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pro vyhledávání: '"Baier, Jorge A."'
A popular approach for sequential decision-making is to perform simulator-based search guided with Machine Learning (ML) methods like policy learning. On the other hand, model-relaxation heuristics can guide the search effectively if a full declarati
Externí odkaz:
http://arxiv.org/abs/2207.04479
Publikováno v:
In Proceedings of the International Symposium on Combinatorial Search (Vol. 12, No. 1, pp. 2-10) 2021
Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be giv
Externí odkaz:
http://arxiv.org/abs/2104.10535
Autor:
Hernández, Carlos, Yeoh, William, Baier, Jorge A., Zhang, Han, Suazo, Luis, Koenig, Sven, Salzman, Oren
Publikováno v:
In Artificial Intelligence January 2023 314
Autor:
Baier, Jorge A., McIlraith, Sheila A.
Publikováno v:
In Artificial Intelligence February 2022 303
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
The knowledge representation community has built general-purpose ontologies which contain large amounts of commonsense knowledge over relevant aspects of the world, including useful visual information, e.g.: "a ball is used by a football player", "a
Externí odkaz:
http://arxiv.org/abs/1705.08844
Semantic Web, and its underlying data format RDF, lend themselves naturally to navigational querying due to their graph-like structure. This is particularly evident when considering RDF data on the Web, where various separately published datasets ref
Externí odkaz:
http://arxiv.org/abs/1701.06454
LTL synthesis -- the construction of a function to satisfy a logical specification formulated in Linear Temporal Logic -- is a 2EXPTIME-complete problem with relevant applications in controller synthesis and a myriad of artificial intelligence applic
Externí odkaz:
http://arxiv.org/abs/1609.04371
Autor:
Baier, Jorge A.
Automated planning is a branch of AI that addresses the problem of generating a course of action to achieve a specified objective, given an initial state of the world. It is an area that is central to the development of intelligent agents and autonom
Externí odkaz:
http://hdl.handle.net/1807/24336
Akademický článek
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Autor:
Hernández, Carlos, Baier, Jorge A
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 43, pages 523-570, 2012
Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-tim
Externí odkaz:
http://arxiv.org/abs/1401.5854