Zobrazeno 1 - 10
of 241
pro vyhledávání: '"Schewe, Sven"'
Autor:
Vasylenko, Andrij, Antypov, Dmytro, Schewe, Sven, Daniels, Luke M., Claridge, John B., Dyer, Matthew S., Rosseinsky, Matthew J.
Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongly affected by the choice of the numerical representation for describin
Externí odkaz:
http://arxiv.org/abs/2408.02292
We propose DFAMiner, a passive learning tool for learning minimal separating deterministic finite automata (DFA) from a set of labelled samples. Separating automata are an interesting class of automata that occurs generally in regular model checking
Externí odkaz:
http://arxiv.org/abs/2405.18871
While discounted payoff games and classic games that reduce to them, like parity and mean-payoff games, are symmetric, their solutions are not. We have taken a fresh view on the properties that optimal solutions need to have, and devised a novel way
Externí odkaz:
http://arxiv.org/abs/2404.04124
Autor:
Hahn, Ernst Moritz, Perez, Mateo, Schewe, Sven, Somenzi, Fabio, Trivedi, Ashutosh, Wojtczak, Dominik
Regular decision processes (RDPs) are a subclass of non-Markovian decision processes where the transition and reward functions are guarded by some regular property of the past (a lookback). While RDPs enable intuitive and succinct representation of n
Externí odkaz:
http://arxiv.org/abs/2312.08602
Publikováno v:
EPTCS 390, 2023, pp. 203-219
While discounted payoff games and classic games that reduce to them, like parity and mean-payoff games, are symmetric, their solutions are not. We have taken a fresh view on the constraints that optimal solutions need to satisfy, and devised a novel
Externí odkaz:
http://arxiv.org/abs/2310.01008
Autor:
Hahn, Ernst Moritz, Perez, Mateo, Schewe, Sven, Somenzi, Fabio, Trivedi, Ashutosh, Wojtczak, Dominik
Reinforcement learning (RL) is a powerful approach for training agents to perform tasks, but designing an appropriate reward mechanism is critical to its success. However, in many cases, the complexity of the learning objectives goes beyond the capab
Externí odkaz:
http://arxiv.org/abs/2308.07469
Autor:
Schewe, Sven, Tang, Qiyi
Good-for-MDPs and good-for-games automata are two recent classes of nondeterministic automata that reside between general nondeterministic and deterministic automata. Deterministic automata are good-for-games, and good-for-games automata are good-for
Externí odkaz:
http://arxiv.org/abs/2307.11483
Families of DFAs (FDFAs) have recently been introduced as a new representation of $\omega$-regular languages. They target ultimately periodic words, with acceptors revolving around accepting some representation $u\cdot v^\omega$. Three canonical FDFA
Externí odkaz:
http://arxiv.org/abs/2307.07490
We introduce a method for translating an alternating weak B\"uchi automaton (AWA), which corresponds to a Linear Dynamic Logic (LDL) formula, to an unambiguous B\"uchi automaton (UBA). Our translations generalise constructions for Linear Temporal Log
Externí odkaz:
http://arxiv.org/abs/2305.09966
Publikováno v:
Logical Methods in Computer Science, Volume 20, Issue 4 (October 2, 2024) lmcs:11166
We explore the notion of history-determinism in the context of timed automata (TA) over infinite timed words. History-deterministic (HD) automata are those in which nondeterminism can be resolved on the fly, based on the run constructed thus far. His
Externí odkaz:
http://arxiv.org/abs/2304.03183