Zobrazeno 1 - 10
of 146
pro vyhledávání: '"Rina Dechter"'
Autor:
Rina Dechter
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without e
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 26:1895-1901
The paper focuses on finding the m best solutions to combinatorial optimization problems using Best-First or Branchand- Bound search. Specifically, we present m-A*, extending the well-known A* to the m-best task, and prove that all its desirable prop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d92069a99d04d735101f5768d572d7a
https://doi.org/10.1609/aaai.v26i1.8405
https://doi.org/10.1609/aaai.v26i1.8405
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 6:171-175
In empirical studies we observed that caching can have very little impact in reducing the search effort in Branch and Bound search over context-minimal OR spaces. For example, in one of the problem domains used in our experiments we reduce only by 1%
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4172869477e7c337f9f493b471977180
https://doi.org/10.1609/socs.v6i1.18381
https://doi.org/10.1609/socs.v6i1.18381
Publikováno v:
Proceedings of the International Symposium on Combinatorial Search. 5:71-79
Weighted search was explored significantly in recent years for path-finding problems, but until now was barely considered for optimization tasks such as MPE/MAP and Weighted CSPs. An important virtue of weighted search schemes, especially in the cont
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5011853c738fb86c363e331138726f5c
https://doi.org/10.1609/socs.v5i1.18329
https://doi.org/10.1609/socs.v5i1.18329
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:12158-12165
Influence diagrams provide a modeling and inference framework for sequential decision problems, representing the probabilistic knowledge by a Bayesian network and the preferences of an agent by utility functions over the random variables and decision
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c85ba186c6225187c158bdc58d346546
https://doi.org/10.1609/aaai.v35i13.17443
https://doi.org/10.1609/aaai.v35i13.17443
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:12147-12157
Influence diagrams (IDs) are graphical models for representing and reasoning with sequential decision-making problems under uncertainty. Limited memory influence diagrams (LIMIDs) model a decision-maker (DM) who forgets the history in the course of m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1fdcd6cba26cf9974817c8a844c4e29
https://doi.org/10.1609/aaai.v35i13.17442
https://doi.org/10.1609/aaai.v35i13.17442
Publikováno v:
IJCAI
Bucket Elimination (BE) is a universal inference scheme that can solve most tasks over probabilistic and deterministic graphical models exactly. However, it often requires exponentially high levels of memory (in the induced-width) preventing its exec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81acb8ea92aa123f7c91fe87df444b3a
https://doi.org/10.24963/ijcai.2021/582
https://doi.org/10.24963/ijcai.2021/582
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
Computing the partition function of a graphical model is a fundamental task in probabilistic inference. Variational bounds and Monte Carlo methods, two important approximate paradigms for this task, each has its respective strengths for solving diffe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0040ebcfe6a4ee8ffdb8378ca6a6868f
https://doi.org/10.1609/aaai.v33i01.33017900
https://doi.org/10.1609/aaai.v33i01.33017900
Publikováno v:
AAAI
Scopus-Elsevier
Scopus-Elsevier
Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art solvers for this task are based on a hybrid best-first and depth-first search scheme that allows them to compute upper and lower bounds on the optimal so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e9560ef96b28c2e782a623f26f7980b
https://doi.org/10.1609/aaai.v33i01.33017924
https://doi.org/10.1609/aaai.v33i01.33017924
Autor:
Rina Dechter
Publikováno v:
Synthesis Lectures on Artificial Intelligence and Machine Learning. 13:1-199
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec9e71bfae03f8dd8468fe0405208341
https://doi.org/10.2200/s00893ed2v01y201901aim041
https://doi.org/10.2200/s00893ed2v01y201901aim041