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pro vyhledávání: '"Witwicki, Stefan"'
Autor:
Delecki, Harrison, Vazquez-Chanlatte, Marcell, Yel, Esen, Wray, Kyle, Arnon, Tomer, Witwicki, Stefan, Kochenderfer, Mykel J.
Model-based planners for partially observable problems must accommodate both model uncertainty during planning and goal uncertainty during objective inference. However, model-based planners may be brittle under these types of uncertainty because they
Externí odkaz:
http://arxiv.org/abs/2402.09388
Expert demonstrations have proven an easy way to indirectly specify complex tasks. Recent algorithms even support extracting unambiguous formal specifications, e.g. deterministic finite automata (DFA), from demonstrations. Unfortunately, these techni
Externí odkaz:
http://arxiv.org/abs/2402.07051
Autor:
Yildiz, Anil, Yel, Esen, Corso, Anthony L., Wray, Kyle H., Witwicki, Stefan J., Kochenderfer, Mykel J.
One of the bottlenecks of training autonomous vehicle (AV) agents is the variability of training environments. Since learning optimal policies for unseen environments is often very costly and requires substantial data collection, it becomes computati
Externí odkaz:
http://arxiv.org/abs/2305.18633
Autor:
Basich, Connor, Svegliato, Justin, Wray, Kyle Hollins, Witwicki, Stefan J., Zilberstein, Shlomo
Publikováno v:
EPTCS 319, 2020, pp. 37-53
Given the complexity of real-world, unstructured domains, it is often impossible or impractical to design models that include every feature needed to handle all possible scenarios that an autonomous system may encounter. For an autonomous system to b
Externí odkaz:
http://arxiv.org/abs/2007.11740
Autor:
Basich, Connor, Svegliato, Justin, Wray, Kyle Hollins, Witwicki, Stefan, Biswas, Joydeep, Zilberstein, Shlomo
Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of
Externí odkaz:
http://arxiv.org/abs/2003.07745
Publikováno v:
Journal of Artificial Intelligence Research, pp. 789-870, AI Access Foundation, Inc., February 2021
Making decisions in complex environments is a key challenge in artificial intelligence (AI). Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods. A body of wor
Externí odkaz:
http://arxiv.org/abs/1907.09278
Autor:
Basich, Connor, Svegliato, Justin, Wray, Kyle H., Witwicki, Stefan, Biswas, Joydeep, Zilberstein, Shlomo
Publikováno v:
In Artificial Intelligence March 2023 316
Recent years have seen the development of methods for multiagent planning under uncertainty that scale to tens or even hundreds of agents. However, most of these methods either make restrictive assumptions on the problem domain, or provide approximat
Externí odkaz:
http://arxiv.org/abs/1502.05443
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Akademický článek
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