Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userPushing undecidability of the isolation problem for probabilistic automata
174
0
0.0
(
0
)
Added by
Nathanael Fijalkow
Publication date
2011
fields
Informatics Engineering
and research's language is
English
Authors
Nathanael Fijalkow
Ask ChatGPT about the research
No Arabic abstract
This short note aims at proving that the isolation problem is undecidable for probabilistic automata with only one probabilistic transition. This problem is known to be undecidable for general probabilistic automata, without restriction on the number of probabilistic transitions. In this note, we develop a simulation technique that allows to simulate any probabilistic automaton with one having only one probabilistic transition.
rate research
Read More
A weight normalization procedure, commonly called pushing, is introduced for weighted tree automata (wta) over commutative semifields. The normalization preserves the recognized weighted tree language even for nondeterministic wta, but it is most useful for bottom-up deterministic wta, where it can be used for minimization and equivalence testing. In both applications a careful selection of the weights to be redistributed followed by normalization allows a reduction of the general problem to the corresponding problem for bottom-up deterministic unweighted tree automata. This approach was already successfully used by Mohri and Eisner for the minimization of deterministic weighted string automata. Moreover, the new equivalence test for two wta $M$ and $M$ runs in time $mathcal O((lvert M rvert + lvert Mrvert) cdot log {(lvert Qrvert + lvert Qrvert)})$, where $Q$ and $Q$ are the states of $M$ and $M$, respectively, which improves the previously best run-time $mathcal O(lvert M rvert cdot lvert Mrvert)$.
The probabilistic bisimilarity distance of Deng et al. has been proposed as a robust quantitative generalization of Segala and Lynchs probabilistic bisimilarity for probabilistic automata. In this paper, we present a characterization of the bisimilarity distance as the solution of a simple stochastic game. The characterization gives us an algorithm to compute the distances by applying Condons simple policy iteration on these games. The correctness of Condons approach, however, relies on the assumption that the games are stopping. Our games may be non-stopping in general, yet we are able to prove termination for this extended class of games. Already other algorithms have been proposed in the literature to compute these distances, with complexity in $textbf{UP} cap textbf{coUP}$ and textbf{PPAD}. Despite the theoretical relevance, these algorithms are inefficient in practice. To the best of our knowledge, our algorithm is the first practical solution. The characterization of the probabilistic bisimilarity distance mentioned above crucially uses a dual presentation of the Hausdorff distance due to Memoli. As an additional contribution, in this paper we show that Memolis result can be used also to prove that the bisimilarity distance bounds the difference in the maximal (or minimal) probability of two states to satisfying arbitrary $omega$-regular properties, expressed, eg., as LTL formulas.
In [1], we introduced the weakly synchronizing languages for probabilistic automata. In this report, we show that the emptiness problem of weakly synchronizing languages for probabilistic automata is undecidable. This implies that the decidability result of [1-3] for the emptiness problem of weakly synchronizing language is incorrect.
We characterize the class of nondeterministic ${omega}$-automata that can be used for the analysis of finite Markov decision processes (MDPs). We call these automata `good-for-MDPs (GFM). We show that GFM automata are closed under classic simulation as well as under more powerful simulation relations that leverage properties of optimal control strategies for MDPs. This closure enables us to exploit state-space reduction techniques, such as those based on direct and delayed simulation, that guarantee simulation equivalence. We demonstrate the promise of GFM automata by defining a new class of automata with favorable properties - they are Buchi automata with low branching degree obtained through a simple construction - and show that going beyond limit-deterministic automata may significantly benefit reinforcement learning.
Subzero automata is a class of tree automata whose acceptance condition can express probabilistic constraints. Our main result is that the problem of determining if a subzero automaton accepts some regular tree is decidable.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
