no code implementations • 10 May 2024 • Joyce Fonteles, Eduardo Davalos, Ashwin T. S., Yike Zhang, Mengxi Zhou, Efrat Ayalon, Alicia Lane, Selena Steinberg, Gabriella Anton, Joshua Danish, Noel Enyedy, Gautam Biswas
Investigating children's embodied learning in mixed-reality environments, where they collaboratively simulate scientific processes, requires analyzing complex multimodal data to interpret their learning and coordination behaviors.
1 code implementation • 6 May 2024 • Clayton Cohn, Caitlin Snyder, Justin Montenegro, Gautam Biswas
LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks.
no code implementations • 21 Mar 2024 • Clayton Cohn, Nicole Hutchins, Tuan Le, Gautam Biswas
This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science.
no code implementations • 18 Oct 2023 • Yuhang Zhang, Marcos Quinones-Grueiro, Zhiyao Zhang, Yanbing Wang, William Barbour, Gautam Biswas, Daniel Work
Variable Speed Limit (VSL) control acts as a promising highway traffic management strategy with worldwide deployment, which can enhance traffic safety by dynamically adjusting speed limits according to real-time traffic conditions.
no code implementations • 21 May 2023 • Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we present an approach to supervisory reinforcement learning control for unmanned aerial vehicles (UAVs).
no code implementations • 20 May 2023 • Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
Instead, we propose a model-based transformation, such that when actions from a control policy are applied to the target system, a positive transfer is achieved.
1 code implementation • 19 May 2022 • Luke Bhan, Marcos Quinones-Grueiro, Gautam Biswas
In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters.
no code implementations • 10 Dec 2020 • Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
The enhancement is applied when a new fault occurs, to re-initialize the parameters of a new RL policy that achieves faster adaption with a small number of samples of system behavior with the new fault.
no code implementations • 26 Sep 2020 • Ibrahim Ahmed, Marcos Quinones-Grueiro, Gautam Biswas
This contrasts with MAML, where the controller derives intermediate policies anew, sampled from a distribution of similar systems, to initialize a new policy.
no code implementations • 10 Aug 2020 • Ibrahim Ahmed, Marcos Quiñones-Grueiro, Gautam Biswas
We propose a novel adaptive reinforcement learning control approach for fault tolerant control of degrading systems that is not preceded by a fault detection and diagnosis step.
no code implementations • 10 Aug 2020 • Ibrahim Ahmed, Hamed Khorasgani, Gautam Biswas
A desirable property in fault-tolerant controllers is adaptability to system changes as they evolve during systems operations.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 4 Aug 2020 • Avisek Naug, Marcos Quiñones-Grueiro, Gautam Biswas
We demonstrate this approach for a data-driven 'smart building environment' that we use as a test-bed for developing HVAC controllers for reducing energy consumption of large buildings on our university campus.
no code implementations • 27 Mar 2013 • Gautam Biswas, Teywansh S. Anand
This paper discusses an expert system shell that integrates rule-based reasoning and the Dempster-Shafer evidence combination scheme.
no code implementations • Kinnebrew, J. S., Loretz, K. M., & Biswas, G. (2013). A Contextualized, Differential Sequence Mining Method to Derive Students’ Learning Behavior Patterns. JEDM | Journal of Educational Data Mining, 5(1), 190-219. Retrieved from 2013 • John S. Kinnebrew, Kirk M. Loretz, Gautam Biswas
Computer-based learning environments can produce a wealth of data on student learning interactions.