516 research outputs found
One-Tape Turing Machine Variants and Language Recognition
We present two restricted versions of one-tape Turing machines. Both
characterize the class of context-free languages. In the first version,
proposed by Hibbard in 1967 and called limited automata, each tape cell can be
rewritten only in the first visits, for a fixed constant .
Furthermore, for deterministic limited automata are equivalent to
deterministic pushdown automata, namely they characterize deterministic
context-free languages. Further restricting the possible operations, we
consider strongly limited automata. These models still characterize
context-free languages. However, the deterministic version is less powerful
than the deterministic version of limited automata. In fact, there exist
deterministic context-free languages that are not accepted by any deterministic
strongly limited automaton.Comment: 20 pages. This article will appear in the Complexity Theory Column of
the September 2015 issue of SIGACT New
An Experiment in Ping-Pong Protocol Verification by Nondeterministic Pushdown Automata
An experiment is described that confirms the security of a well-studied class
of cryptographic protocols (Dolev-Yao intruder model) can be verified by
two-way nondeterministic pushdown automata (2NPDA). A nondeterministic pushdown
program checks whether the intersection of a regular language (the protocol to
verify) and a given Dyck language containing all canceling words is empty. If
it is not, an intruder can reveal secret messages sent between trusted users.
The verification is guaranteed to terminate in cubic time at most on a
2NPDA-simulator. The interpretive approach used in this experiment simplifies
the verification, by separating the nondeterministic pushdown logic and program
control, and makes it more predictable. We describe the interpretive approach
and the known transformational solutions, and show they share interesting
features. Also noteworthy is how abstract results from automata theory can
solve practical problems by programming language means.Comment: In Proceedings MARS/VPT 2018, arXiv:1803.0866
Proofs of proximity for context-free languages and read-once branching programs
Proofs of proximity are probabilistic proof systems in which the verifier only queries a sub-linear number of input bits, and soundness only means that, with high probability, the input is close to an accepting input. In their minimal form, called Merlin-Arthur proofs of proximity ( MAP ), the verifier receives, in addition to query access to the input, also free access to an explicitly given short (sub-linear) proof. A more general notion is that of an interactive proof of proximity ( IPP ), in which the verifier is allowed to interact with an all-powerful, yet untrusted, prover. MAP s and IPP s may be thought of as the NP and IP analogues of property testing, respectively
Distributing Labels on Infinite Trees
Sturmian words are infinite binary words with many equivalent definitions:
They have a minimal factor complexity among all aperiodic sequences; they are
balanced sequences (the labels 0 and 1 are as evenly distributed as possible)
and they can be constructed using a mechanical definition. All this properties
make them good candidates for being extremal points in scheduling problems over
two processors. In this paper, we consider the problem of generalizing Sturmian
words to trees. The problem is to evenly distribute labels 0 and 1 over
infinite trees. We show that (strongly) balanced trees exist and can also be
constructed using a mechanical process as long as the tree is irrational. Such
trees also have a minimal factor complexity. Therefore they bring the hope that
extremal scheduling properties of Sturmian words can be extended to such trees,
as least partially. Such possible extensions are illustrated by one such
example.Comment: 30 pages, use pgf/tik
Transformers Learn Shortcuts to Automata
Algorithmic reasoning requires capabilities which are most naturally
understood through recurrent models of computation, like the Turing machine.
However, Transformer models, while lacking recurrence, are able to perform such
reasoning using far fewer layers than the number of reasoning steps. This
raises the question: what solutions are learned by these shallow and
non-recurrent models? We find that a low-depth Transformer can represent the
computations of any finite-state automaton (thus, any bounded-memory
algorithm), by hierarchically reparameterizing its recurrent dynamics. Our
theoretical results characterize shortcut solutions, whereby a Transformer with
layers can exactly replicate the computation of an automaton on an input
sequence of length . We find that polynomial-sized -depth
solutions always exist; furthermore, -depth simulators are surprisingly
common, and can be understood using tools from Krohn-Rhodes theory and circuit
complexity. Empirically, we perform synthetic experiments by training
Transformers to simulate a wide variety of automata, and show that shortcut
solutions can be learned via standard training. We further investigate the
brittleness of these solutions and propose potential mitigations
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