1,044 research outputs found
Path-equivalent developments in acyclic weighted automata
International audienceWeighted finite automata (WFA) are used with FPGA accelerating hardware to scan large genomic banks. Hardwiring such automata raises surface area and clock frequency constraints, requiring efficient ε-transitions-removal techniques. In this paper, we present bounds on the number of new transitions for the development of acyclic WFA, which is a special case of the ε-transitions-removal problem. We introduce a new problem, a partial removal of ε-transitions while accepting short chains of ε-transitions
State Elimination Ordering Strategies: Some Experimental Results
Recently, the problem of obtaining a short regular expression equivalent to a
given finite automaton has been intensively investigated. Algorithms for
converting finite automata to regular expressions have an exponential blow-up
in the worst-case. To overcome this, simple heuristic methods have been
proposed.
In this paper we analyse some of the heuristics presented in the literature
and propose new ones. We also present some experimental comparative results
based on uniform random generated deterministic finite automata.Comment: In Proceedings DCFS 2010, arXiv:1008.127
Complexity of Equivalence and Learning for Multiplicity Tree Automata
We consider the complexity of equivalence and learning for multiplicity tree
automata, i.e., weighted tree automata over a field. We first show that the
equivalence problem is logspace equivalent to polynomial identity testing, the
complexity of which is a longstanding open problem. Secondly, we derive lower
bounds on the number of queries needed to learn multiplicity tree automata in
Angluin's exact learning model, over both arbitrary and fixed fields.
Habrard and Oncina (2006) give an exact learning algorithm for multiplicity
tree automata, in which the number of queries is proportional to the size of
the target automaton and the size of a largest counterexample, represented as a
tree, that is returned by the Teacher. However, the smallest
tree-counterexample may be exponential in the size of the target automaton.
Thus the above algorithm does not run in time polynomial in the size of the
target automaton, and has query complexity exponential in the lower bound.
Assuming a Teacher that returns minimal DAG representations of
counterexamples, we give a new exact learning algorithm whose query complexity
is quadratic in the target automaton size, almost matching the lower bound, and
improving the best previously-known algorithm by an exponential factor
Simple Priced Timed Games Are Not That Simple
Priced timed games are two-player zero-sum games played on priced timed
automata (whose locations and transitions are labeled by weights modeling the
costs of spending time in a state and executing an action, respectively). The
goals of the players are to minimise and maximise the cost to reach a target
location, respectively. We consider priced timed games with one clock and
arbitrary (positive and negative) weights and show that, for an important
subclass of theirs (the so-called simple priced timed games), one can compute,
in exponential time, the optimal values that the players can achieve, with
their associated optimal strategies. As side results, we also show that
one-clock priced timed games are determined and that we can use our result on
simple priced timed games to solve the more general class of so-called
reset-acyclic priced timed games (with arbitrary weights and one-clock)
Decidability of One-Clock Weighted Timed Games with Arbitrary Weights
Weighted Timed Games (WTG for short) are the most widely used model to describe controller synthesis problems involving real-time issues. Unfortunately, they are notoriously difficult, and undecidable in general. As a consequence, one-clock WTG has attracted a lot of attention, especially because they are known to be decidable when only non-negative weights are allowed. However, when arbitrary weights are considered, despite several recent works, their decidability status was still unknown. In this paper, we solve this problem positively and show that the value function can be computed in exponential time (if weights are encoded in unary)
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