Ava Pettet is currently a visiting scholar at the Institute for Software Integrated Systems. She received her Ph.D. in computer science at Vanderbilt University in 2022. She completed her undergraduate studies in computer science at Vanderbilt University in May 2016.

Ava Pettet Publications

A. Sivagnanam, A. Pettet, H. Lee, A. Mukhopadhyay, A. Dubey, and A. Laszka, Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing, in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
```
@inproceedings{sivagnanam2024,
  title = {Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing},
  author = {Sivagnanam, Amutheezan and Pettet, Ava and Lee, Hunter and Mukhopadhyay, Ayan and Dubey, Abhishek and Laszka, Aron},
  year = {2024},
  booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML)},
  location = {Vienna, Austria},
  publisher = {JMLR.org},
  series = {ICML'24}
}
```
An emergency responder management (ERM) system dispatches responders, such as ambulances, when it receives requests for medical aid. ERM systems can also proactively reposition responders between predesignated waiting locations to cover any gaps that arise due to the prior dispatch of responders or significant changes in the distribution of anticipated requests. Optimal repositioning is computationally challenging due to the exponential number of ways to allocate responders between locations and the uncertainty in future requests. The state-of-the-art approach in proactive repositioning is a hierarchical approach based on spatial decomposition and online Monte Carlo tree search, which may require minutes of computation for each decision in a domain where seconds can save lives. We address the issue of long decision times by introducing a novel reinforcement learning (RL) approach, based on the same hierarchical decomposition, but replacing online search with learning. To address the computational challenges posed by large, variable-dimensional, and discrete state and action spaces, we propose: (1) actor-critic based agents that incorporate transformers to handle variable-dimensional states and actions, (2) projections to fixed-dimensional observations to handle complex states, and (3) combinatorial techniques to map continuous actions to discrete allocations. We evaluate our approach using realworld data from two U.S. cities, Nashville, TN and Seattle, WA. Our experiments show that compared to the state of the art, our approach reduces computation time per decision by three orders of magnitude, while also slightly reducing average ambulance response time by 5 seconds.
J. P. Talusan, R. Sen, A. K. Ava Pettet, Y. Suzue, L. Pedersen, A. Mukhopadhyay, and A. Dubey, OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
```
@inproceedings{talusan2024smartcomp,
  title = {OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization},
  author = {Talusan, Jose Paolo and Sen, Rishav and Ava Pettet, Aaron Kandel and Suzue, Yoshinori and Pedersen, Liam and Mukhopadhyay, Ayan and Dubey, Abhishek},
  year = {2024},
  month = jun,
  booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
  volume = {},
  number = {}
}
```
The increasing popularity of electronic vehicles has spurred a demand for EV charging infrastructure. In the United States alone, over 160,000 public and private charging ports have been installed. This has stoked fear of potential grid issues in the future. Meanwhile, companies, specifically building owners are also seeing the opportunity to leverage EV batteries as energy stores to serve as buffers against the electric grid. The main idea is to influence and control charging behavior to provide a certain level of energy resiliency and demand responsiveness to the building from grid events while ensuring that they meet the demands of EV users. However, managing and co-optimizing energy requirements of EVs and cost-saving measures of building owners is a difficult task. First, user behavior and grid uncertainty contribute greatly to the potential effectiveness of different policies. Second, different charger configurations can have drastically different effects on the cost. Therefore, we propose a complete end-to-end discrete event simulator for vehicle-to-building charging optimization. This software is aimed at building owners and EV manufacturers such as Nissan, looking to deploy their charging stations with state-of-the-art optimization algorithms. We provide a complete solution that allows the owners to train, evaluate, introduce uncertainty, and benchmark policies on their datasets. Lastly, we discuss the potential for extending our work with other vehicle-to-grid deployments.
A. Pettet, Y. Zhang, B. Luo, K. Wray, H. Baier, A. Laszka, A. Dubey, and A. Mukhopadhyay, Decision Making in Non-Stationary Environments with Policy-Augmented Search, Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2024, Auckland, New Zealand. 2024.
```
@misc{pettet2024decision,
  title = {Decision Making in Non-Stationary Environments with Policy-Augmented Search},
  author = {Pettet, Ava and Zhang, Yunuo and Luo, Baiting and Wray, Kyle and Baier, Hendrik and Laszka, Aron and Dubey, Abhishek and Mukhopadhyay, Ayan},
  year = {2024},
  booktitle = {Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2024, Auckland, New Zealand},
  location = {Auckland, New Zealand},
  numpages = {9}
}
```
Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action trajectories at decision time. Decision-making is particularly challenging in non-stationary environments, where the environment in which an agent operates can change over time. Both approaches have shortcomings in such settings – on the one hand, policies learned before execution become stale when the environment changes and relearning takes both time and computational effort. Online search, on the other hand, can return sub-optimal actions when there are limitations on allowed runtime. In this paper, we introduce \textitPolicy-Augmented Monte Carlo tree search (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment. We prove theoretical results showing conditions under which PA-MCTS selects the one-step optimal action and also bound the error accrued while following PA-MCTS as a policy. We compare and contrast our approach with AlphaZero, another hybrid planning approach, and Deep Q Learning on several OpenAI Gym environments. Through extensive experiments, we show that under non-stationary settings with limited time constraints, PA-MCTS outperforms these baselines.
B. Luo, S. Ramakrishna, A. Pettet, C. Kuhn, G. Karsai, and A. Mukhopadhyay, Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems, in Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023), New York, NY, USA, 2023, pp. 177–186.
```
@inproceedings{baiting2023iccps,
  title = {Dynamic Simplex: Balancing Safety and Performance in Autonomous Cyber Physical Systems},
  author = {Luo, Baiting and Ramakrishna, Shreyas and Pettet, Ava and Kuhn, Christopher and Karsai, Gabor and Mukhopadhyay, Ayan},
  year = {2023},
  booktitle = {Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)},
  location = {San Antonio, TX, USA},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {ICCPS '23},
  pages = {177--186},
  doi = {10.1145/3576841.3585934},
  isbn = {9798400700361},
  url = {https://doi.org/10.1145/3576841.3585934},
  numpages = {10}
}
```
Learning Enabled Components (LEC) have greatly assisted cyber-physical systems in achieving higher levels of autonomy. However, LEC’s susceptibility to dynamic and uncertain operating conditions is a critical challenge for the safety of these systems. Redundant controller architectures have been widely adopted for safety assurance in such contexts. These architectures augment LEC "performant" controllers that are difficult to verify with "safety" controllers and the decision logic to switch between them. While these architectures ensure safety, we point out two limitations. First, they are trained offline to learn a conservative policy of always selecting a controller that maintains the system’s safety, which limits the system’s adaptability to dynamic and non-stationary environments. Second, they do not support reverse switching from the safety controller to the performant controller, even when the threat to safety is no longer present. To address these limitations, we propose a dynamic simplex strategy with an online controller switching logic that allows two-way switching. We consider switching as a sequential decision-making problem and model it as a semi-Markov decision process. We leverage a combination of a myopic selector using surrogate models (for the forward switch) and a non-myopic planner (for the reverse switch) to balance safety and performance. We evaluate this approach using an autonomous vehicle case study in the CARLA simulator using different driving conditions, locations, and component failures. We show that the proposed approach results in fewer collisions and higher performance than state-of-the-art alternatives.
G. Pettet, H. Baxter, S. Vazirizade, H. Purohit, M. Ma, A. Mukhopadhyay, and A. Dubey, Designing Decision Support Systems for Emergency Response: Challenges and Opportunities, in 2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER), Los Alamitos, CA, USA, 2022, pp. 30–35.
```
@inproceedings{pettet2022designing,
  author = {Pettet, G. and Baxter, H. and Vazirizade, S. and Purohit, H. and Ma, M. and Mukhopadhyay, A. and Dubey, A.},
  booktitle = {2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER)},
  title = {Designing Decision Support Systems for Emergency Response: Challenges and Opportunities},
  year = {2022},
  volume = {},
  issn = {},
  pages = {30-35},
  keywords = {decision support systems;road accidents;uncertainty;decision making;medical services;emergency services;hazards},
  doi = {10.1109/CPS-ER56134.2022.00012},
  url = {https://doi.ieeecomputersociety.org/10.1109/CPS-ER56134.2022.00012},
  publisher = {IEEE Computer Society},
  address = {Los Alamitos, CA, USA},
  month = may
}
```
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.
G. Pettet, A. Mukhopadhyay, and A. Dubey, Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search. 2022.
```
@misc{pettet2022decision,
  title = {Decision Making in Non-Stationary Environments with Policy-Augmented Monte Carlo Tree Search},
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Dubey, Abhishek},
  year = {2022},
  eprint = {2202.13003},
  preprint = {https://arxiv.org/abs/2202.13003},
  archiveprefix = {arXiv},
  primaryclass = {cs.AI}
}
```
Decision-making under uncertainty (DMU) is present in many important problems. An open challenge is DMU in non-stationary environments, where the dynamics of the environment can change over time. Reinforcement Learning (RL), a popular approach for DMU problems, learns a policy by interacting with a model of the environment offline. Unfortunately, if the environment changes the policy can become stale and take sub-optimal actions, and relearning the policy for the updated environment takes time and computational effort. An alternative is online planning approaches such as Monte Carlo Tree Search (MCTS), which perform their computation at decision time. Given the current environment, MCTS plans using high-fidelity models to determine promising action trajectories. These models can be updated as soon as environmental changes are detected to immediately incorporate them into decision making. However, MCTS’s convergence can be slow for domains with large state-action spaces. In this paper, we present a novel hybrid decision-making approach that combines the strengths of RL and planning while mitigating their weaknesses. Our approach, called Policy Augmented MCTS (PA-MCTS), integrates a policy’s actin-value estimates into MCTS, using the estimates to seed the action trajectories favored by the search. We hypothesize that PA-MCTS will converge more quickly than standard MCTS while making better decisions than the policy can make on its own when faced with nonstationary environments. We test our hypothesis by comparing PA-MCTS with pure MCTS and an RL agent applied to the classical CartPole environment. We find that PC-MCTS can achieve higher cumulative rewards than the policy in isolation under several environmental shifts while converging in significantly fewer iterations than pure MCTS.
M. Wilbur, S. Kadir, Y. Kim, G. Pettet, A. Mukhopadhyay, P. Pugliese, S. Samaranayake, A. Laszka, and A. Dubey, An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services, in ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS), 2022.
```
@inproceedings{wilbur2022,
  title = {An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services},
  booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
  publisher = {IEEE},
  author = {Wilbur, Michael and Kadir, Salah and Kim, Youngseo and Pettet, Geoffrey and Mukhopadhyay, Ayan and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Dubey, Abhishek},
  year = {2022},
  month = apr
}
```
Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to compute near-optimal actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.
G. Pettet, A. Mukhopadhyay, M. J. Kochenderfer, and A. Dubey, Hierarchical Planning for Dynamic Resource Allocation in Smart and Connected Communities, ACM Trans. Cyber-Phys. Syst., vol. 6, no. 4, Nov. 2022.
```
@article{pettet2021hierarchical,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel J. and Dubey, Abhishek},
  title = {Hierarchical Planning for Dynamic Resource Allocation in Smart and Connected Communities},
  year = {2022},
  issue_date = {October 2022},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  volume = {6},
  number = {4},
  issn = {2378-962X},
  url = {https://doi-org.proxy.library.vanderbilt.edu/10.1145/3502869},
  doi = {10.1145/3502869},
  journal = {ACM Trans. Cyber-Phys. Syst.},
  month = nov,
  articleno = {32},
  numpages = {26},
  preprint = {https://arxiv.org/abs/2107.01292},
  keywords = {planning under uncertainty, semi-Markov decision process, large-scale CPS, hierarchical planning, Dynamic resource allocation}
}
```
Resource allocation under uncertainty is a classic problem in city-scale cyber-physical systems. Consider emergency response, where urban planners and first responders optimize the location of ambulances to minimize expected response times to incidents such as road accidents. Typically, such problems involve sequential decision making under uncertainty and can be modeled as Markov (or semi-Markov) decision processes. The goal of the decision maker is to learn a mapping from states to actions that can maximize expected rewards. While online, offline, and decentralized approaches have been proposed to tackle such problems, scalability remains a challenge for real world use cases. We present a general approach to hierarchical planning that leverages structure in city level CPS problems for resource allocation. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then use Monte Carlo planning for solving the smaller problems and managing the interaction between them. Finally, we use data from Nashville, Tennessee, a major metropolitan area in the United States, to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.
A. Mukhopadhyay, G. Pettet, S. M. Vazirizade, D. Lu, A. Jaimes, S. E. Said, H. Baroud, Y. Vorobeychik, M. Kochenderfer, and A. Dubey, A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management, Accident Analysis & Prevention, vol. 165, p. 106501, 2022.
```
@article{mukhopadhyay2021review,
  title = {A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management},
  journal = {Accident Analysis & Prevention},
  volume = {165},
  pages = {106501},
  year = {2022},
  issn = {0001-4575},
  doi = {https://doi.org/10.1016/j.aap.2021.106501},
  url = {https://www.sciencedirect.com/science/article/pii/S0001457521005327},
  author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Vazirizade, Sayyed Mohsen and Lu, Di and Jaimes, Alejandro and Said, Said El and Baroud, Hiba and Vorobeychik, Yevgeniy and Kochenderfer, Mykel and Dubey, Abhishek},
  keywords = {Resource allocation for smart cities, Incident prediction, Computer aided dispatch, Decision making under uncertainty, Accident analysis, Emergency response},
  preprint = {https://arxiv.org/abs/2006.04200}
}
```
In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, and (c) computer-aided dispatch for emergency response. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. We conclude by illustrating open challenges and opportunities for future research in this complex domain.
S. M. Vazirizade, A. Mukhopadhyay, G. Pettet, S. E. Said, H. Baroud, and A. Dubey, Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems, 2021.
```
@inproceedings{vazirizade2021learning,
  title = {Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems},
  author = {Vazirizade, Sayyed Mohsen and Mukhopadhyay, Ayan and Pettet, Geoffrey and Said, Said El and Baroud, Hiba and Dubey, Abhishek},
  year = {2021},
  eprint = {2106.08307},
  archiveprefix = {arXiv},
  tag = {ai4cps,incident},
  preprint = {https://arxiv.org/abs/2106.08307},
  primaryclass = {cs.LG}
}
```
Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, and A. Dubey, Hierarchical Planning for Resource Allocation in Emergency Response Systems, in Proceedings of the 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, Nashville, TN, USA, 2021.
```
@inproceedings{iccps2021,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Dubey, Abhishek},
  title = {Hierarchical Planning for Resource Allocation in Emergency Response Systems},
  booktitle = {Proceedings of the 12th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2021, Nashville, TN, USA},
  year = {2021},
  tag = {ai4cps,decentralization,incident},
  keywords = {emergency},
  project = {smart-cities,smart-emergency-response}
}
```
A classical problem in city-scale cyber-physical systems (CPS) is resource allocation under uncertainty. Spatial-temporal allocation of resources is optimized to allocate electric scooters across urban areas, place charging stations for vehicles, and design efficient on-demand transit. Typically, such problems are modeled as Markov (or semi-Markov) decision processes. While online, offline, and decentralized methodologies have been used to tackle such problems, none of the approaches scale well for large-scale decision problems. We create a general approach to hierarchical planning that leverages structure in city-level CPS problems to tackle resource allocation under uncertainty. We use emergency response as a case study and show how a large resource allocation problem can be split into smaller problems. We then create a principled framework for solving the smaller problems and tackling the interaction between them. Finally, we use real-world data from a major metropolitan area in the United States to validate our approach. Our experiments show that the proposed approach outperforms state-of-the-art approaches used in the field of emergency response.
G. Pettet, M. Ghosal, S. Mahserejian, S. Davis, S. Sridhar, A. Dubey, and M. Meyer, A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling, in 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020.
```
@inproceedings{pettetisgt2020,
  author = {Pettet, Geoffrey and Ghosal, Malini and Mahserejian, Shant and Davis, Sarah and Sridhar, Siddharth and Dubey, Abhishek and Meyer, Michael},
  title = {A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling},
  booktitle = {2020 IEEE Power \& Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
  year = {2020},
  organization = {IEEE},
  tag = {ai4cps,power}
}
```
While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price and grid load. The framework co-simulates models for traffic (Simulation of Urban Mobility) and the electric grid (GridLAB-D), which are used by a decision-theoretic planner to evaluate charging decisions with regard to their long-term effect on grid reliability and cost. We evaluated the framework on a simulation of Richland, WA’s bus and grid network, and found that it could save over $100k per year on operating costs for the city compared to greedy methods.
G. Pettet, A. Mukhopadhyay, M. Kochenderfer, Y. Vorobeychik, and A. Dubey, On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities, in Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2020, Auckland, New Zealand, 2020.
```
@inproceedings{Pettet2020,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Kochenderfer, Mykel and Vorobeychik, Yevgeniy and Dubey, Abhishek},
  title = {On Algorithmic Decision Procedures in Emergency Response Systems in Smart and Connected Communities},
  booktitle = {Proceedings of the 19th Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2020, Auckland, New Zealand},
  year = {2020},
  tag = {ai4cps, decentralization,incident},
  category = {selectiveconference},
  keywords = {emergency, performance},
  project = {smart-emergency-response,smart-cities},
  timestamp = {Wed, 17 Jan 2020 07:24:00 +0200}
}
```
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite its importance, it is common for ERM systems to follow myopic and straight-forward decision policies in the real world. Principled approaches to aid decision-making under uncertainty have been explored in this context but have failed to be accepted into real systems. We identify a key issue impeding their adoption — algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. However, this is not a trivial planning problem - a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is to plan under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both the problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.
A. Mukhopadhyay, G. Pettet, C. Samal, A. Dubey, and Y. Vorobeychik, An online decision-theoretic pipeline for responder dispatch, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 185–196.
```
@inproceedings{Mukhopadhyay2019,
  author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
  title = {An online decision-theoretic pipeline for responder dispatch},
  booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
  year = {2019},
  tag = {ai4cps,incident},
  pages = {185--196},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/iccps/MukhopadhyayPSD19},
  category = {selectiveconference},
  doi = {10.1145/3302509.3311055},
  file = {:Mukhopadhyay2019-An_Online_Decision_Theoretic_Pipeline_for_Responder_Dispatch.pdf:PDF},
  keywords = {emergency},
  project = {smart-cities,smart-emergency-response},
  timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
  url = {https://doi.org/10.1145/3302509.3311055}
}
```
The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.
G. Pettet, A. Mukhopadhyay, C. Samal, A. Dubey, and Y. Vorobeychik, Incident management and analysis dashboard for fire departments: ICCPS demo, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 336–337.
```
@inproceedings{Pettet2019,
  author = {Pettet, Geoffrey and Mukhopadhyay, Ayan and Samal, Chinmaya and Dubey, Abhishek and Vorobeychik, Yevgeniy},
  title = {Incident management and analysis dashboard for fire departments: {ICCPS} demo},
  booktitle = {Proceedings of the 10th {ACM/IEEE} International Conference on Cyber-Physical Systems, {ICCPS} 2019, Montreal, QC, Canada},
  year = {2019},
  pages = {336--337},
  tag = {incident},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/iccps/PettetMSDV19},
  category = {poster},
  doi = {10.1145/3302509.3313329},
  file = {:Pettet2019-Incident_management_and_analysis_dashboard_for_fire_departments_ICCPS_demo.pdf:PDF},
  keywords = {emergency},
  project = {smart-cities,smart-emergency-response},
  timestamp = {Sun, 07 Apr 2019 16:25:36 +0200},
  url = {https://doi.org/10.1145/3302509.3313329}
}
```
This work presents a dashboard tool that helps emergency responders analyze and manage spatial-temporal incidents like crime and traffic accidents. It uses state-of-the-art statistical models to learn incident probabilities based on factors such as prior incidents, time and weather. The dashboard can then present historic and predicted incident distributions. It also allows responders to analyze how moving or adding depots (stations for emergency responders) affects average response times, and can make dispatching recommendations based on heuristics. Broadly, it is a one-stop tool that helps responders visualize historical data as well as plan for and respond to incidents.
G. Pettet, S. Sahoo, and A. Dubey, Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge, in IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, 2019, pp. 511–516.
```
@inproceedings{Pettet2019a,
  author = {Pettet, Geoffrey and Sahoo, Saroj and Dubey, Abhishek},
  title = {Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge},
  booktitle = {{IEEE} International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019},
  year = {2019},
  pages = {511--516},
  tag = {platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/percom/PettetSD19},
  category = {workshop},
  doi = {10.1109/PERCOMW.2019.8730577},
  file = {:Pettet2019a-Towards_an_Adaptive_Multi-Modal_Traffic_Analytics_Framework_at_the_Edge.pdf:PDF},
  keywords = {middleware, transit},
  project = {cps-middleware,smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/PERCOMW.2019.8730577}
}
```
The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application’s high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources.
J. P. Talusan, F. Tiausas, K. Yasumoto, M. Wilbur, G. Pettet, A. Dubey, and S. Bhattacharjee, Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, June 12-15, 2019, 2019, pp. 13–18.
```
@inproceedings{Talusan2019,
  author = {Talusan, Jose Paolo and Tiausas, Francis and Yasumoto, Keiichi and Wilbur, Michael and Pettet, Geoffrey and Dubey, Abhishek and Bhattacharjee, Shameek},
  title = {Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA, June 12-15, 2019},
  year = {2019},
  pages = {13--18},
  tag = {platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/TalusanTYWPDB19},
  category = {workshop},
  doi = {10.1109/SMARTCOMP.2019.00022},
  file = {:Talusan2019-Smart_Transportation_Delay_and_Resiliency_Testbed_Based_on_Information_Flow_of_Things_Middleware.pdf:PDF},
  keywords = {middleware, transit},
  project = {cps-middleware,smart-transit},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00022}
}
```
Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test countermeasure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems.
S. Eisele, G. Pettet, A. Dubey, and G. Karsai, Towards an architecture for evaluating and analyzing decentralized Fog applications, in IEEE Fog World Congress, FWC 2017, Santa Clara, CA, USA, October 30 - Nov. 1, 2017, 2017, pp. 1–6.
```
@inproceedings{Eisele2017,
  author = {Eisele, Scott and Pettet, Geoffrey and Dubey, Abhishek and Karsai, Gabor},
  title = {Towards an architecture for evaluating and analyzing decentralized Fog applications},
  booktitle = {{IEEE} Fog World Congress, {FWC} 2017, Santa Clara, CA, USA, October 30 - Nov. 1, 2017},
  year = {2017},
  tag = {platform,decentralization},
  pages = {1--6},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/fwc/EiselePDK17},
  category = {workshop},
  doi = {10.1109/FWC.2017.8368531},
  file = {:Eisele2017-Towards_an_architecture_for_evaluating_and_analyzing_decentralized_Fog_applications.pdf:PDF},
  keywords = {middleware},
  project = {cps-reliability,cps-middleware},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  url = {https://doi.org/10.1109/FWC.2017.8368531}
}
```
As the number of low cost computing devices at the edge of network increases, there are greater opportunities to enable novel, innovative capabilities, especially in decentralized cyber-physical systems. For example, in an urban setting, a set of networked, collaborating processors at the edge can be used to dynamically detect traffic densities via image processing and then use those densities to control the traffic flow by coordinating traffic light sequences, in a decentralized architecture. In this paper we describe a testbed and an application framework for such applications.
G. Pettet, S. Nannapaneni, B. Stadnick, A. Dubey, and G. Biswas, Incident analysis and prediction using clustering and Bayesian network, in 2017 IEEE SmartWorld, 2017, pp. 1–8.
```
@inproceedings{Pettet2017,
  author = {Pettet, Geoffrey and Nannapaneni, Saideep and Stadnick, Benjamin and Dubey, Abhishek and Biswas, Gautam},
  title = {Incident analysis and prediction using clustering and Bayesian network},
  booktitle = {2017 {IEEE} SmartWorld},
  year = {2017},
  tag = {ai4cps,incident},
  pages = {1--8},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/uic/PettetNSDB17},
  category = {selectiveconference},
  doi = {10.1109/UIC-ATC.2017.8397587},
  file = {:Pettet2017-Incident_analysis_and_prediction_using_clustering_and_Bayesian_network.pdf:PDF},
  keywords = {emergency},
  project = {smart-emergency-response,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
  url = {https://doi.org/10.1109/UIC-ATC.2017.8397587}
}
```
Advances in data collection and storage infrastructure offer an unprecedented opportunity to integrate both data and emergency resources in a city into a dynamic learning system that can anticipate and rapidly respond to heterogeneous incidents. In this paper, we describe integration methods for spatio-temporal incident forecasting using previously collected vehicular accident data provided to us by the Nashville Fire Department. The literature provides several techniques that focus on analyzing features and predicting accidents for specific situations (specific intersections in a city, or certain segments of a freeway, for example), but these models break down when applied to a large, general area consisting of many road and intersection types and other factors like weather conditions. We use Similarity Based Agglomerative Clustering (SBAC) analysis to categorize incidents to account for these variables. Thereafter, we use survival analysis to learn the likelihood of incidents per cluster. The mapping of the clusters to the spatial locations is achieved using a Bayesian network. The prediction methods we have developed lay the foundation for future work on an optimal emergency vehicle allocation and dispatch system in Nashville.

ScopeLab - Design, Operation and Optimization of Smart Cyber-Physical Systems

Ava Pettet Publications

ScopeLab
- Design, Operation and Optimization of Smart Cyber-Physical Systems