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Jérôme Amilhastre

dblp:94/439 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-authorTheory of computation · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Planning, search and constraint satisfaction · 77% Knowledge representation and reasoning · 23%
Theoretical computer science
1 paper
Algorithms and data structures · 50% Graph algorithms and graph theory · 50%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › constraint programming
dynamic constraint satisfaction
0.012002
Consistency restoration and explanations in dynamic CSPs Application to configuration · Artif. Intell. 2002
Algorithms and data structures › combinatorial algorithms
biclique cover
0.011997
Computing a Minimum Biclique Cover is Polynomial for Bipartite Domino-Free Graphs · SODA 1997
Graph algorithms and graph theory
graph classes
0.011997
Computing a Minimum Biclique Cover is Polynomial for Bipartite Domino-Free Graphs · SODA 1997

Methods — techniques the papers use, named apart from their topics

polynomial-time algorithm · 0.0
YearPublicationVenuePosition
2012 Compiling CSPs: A Complexity Map of (Non-Deterministic) Multivalued Decision Diagrams
abstract
Constraint Satisfaction Problems (CSPs) offer a powerful framework for representing a great variety of problems. The difficulty is that most of the requests associated with CSPs are NP-hard. As these requests must be addressed online, Multivalued Decision Diagrams (MDDs) have been proposed as a way to compile CSPs. In the present paper, we draw a compilation map of MDDs, in the spirit of the NNF compilation map, analyzing MDDs according to their succinctness and to their playtime transformations and queries. Deterministic ordered MDDs are a generalization of ordered binary decision diagrams to non-Boolean domains: unsurprisingly, they have similar capabilities. More interestingly, our study puts forward the interest of non-deterministic ordered MDDs: when restricted to Boolean domains, this fragment captures OBDD and DNF as proper subsets and has performances close to those of DNNF. The comparison to classical, deterministic MDDs shows that relaxing the determinism requirement leads to an increase in succinctness and allows more transformations to be satisfied in polytime (typically, the disjunctive ones). Experiments on random problems confirm the gain in succinctness.
Jérôme Amilhastre, Hélène Fargier, Alexandre Niveau, Cédric Pralet
ICTAI1
2002 Consistency restoration and explanations in dynamic CSPs Application to configuration
Jérôme Amilhastre, Hélène Fargier, Pierre Marquis
Artif. Intell.1
1998 Complexity of Minimum Biclique Cover and Minimum Biclique Decomposition for Bipartite Domino-free Graphs
Jérôme Amilhastre, Marie-Catherine Vilarem, Philippe Janssen
Discret. Appl. Math.1
1997 Computing a Minimum Biclique Cover is Polynomial for Bipartite Domino-Free Graphs
Jérôme Amilhastre, Philippe Janssen, Marie-Catherine Vilarem
SODA1