Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Sunberg, Zachary"'
Partially Observable Markov Decision Processes (POMDPs) provide a structured framework for decision-making under uncertainty, but their application requires efficient belief updates. Sequential Importance Resampling Particle Filters (SIRPF), also kno
Externí odkaz:
http://arxiv.org/abs/2409.16392
Partially Observable Markov Decision Processes (POMDPs) are powerful models for sequential decision making under transition and observation uncertainties. This paper studies the challenging yet important problem in POMDPs known as the (indefinite-hor
Externí odkaz:
http://arxiv.org/abs/2406.02871
Autor:
Becker, Tyler, Sunberg, Zachary
Many real-world decision problems involve interaction of multiple self-interested agents with limited sensing ability. The partially observable stochastic game (POSG) provides a mathematical framework for posing these problems, however solving a POSG
Externí odkaz:
http://arxiv.org/abs/2405.18703
As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aims to reduce confusion and foster trust in systems by providing explana
Externí odkaz:
http://arxiv.org/abs/2403.19760
Quality colormaps can help communicate important data patterns. However, finding an aesthetically pleasing colormap that looks "just right" for a given scenario requires significant design and technical expertise. We introduce Cieran, a tool that all
Externí odkaz:
http://arxiv.org/abs/2402.15997
In this paper, we present a controller framework that synthesizes control policies for Jump Markov Linear Systems subject to stochastic mode switches and imperfect mode estimation. Our approach builds on safe and robust methods for Model Predictive C
Externí odkaz:
http://arxiv.org/abs/2310.14116
Autor:
Ho, Qi Heng, Becker, Tyler, Kraske, Benjamin, Laouar, Zakariya, Feather, Martin S., Rossi, Federico, Lahijanian, Morteza, Sunberg, Zachary N.
Many sequential decision problems involve optimizing one objective function while imposing constraints on other objectives. Constrained Partially Observable Markov Decision Processes (C-POMDP) model this case with transition uncertainty and partial o
Externí odkaz:
http://arxiv.org/abs/2310.09688
Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search \& rescue missions, require nearly continuous human supervision for vehicle guidance and operation. These methods do not co
Externí odkaz:
http://arxiv.org/abs/2309.06395
As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success. Ensuring users understand the models over which algorithms reason promotes us
Externí odkaz:
http://arxiv.org/abs/2305.00931
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 931-938, 2024
This paper introduces a sampling-based strategy synthesis algorithm for nondeterministic hybrid systems with complex continuous dynamics under temporal and reachability constraints. We model the evolution of the hybrid system as a two-player game, wh
Externí odkaz:
http://arxiv.org/abs/2304.06876