1,088 research outputs found
Adaptive Load Balancing: A Study in Multi-Agent Learning
We study the process of multi-agent reinforcement learning in the context of
load balancing in a distributed system, without use of either central
coordination or explicit communication. We first define a precise framework in
which to study adaptive load balancing, important features of which are its
stochastic nature and the purely local information available to individual
agents. Given this framework, we show illuminating results on the interplay
between basic adaptive behavior parameters and their effect on system
efficiency. We then investigate the properties of adaptive load balancing in
heterogeneous populations, and address the issue of exploration vs.
exploitation in that context. Finally, we show that naive use of communication
may not improve, and might even harm system efficiency.Comment: See http://www.jair.org/ for any accompanying file
Bounded Quantifier Instantiation for Checking Inductive Invariants
We consider the problem of checking whether a proposed invariant
expressed in first-order logic with quantifier alternation is inductive, i.e.
preserved by a piece of code. While the problem is undecidable, modern SMT
solvers can sometimes solve it automatically. However, they employ powerful
quantifier instantiation methods that may diverge, especially when is
not preserved. A notable difficulty arises due to counterexamples of infinite
size.
This paper studies Bounded-Horizon instantiation, a natural method for
guaranteeing the termination of SMT solvers. The method bounds the depth of
terms used in the quantifier instantiation process. We show that this method is
surprisingly powerful for checking quantified invariants in uninterpreted
domains. Furthermore, by producing partial models it can help the user diagnose
the case when is not inductive, especially when the underlying reason
is the existence of infinite counterexamples.
Our main technical result is that Bounded-Horizon is at least as powerful as
instrumentation, which is a manual method to guarantee convergence of the
solver by modifying the program so that it admits a purely universal invariant.
We show that with a bound of 1 we can simulate a natural class of
instrumentations, without the need to modify the code and in a fully automatic
way. We also report on a prototype implementation on top of Z3, which we used
to verify several examples by Bounded-Horizon of bound 1
Multistable attractors in a network of phase oscillators with three-body interaction
Three-body interactions have been found in physics, biology, and sociology.
To investigate their effect on dynamical systems, as a first step, we study
numerically and theoretically a system of phase oscillators with three-body
interaction. As a result, an infinite number of multistable synchronized states
appear above a critical coupling strength, while a stable incoherent state
always exists for any coupling strength. Owing to the infinite multistability,
the degree of synchrony in asymptotic state can vary continuously within some
range depending on the initial phase pattern.Comment: 5 pages, 3 figure
Robust vector quantization for noisy channels
The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance
Development of an Artificial Neural Network Model for Predicting Surface Water Level: Case of Modder River Catchment Area
Published ArticleWater is vital for life; however, water is a scarce natural resource that
is under serious threat of depletion. South Africa and indeed the Free State is a
water-scarce region, and facing growing challenges of delivering fresh and
adequate water to the people. In order to effectively manage surface water,
monitoring and predictions tools are required to inform decision makers on a
real-time basis. Artificial Neural Networks (ANNs) have proven that they can be
used to develop such prediction models and tools. This research makes use of
experimentation, prototyping and case study to develop, identify and evaluate
the ANN with best surface water level prediction capabilities. What ANN’s
techniques and algorithms are the most suitable for predicting surface water
levels given parameters such as water levels, precipitation, air temperature, wind
speed, wind direction? How accurately will the ANNs developed predict surface
water levels of the Modder River catchment area
The Emergence of Norms via Contextual Agreements in Open Societies
This paper explores the emergence of norms in agents' societies when agents
play multiple -even incompatible- roles in their social contexts
simultaneously, and have limited interaction ranges. Specifically, this article
proposes two reinforcement learning methods for agents to compute agreements on
strategies for using common resources to perform joint tasks. The computation
of norms by considering agents' playing multiple roles in their social contexts
has not been studied before. To make the problem even more realistic for open
societies, we do not assume that agents share knowledge on their common
resources. So, they have to compute semantic agreements towards performing
their joint actions. %The paper reports on an empirical study of whether and
how efficiently societies of agents converge to norms, exploring the proposed
social learning processes w.r.t. different society sizes, and the ways agents
are connected. The results reported are very encouraging, regarding the speed
of the learning process as well as the convergence rate, even in quite complex
settings
A Game of Attribute Decomposition for Software Architecture Design
Attribute-driven software architecture design aims to provide decision
support by taking into account the quality attributes of softwares. A central
question in this process is: What architecture design best fulfills the
desirable software requirements? To answer this question, a system designer
needs to make tradeoffs among several potentially conflicting quality
attributes. Such decisions are normally ad-hoc and rely heavily on experiences.
We propose a mathematical approach to tackle this problem. Game theory
naturally provides the basic language: Players represent requirements, and
strategies involve setting up coalitions among the players. In this way we
propose a novel model, called decomposition game, for attribute-driven design.
We present its solution concept based on the notion of cohesion and
expansion-freedom and prove that a solution always exists. We then investigate
the computational complexity of obtaining a solution. The game model and the
algorithms may serve as a general framework for providing useful guidance for
software architecture design. We present our results through running examples
and a case study on a real-life software project.Comment: 23 pages, 5 figures, a shorter version to appear at 12th
International Colloquium on Theoretical Aspects of Computing (ICTAC 2015
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