Multi-Agent Data Collection with Distributed Stochastic Coordination for Wireless Data Delivery
Robert T. Lattus, John M. Shea (2025)
2025 IEEE International Conference on Machine Learning for Communication and Networking
Abstract: We consider a scenario in which multiple mobile agents are tasked with moving around an area to collect data about some phenomena that occur at random within the prescribed area. The agents must deliver the data by traveling to a location inside the communication range of one of several access points (APs). We consider a fully decentralized setting, in which agents first randomly search the region for phenomena of interest and independently make choices about which APs to travel to once they have data to deliver. In this scenario, multiple agents that observe the same phenomenon may travel to a single AP that they characterize as most ideal, such as in terms of distance or communication capacity. This can cause delays in delivering the data if the communication capacity of the selected AP has to be shared among the agents. If centralized control were used, agents would be assigned to different APs to avoid these delays. We explore the use of stochastic policies to facilitate a form of distributed coordination and demonstrate their advantage over deterministic policies in a simulated environment.
Patricia Solís, Gautam Dasarathy, Pavan Turaga, Alexandria Drake, Kevin Jatin Vora, Akarshan Sjasa, Ankith Raaman, Sarbeswar Praharaj, Robert Lattus (2023)
Examining the COVID Crisis from a Geographical Perspective. Routledge, 2023. 100-123.
Abstract: The uneven outcomes of the COVID-19 pandemic in the United States can be characterized by its patchwork patterns. Given a weak national coordinated response, state-level decisions offer an important frame for analysis. This article explores how such analysis invokes fundamental geographic challenges related to the modified areal unit problem, and results in scientific predictive models that behave differently in different states. We examined morbidity with respect to state-level policy decisions, by comparing the fit and significance of different types of predictive modeling using data from the first wave of 2020. Our research reflects upon public health literature, mathematical modeling, and geographic approaches in the wake of the underlying complex pattern of drivers, decisions, and their impact on public health outcomes state by statetime line. Contemplating these findings, we discuss the need to improve integration of fundamental geographic concepts to creatively develop modeling and interpretations across disciplines that offer value for both informing and holding accountable decision makers of the jurisdictions in which we live.
Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques
Lattus, Robert (2021)
Barrett, the Honors College Thesis, Arizona State University
Abstract: Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one's problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a "global" approach, looking at the data as a whole, and the second is more "local"--partitioning the data at the outset and then building up to estimation of the three Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have signification ramifications of "big data" problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
Coordination of Distributed Agents Through Stochastic Policies in a Cooperative Jamming Scenario (2025)
Multi-Agent Data Collection and Delivery Under Intermittent Sensing with Deep Reinforcement Learning (2025)