9,441 research outputs found
Streaming algorithms for language recognition problems
We study the complexity of the following problems in the streaming model.
Membership testing for \DLIN We show that every language in \DLIN\ can be
recognised by a randomized one-pass space algorithm with inverse
polynomial one-sided error, and by a deterministic p-pass space
algorithm. We show that these algorithms are optimal.
Membership testing for \LL For languages generated by \LL grammars
with a bound of on the number of nonterminals at any stage in the left-most
derivation, we show that membership can be tested by a randomized one-pass
space algorithm with inverse polynomial (in ) one-sided error.
Membership testing for \DCFL We show that randomized algorithms as efficient
as the ones described above for \DLIN\ and \LL(k) (which are subclasses of
\DCFL) cannot exist for all of \DCFL: there is a language in \VPL\ (a subclass
of \DCFL) for which any randomized p-pass algorithm with error bounded by
must use space.
Degree sequence problem We study the problem of determining, given a sequence
and a graph , whether the degree sequence of is
precisely . We give a randomized one-pass space
algorithm with inverse polynomial one-sided error probability. We show that our
algorithms are optimal.
Our randomized algorithms are based on the recent work of Magniez et al.
\cite{MMN09}; our lower bounds are obtained by considering related
communication complexity problems
Decision problems for Clark-congruential languages
A common question when studying a class of context-free grammars is whether
equivalence is decidable within this class. We answer this question positively
for the class of Clark-congruential grammars, which are of interest to
grammatical inference. We also consider the problem of checking whether a given
CFG is Clark-congruential, and show that it is decidable given that the CFG is
a DCFG.Comment: Version 2 incorporates revisions prompted by the comments of
anonymous referees at ICGI and LearnAu
Towards an Intelligent Database System Founded on the SP Theory of Computing and Cognition
The SP theory of computing and cognition, described in previous publications,
is an attractive model for intelligent databases because it provides a simple
but versatile format for different kinds of knowledge, it has capabilities in
artificial intelligence, and it can also function like established database
models when that is required.
This paper describes how the SP model can emulate other models used in
database applications and compares the SP model with those other models. The
artificial intelligence capabilities of the SP model are reviewed and its
relationship with other artificial intelligence systems is described. Also
considered are ways in which current prototypes may be translated into an
'industrial strength' working system
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