2,825 research outputs found
POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infinite set of all computable descriptions of tasks with possibly
computable solutions. The novel algorithmic framework POWERPLAY (2011)
continually searches the space of possible pairs of new tasks and modifications
of the current problem solver, until it finds a more powerful problem solver
that provably solves all previously learned tasks plus the new one, while the
unmodified predecessor does not. Wow-effects are achieved by continually making
previously learned skills more efficient such that they require less time and
space. New skills may (partially) re-use previously learned skills. POWERPLAY's
search orders candidate pairs of tasks and solver modifications by their
conditional computational (time & space) complexity, given the stored
experience so far. The new task and its corresponding task-solving skill are
those first found and validated. The computational costs of validating new
tasks need not grow with task repertoire size. POWERPLAY's ongoing search for
novelty keeps breaking the generalization abilities of its present solver. This
is related to Goedel's sequence of increasingly powerful formal theories based
on adding formerly unprovable statements to the axioms without affecting
previously provable theorems. The continually increasing repertoire of problem
solving procedures can be exploited by a parallel search for solutions to
additional externally posed tasks. POWERPLAY may be viewed as a greedy but
practical implementation of basic principles of creativity. A first
experimental analysis can be found in separate papers [53,54].Comment: 21 pages, additional connections to previous work, references to
first experiments with POWERPLA
Silent MST approximation for tiny memory
In network distributed computing, minimum spanning tree (MST) is one of the
key problems, and silent self-stabilization one of the most demanding
fault-tolerance properties. For this problem and this model, a polynomial-time
algorithm with memory is known for the state model. This is
memory optimal for weights in the classic range (where
is the size of the network). In this paper, we go below this
memory, using approximation and parametrized complexity.
More specifically, our contributions are two-fold. We introduce a second
parameter~, which is the space needed to encode a weight, and we design a
silent polynomial-time self-stabilizing algorithm, with space . In turn, this allows us to get an approximation algorithm for the problem,
with a trade-off between the approximation ratio of the solution and the space
used. For polynomial weights, this trade-off goes smoothly from memory for an -approximation, to memory for exact solutions,
with for example memory for a 2-approximation
Inference of termination conditions for numerical loops in Prolog
We present a new approach to termination analysis of numerical computations
in logic programs. Traditional approaches fail to analyse them due to non
well-foundedness of the integers. We present a technique that allows overcoming
these difficulties. Our approach is based on transforming a program in a way
that allows integrating and extending techniques originally developed for
analysis of numerical computations in the framework of query-mapping pairs with
the well-known framework of acceptability. Such an integration not only
contributes to the understanding of termination behaviour of numerical
computations, but also allows us to perform a correct analysis of such
computations automatically, by extending previous work on a constraint-based
approach to termination. Finally, we discuss possible extensions of the
technique, including incorporating general term orderings.Comment: To appear in Theory and Practice of Logic Programming. To appear in
Theory and Practice of Logic Programmin
Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks
Mixed-criticality models are an emerging paradigm for the design of real-time
systems because of their significantly improved resource efficiency. However,
formal mixed-criticality models have traditionally been characterized by two
impractical assumptions: once \textit{any} high-criticality task overruns,
\textit{all} low-criticality tasks are suspended and \textit{all other}
high-criticality tasks are assumed to exhibit high-criticality behaviors at the
same time. In this paper, we propose a more realistic mixed-criticality model,
called the flexible mixed-criticality (FMC) model, in which these two issues
are addressed in a combined manner. In this new model, only the overrun task
itself is assumed to exhibit high-criticality behavior, while other
high-criticality tasks remain in the same mode as before. The guaranteed
service levels of low-criticality tasks are gracefully degraded with the
overruns of high-criticality tasks. We derive a utilization-based technique to
analyze the schedulability of this new mixed-criticality model under EDF-VD
scheduling. During runtime, the proposed test condition serves an important
criterion for dynamic service level tuning, by means of which the maximum
available execution budget for low-criticality tasks can be directly determined
with minimal overhead while guaranteeing mixed-criticality schedulability.
Experiments demonstrate the effectiveness of the FMC scheme compared with
state-of-the-art techniques.Comment: This paper has been submitted to IEEE Transaction on Computers (TC)
on Sept-09th-201
Interval-valued contractive fuzzy negations
In this work we consider the concept of contractive interval-valued fuzzy negation, as a negation such that it does not increase the length or amplitude of an interval. We relate this to the concept of Lipschitz function. In particular, we prove that the only strict (strong) contractive interval-valued fuzzy negation is the one generated from the standard (Zadeh's) negation
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