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Emanuel Ramneantu

dblp:264/5096 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Theory of computation · 2 · 1 since 2021Software engineering, systems software and programming languages · 1

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
2 papers
Algorithmic game theory and mechanism design · 48% Automata and formal languages · 28% Automated reasoning and model checking · 24%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 9 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Algorithmic game theory and mechanism design › stochastic games
simple stochastic games
0.612022
Comparison of algorithms for simple stochastic games · Inf. Comput. 2022
Algorithmic game theory and mechanism design
stochastic games
0.612022
Comparison of algorithms for simple stochastic games · Inf. Comput. 2022
Automated reasoning and model checking › probabilistic verification
value iteration
0.612022
Comparison of algorithms for simple stochastic games · Inf. Comput. 2022
Computing education
problem generation
0.412020
Automata Tutor v3 · CAV (2) 2020
Automata and formal languages › formal grammars
context-free grammar
0.112020
Automata Tutor v3 · CAV (2) 2020
Automata and formal languages
finite automata
0.112020
Automata Tutor v3 · CAV (2) 2020
Automata and formal languages
pushdown automata
0.112020
Automata Tutor v3 · CAV (2) 2020
Automata and formal languages › regular languages
regular expressions
0.112020
Automata Tutor v3 · CAV (2) 2020
Automata and formal languages
turing machines
0.112020
Automata Tutor v3 · CAV (2) 2020

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

value iteration · 0.6strategy iteration · 0.6quadratic programming · 0.6
YearPublicationVenuePosition
2022 Comparison of algorithms for simple stochastic games
abstract
Simple stochastic games are turn-based 2½-player zero-sum graph games with a reachability objective. The problem is to compute the winning probabilities as well as the optimal strategies of both players. In this paper, we compare the three known classes of algorithms – value iteration, strategy iteration and quadratic programming – both theoretically and practically. Further, we suggest several improvements for all algorithms, including the first approach based on quadratic programming that avoids transforming the stochastic game to a stopping one. Our extensive experiments show that these improvements can lead to significant speed-ups. We implemented all algorithms in PRISM-games 3.0, thereby providing the first implementation of quadratic programming for solving simple stochastic games.
Jan Kretínský, Emanuel Ramneantu, Alexander Slivinskiy, Maximilian Weininger
Inf. Comput.2
2020 Automata Tutor v3
abstract
Computer science class enrollments have rapidly risen in the past decade. With current class sizes, standard approaches to grading and providing personalized feedback are no longer possible and new techniques become both feasible and necessary. In this paper, we present the third version of Automata Tutor, a tool for helping teachers and students in large courses on automata and formal languages. The second version of Automata Tutor supported automatic grading and feedback for finite-automata constructions and has already been used by thousands of users in dozens of countries. This new version of Automata Tutor supports automated grading and feedback generation for a greatly extended variety of new problems, including problems that ask students to create regular expressions, context-free grammars, pushdown automata and Turing machines corresponding to a given description, and problems about converting between equivalent models - e.g., from regular expressions to nondeterministic finite automata. Moreover, for several problems, this new version also enables teachers and students to automatically generate new problem instances. We also present the results of a survey run on a class of 950 students, which shows very positive results about the usability and usefulness of the tool.
Loris D'Antoni, Martin Helfrich, Jan Kretínský, Emanuel Ramneantu, Maximilian Weininger
CAV (2)4