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Christian Eisentraut

dblp:84/4405 · DBLP profile ↗
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7ranked-venue papers
6as first author
0since 2021 · last 2018
0000-0002-7735-8751ORCID · corroborated

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

Theory of computation · 5 · 4 first-authorSoftware engineering, systems software and programming languages · 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.

Theoretical computer science
3 papers
Automata and formal languages · 54% Logic in computer science · 46%
Artificial intelligence
1 paper
Reinforcement learning · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 100%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automata and formal languages
probabilistic automata		 0.422018
The quest for minimal quotients for probabilistic and Markov automata · Inf. Comput. 2018
On Probabilistic Automata in Continuous Time · LICS 2010
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning
bisimulation		 0.212015
Probabilistic Bisimulation for Realistic Schedulers · FM 2015
Logic in computer science
concurrency theory		 0.222015
On Probabilistic Automata in Continuous Time · LICS 2010
Probabilistic Bisimulation for Realistic Schedulers · FM 2015
Logic in computer science
bisimulation		 0.112010
On Probabilistic Automata in Continuous Time · LICS 2010
Logic in computer science › bisimulation
weak bisimulation		 0.112010
On Probabilistic Automata in Continuous Time · LICS 2010
Automata and formal languages › probabilistic automata
markov automata		 0.112018
The quest for minimal quotients for probabilistic and Markov automata · Inf. Comput. 2018
Logic in computer science › bisimulation
probabilistic bisimulation		 0.112015
Probabilistic Bisimulation for Realistic Schedulers · FM 2015
Performance modeling and evaluation › stochastic petri nets
generalized stochastic petri nets		 0.012010
On Probabilistic Automata in Continuous Time · LICS 2010
Performance modeling and evaluation
stochastic petri nets		 0.012010
On Probabilistic Automata in Continuous Time · LICS 2010

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

probabilistic bisimulation · 0.4compositional behavioral model · 0.2bisimulation · 0.2
YearPublicationVenuePosition
2018 Probabilistic bisimulation for realistic schedulers
Lijun Zhang 0001, Pengfei Yang 0002, Lei Song 0001, Holger Hermanns, Christian Eisentraut, David N. Jansen, Jens Chr. Godskesen
 Acta Informatica5
2018 The quest for minimal quotients for probabilistic and Markov automata
Christian Eisentraut, Holger Hermanns, Johann Schuster, Andrea Turrini, Lijun Zhang 0001
 Inf. Comput.1
2015 Probabilistic Bisimulation for Realistic Schedulers
Christian Eisentraut, Jens Chr. Godskesen, Holger Hermanns, Lei Song 0001, Lijun Zhang 0001
 FM1
2013 A Semantics for Every GSPN
Christian Eisentraut, Holger Hermanns, Joost-Pieter Katoen, Lijun Zhang 0001
 Petri Nets1
2013 The Quest for Minimal Quotients for Probabilistic Automata
Christian Eisentraut, Holger Hermanns, Johann Schuster, Andrea Turrini, Lijun Zhang 0001
 TACAS1
2010 Concurrency and Composition in a Stochastic World
Christian Eisentraut, Holger Hermanns, Lijun Zhang 0001
 CONCUR1
2010 On Probabilistic Automata in Continuous Time
abstract
We develop a compositional behavioural model that integrates a variation of probabilistic automata into a conservative extension of interactive Markov chains. The model is rich enough to embody the semantics of generalised stochastic Petri nets. We define strong and weak bisimulations and discuss their compositionality properties. Weak bisimulation is partly oblivious to the probabilistic branching structure, in order to reflect some natural equalities in this spectrum of models. As a result, the standard way to associate a stochastic process to a generalised stochastic Petri net can be proven sound with respect to weak bisimulation.
Christian Eisentraut, Holger Hermanns, Lijun Zhang 0001
 LICS1