Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Kolb, Samuel"'
Propositional model counting (#SAT) can be solved efficiently when the input formula is in deterministic decomposable negation normal form (d-DNNF). Translating an arbitrary formula into a representation that allows inference tasks, such as counting,
Externí odkaz:
http://arxiv.org/abs/2306.04541
Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints
Externí odkaz:
http://arxiv.org/abs/2202.03888
Mixed-integer linear programs (MILPs) are widely used in artificial intelligence and operations research to model complex decision problems like scheduling and routing. Designing such programs however requires both domain and modelling expertise. In
Externí odkaz:
http://arxiv.org/abs/2107.07136
Autor:
Gautrais, Clément, Dauxais, Yann, Teso, Stefano, Kolb, Samuel, Verbruggen, Gust, De Raedt, Luc
Everybody wants to analyse their data, but only few posses the data science expertise to to this. Motivated by this observation we introduce a novel framework and system \textsc{VisualSynth} for human-machine collaboration in data science. It wants t
Externí odkaz:
http://arxiv.org/abs/2004.11113
Probabilistic inference in the hybrid domain, i.e. inference over discrete-continuous domains, requires tackling two well known #P-hard problems 1)~weighted model counting (WMC) over discrete variables and 2)~integration over continuous variables. Fo
Externí odkaz:
http://arxiv.org/abs/2001.04566
Publikováno v:
In Artificial Intelligence January 2023 314
Autor:
Gautrais, CléMent, author, Dauxais, Yann, author, Teso, Stefano, author, Kolb, Samuel, author, Verbruggen, Gust, author, Raedt, Luc De, author
Publikováno v:
Human-Like Machine Intelligence, 2021.
Externí odkaz:
https://doi.org/10.1093/oso/9780198862536.003.0019
Publikováno v:
28th International Conference on Principles and Practice of Constraint Programming (CP 2022), LIPIcs
Constraint programming (CP) is used widely for solving real-world problems. However, designing these models require substantial expertise. In this paper, we tackle this problem by synthesizing models automatically from past solutions. We introduce CO
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dc4026e529b91c947f0d657b51464d3
https://doi.org/10.4230/lipics.cp.2022.29
https://doi.org/10.4230/lipics.cp.2022.29
Publikováno v:
28th International Conference on Principles and Practice of Constraint Programming (CP 2022), LIPIcs
Many real-world problems can be effectively solved by means of combinatorial optimization. However, appropriate models to give to a solver are not always available, and sometimes must be learned from historical data. Although some research has been d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f1e720d64d581fcfe27b1036d1a7c61
https://doi.org/10.4230/lipics.cp.2022.8
https://doi.org/10.4230/lipics.cp.2022.8
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:16057-16059
Constraint satisfaction problems (CSPs) are used widely, especially in the field of operations research, to model various real world problems like scheduling or planning. However,modelling a problem as a CSP is not trivial, it is labour intensive and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::666ac578e03172515bcf80c2e0a64a2e
https://doi.org/10.1609/aaai.v35i18.18011
https://doi.org/10.1609/aaai.v35i18.18011