1,390 research outputs found
The Computational Power of Beeps
In this paper, we study the quantity of computational resources (state
machine states and/or probabilistic transition precision) needed to solve
specific problems in a single hop network where nodes communicate using only
beeps. We begin by focusing on randomized leader election. We prove a lower
bound on the states required to solve this problem with a given error bound,
probability precision, and (when relevant) network size lower bound. We then
show the bound tight with a matching upper bound. Noting that our optimal upper
bound is slow, we describe two faster algorithms that trade some state
optimality to gain efficiency. We then turn our attention to more general
classes of problems by proving that once you have enough states to solve leader
election with a given error bound, you have (within constant factors) enough
states to simulate correctly, with this same error bound, a logspace TM with a
constant number of unary input tapes: allowing you to solve a large and
expressive set of problems. These results identify a key simplicity threshold
beyond which useful distributed computation is possible in the beeping model.Comment: Extended abstract to appear in the Proceedings of the International
Symposium on Distributed Computing (DISC 2015
Global Versus Local Computations: Fast Computing with Identifiers
This paper studies what can be computed by using probabilistic local
interactions with agents with a very restricted power in polylogarithmic
parallel time. It is known that if agents are only finite state (corresponding
to the Population Protocol model by Angluin et al.), then only semilinear
predicates over the global input can be computed. In fact, if the population
starts with a unique leader, these predicates can even be computed in a
polylogarithmic parallel time. If identifiers are added (corresponding to the
Community Protocol model by Guerraoui and Ruppert), then more global predicates
over the input multiset can be computed. Local predicates over the input sorted
according to the identifiers can also be computed, as long as the identifiers
are ordered. The time of some of those predicates might require exponential
parallel time. In this paper, we consider what can be computed with Community
Protocol in a polylogarithmic number of parallel interactions. We introduce the
class CPPL corresponding to protocols that use , for some k,
expected interactions to compute their predicates, or equivalently a
polylogarithmic number of parallel expected interactions. We provide some
computable protocols, some boundaries of the class, using the fact that the
population can compute its size. We also prove two impossibility results
providing some arguments showing that local computations are no longer easy:
the population does not have the time to compare a linear number of consecutive
identifiers. The Linearly Local languages, such that the rational language
, are not computable.Comment: Long version of SSS 2016 publication, appendixed version of SIROCCO
201
Learning cover context-free grammars from structural data
We consider the problem of learning an unknown context-free grammar when the
only knowledge available and of interest to the learner is about its structural
descriptions with depth at most The goal is to learn a cover
context-free grammar (CCFG) with respect to , that is, a CFG whose
structural descriptions with depth at most agree with those of the
unknown CFG. We propose an algorithm, called , that efficiently learns
a CCFG using two types of queries: structural equivalence and structural
membership. We show that runs in time polynomial in the number of
states of a minimal deterministic finite cover tree automaton (DCTA) with
respect to . This number is often much smaller than the number of states
of a minimum deterministic finite tree automaton for the structural
descriptions of the unknown grammar
On Convergence and Threshold Properties of Discrete Lotka-Volterra Population Protocols
In this work we focus on a natural class of population protocols whose
dynamics are modelled by the discrete version of Lotka-Volterra equations. In
such protocols, when an agent of type (species) interacts with an agent
of type (species) with as the initiator, then 's type becomes
with probability . In such an interaction, we think of as the
predator, as the prey, and the type of the prey is either converted to that
of the predator or stays as is. Such protocols capture the dynamics of some
opinion spreading models and generalize the well-known Rock-Paper-Scissors
discrete dynamics. We consider the pairwise interactions among agents that are
scheduled uniformly at random. We start by considering the convergence time and
show that any Lotka-Volterra-type protocol on an -agent population converges
to some absorbing state in time polynomial in , w.h.p., when any pair of
agents is allowed to interact. By contrast, when the interaction graph is a
star, even the Rock-Paper-Scissors protocol requires exponential time to
converge. We then study threshold effects exhibited by Lotka-Volterra-type
protocols with 3 and more species under interactions between any pair of
agents. We start by presenting a simple 4-type protocol in which the
probability difference of reaching the two possible absorbing states is
strongly amplified by the ratio of the initial populations of the two other
types, which are transient, but "control" convergence. We then prove that the
Rock-Paper-Scissors protocol reaches each of its three possible absorbing
states with almost equal probability, starting from any configuration
satisfying some sub-linear lower bound on the initial size of each species.
That is, Rock-Paper-Scissors is a realization of a "coin-flip consensus" in a
distributed system. Some of our techniques may be of independent value
On the complexity of minimum inference of regular sets
We prove results concerning the computational tractability of some problems related to determining minimum realizations of finite samples of regular sets by finite automata and regular expressions
Finding patterns common to a set of strings
AbstractAssume a finite alphabet of constant symbols and a disjoint infinite alphabet of variable symbols. A pattern is a non-null finite string of constant and variable symbols. The language of a pattern is all strings obtainable by substituting non-null strings of constant symbols for the variables of the pattern. A sample is a finite nonempty set of non-null strings of constant symbols. Given a sample S, a pattern p is descriptive of S provided the language of p contains S and does not properly contain the language of any other pattern that contains S. The computational problem of finding a pattern descriptive of a given sample is studied. The main result is a polynomial-time algorithm for the special case of patterns containing only one variable symbol (possibly occurring several times in the pattern). Several other results are proved concerning the class of languages generated by patterns and the problem of finding a descriptive pattern
Queries revisited
AbstractWe begin with a brief tutorial on the problem of learning a finite concept class over a finite domain using membership queries and/or equivalence queries. We then sketch general results on the number of queries needed to learn a class of concepts, focusing on the various notions of combinatorial dimension that have been employed, including the teaching dimension, the exclusion dimension, the extended teaching dimension, the fingerprint dimension, the sample exclusion dimension, the VapnikâChervonenkis dimension, the abstract identification dimension, and the general dimension
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