John James | Searcharxiv

arXiv2025

Modeling Narrative Structure in Latin Epic Poetry with Automatically Generated Story Grammars

Computational methods for analyzing prose and poetry utilize word embeddings and other abstract representations that sometimes obscure context-rich literary text. Inspired by the psychology of reading, we utilize story structure and elements to simulate human narrative comprehension to produce a more comprehensive representation of literary text. We present a method for automatically generating story grammar labels for input texts as a means of analysis that is interpretable and accessible by humanists and technologists alike. Using a large language model (LLM) pipeline and few-shot learning, we label Latin epic poetry with story element labels and use this output directly to aid an analysis of the story structure and style. Our method guides literary scholars to discover new areas of interest across texts and provides a new feature set for further study for downstream machine learning tasks.

cs.CL↗

arXiv2024

Evaluating Collaborative Autonomy in Opposed Environments using Maritime Capture-the-Flag Competitions

The objective of this work is to evaluate multi-agent artificial intelligence methods when deployed on teams of unmanned surface vehicles (USV) in an adversarial environment. Autonomous agents were evaluated in real-world scenarios using the Aquaticus test-bed, which is a Capture-the-Flag (CTF) style competition involving teams of USV systems. Cooperative teaming algorithms of various foundations in behavior-based optimization and deep reinforcement learning (RL) were deployed on these USV systems in two versus two teams and tested against each other during a competition period in the fall of 2023. Deep reinforcement learning applied to USV agents was achieved via the Pyquaticus test bed, a lightweight gymnasium environment that allows simulated CTF training in a low-level environment. The results of the experiment demonstrate that rule-based cooperation for behavior-based agents outperformed those trained in Deep-reinforcement learning paradigms as implemented in these competitions. Further integration of the Pyquaticus gymnasium environment for RL with MOOS-IvP in terms of configuration and control schema will allow for more competitive CTF games in future studies. As the development of experimental deep RL methods continues, the authors expect that the competitive gap between behavior-based autonomy and deep RL will be reduced. As such, this report outlines the overall competition, methods, and results with an emphasis on future works such as reward shaping and sim-to-real methodologies and extending rule-based cooperation among agents to react to safety and security events in accordance with human experts intent/rules for executing safety and security processes.

cs.RO↗