhopadhyay +3
The rapid growth of electric vehicles is shifting the main constraint on transport electrification from vehicle adoption to the deployment and operation of charging infrastructure.…
Toward Template-Free Explainability for Monte Carlo Tree Search
Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud +3
Probabilistic search algorithms, such as Monte Carlo Tree Search (MCTS), have proven very effective in solving sequential decision-making tasks under uncertainty. However, interpre…
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Saurabh Bagchi, Hyunseung Kim, Tarek Abdelzaher +20
Resilience in cyber-physical systems (CPS) is the fundamental ability to maintain safety and critical functionality despite adverse "perturbations," which includes security attacks…
Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests
Amutheezan Sivagnanam, Ayan Mukhopadhyay, Samitha Samaranayake +2
Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing proble…
In-Context Planning with Latent Temporal Abstractions
Baiting Luo, Yunuo Zhang, Nathaniel S. Keplinger +3
Planning-based reinforcement learning for continuous control is bottlenecked by two practical issues: planning at primitive time scales leads to prohibitive branching and long hori…
Ensemble Prediction of Task Affinity for Efficient Multi-Task Learning
Afiya Ayman, Ayan Mukhopadhyay, Aron Laszka
A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks…
CONSENT: A Negotiation Framework for Leveraging User Flexibility in Vehicle-to-Building Charging under Uncertainty
Rishav Sen, Fangqi Liu, Jose Paolo Talusan +5
The growth of Electric Vehicles (EVs) creates a conflict in vehicle-to-building (V2B) settings between building operators, who face high energy costs from uncoordinated charging, a…
Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit
Rishav Sen, Amutheezan Sivagnanam, Aron Laszka +2
The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. Howev…
Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems
Rishav Sen, Yunuo Zhang, Fangqi Liu +5
Vehicle-to-building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital contro…
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +2
In Partially Observable Markov Decision Processes (POMDPs), maintaining and updating belief distributions over possible underlying states provides a principled way to summarize act…
MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data
Rishav Sen, Jose Paolo Talusan, Abhishek Dubey +3
High-resolution origin-destination (OD) tables are essential for a wide spectrum of transportation applications, from modeling traffic and signal timing optimization to congestion…
Combining LLMs with Logic-Based Framework to Explain MCTS
Ziyan An, Xia Wang, Hendrik Baier +6
In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural langu…
Observation Adaptation via Annealed Importance Resampling for Partially Observable Markov Decision Processes
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +1
Partially observable Markov decision processes (POMDPs) are a general mathematical model for sequential decision-making in stochastic environments under state uncertainty. POMDPs a…
Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction
Baiting Luo, Ava Pettet, Aron Laszka +2
Sequential decision-making in high-dimensional continuous action spaces, particularly in stochastic environments, faces significant computational challenges. We explore this challe…
Reinforcement Learning-based Approach for Vehicle-to-Building Charging with Heterogeneous Agents and Long Term Rewards
Fangqi Liu, Rishav Sen, Jose Paolo Talusan +5
Strategic aggregation of electric vehicle batteries as energy reservoirs can optimize power grid demand, benefiting smart and connected communities, especially large office buildin…
NS-Gym: Open-Source Simulation Environments and Benchmarks for Non-Stationary Markov Decision Processes
Nathaniel S. Keplinger, Baiting Luo, Iliyas Bektas +5
In many real-world applications, agents must make sequential decisions in environments where conditions are subject to change due to various exogenous factors. These non-stationary…
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
Yunuo Zhang, Baiting Luo, Ayan Mukhopadhyay +8
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban…
Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic
Ziyan An, Hendrik Baier, Abhishek Dubey +2
Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation…
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing
Amutheezan Sivagnanam, Ava Pettet, Hunter Lee +3
An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition res…
Forecasting and Mitigating Disruptions in Public Bus Transit Services
Chaeeun Han, Jose Paolo Talusan, Dan Freudberg +3
Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disru…
An Online Approach to Solving Public Transit Stationing and Dispatch Problem
Jose Paolo Talusan, Chaeeun Han, Ayan Mukhopadhyay +3
Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is…
Deploying ADVISER: Impact and Lessons from Using Artificial Intelligence for Child Vaccination Uptake in Nigeria
Opadele Kehinde, Ruth Abdul, Bose Afolabi +4
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring i…
Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processes
Baiting Luo, Yunuo Zhang, Abhishek Dubey +1
A fundamental (and largely open) challenge in sequential decision-making is dealing with non-stationary environments, where exogenous environmental conditions change over time. Suc…
Decision Making in Non-Stationary Environments with Policy-Augmented Search
Ava Pettet, Yunuo Zhang, Baiting Luo +5
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (…