49,420 research outputs found
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
Distributed Computing with Adaptive Heuristics
We use ideas from distributed computing to study dynamic environments in
which computational nodes, or decision makers, follow adaptive heuristics (Hart
2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly
"best replying" to others' actions, and minimizing "regret", that have been
extensively studied in game theory and economics. We explore when convergence
of such simple dynamics to an equilibrium is guaranteed in asynchronous
computational environments, where nodes can act at any time. Our research
agenda, distributed computing with adaptive heuristics, lies on the borderline
of computer science (including distributed computing and learning) and game
theory (including game dynamics and adaptive heuristics). We exhibit a general
non-termination result for a broad class of heuristics with bounded
recall---that is, simple rules of behavior that depend only on recent history
of interaction between nodes. We consider implications of our result across a
wide variety of interesting and timely applications: game theory, circuit
design, social networks, routing and congestion control. We also study the
computational and communication complexity of asynchronous dynamics and present
some basic observations regarding the effects of asynchrony on no-regret
dynamics. We believe that our work opens a new avenue for research in both
distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion
of v1. Revised version will appear in the proceedings of Innovations in
Computer Science 201
Adaptive Duty Cycling MAC Protocols Using Closed-Loop Control for Wireless Sensor Networks
The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay
Scalable and Sustainable Deep Learning via Randomized Hashing
Current deep learning architectures are growing larger in order to learn from
complex datasets. These architectures require giant matrix multiplication
operations to train millions of parameters. Conversely, there is another
growing trend to bring deep learning to low-power, embedded devices. The matrix
operations, associated with both training and testing of deep networks, are
very expensive from a computational and energy standpoint. We present a novel
hashing based technique to drastically reduce the amount of computation needed
to train and test deep networks. Our approach combines recent ideas from
adaptive dropouts and randomized hashing for maximum inner product search to
select the nodes with the highest activation efficiently. Our new algorithm for
deep learning reduces the overall computational cost of forward and
back-propagation by operating on significantly fewer (sparse) nodes. As a
consequence, our algorithm uses only 5% of the total multiplications, while
keeping on average within 1% of the accuracy of the original model. A unique
property of the proposed hashing based back-propagation is that the updates are
always sparse. Due to the sparse gradient updates, our algorithm is ideally
suited for asynchronous and parallel training leading to near linear speedup
with increasing number of cores. We demonstrate the scalability and
sustainability (energy efficiency) of our proposed algorithm via rigorous
experimental evaluations on several real datasets
Adaptive Epidemic Dynamics in Networks: Thresholds and Control
Theoretical modeling of computer virus/worm epidemic dynamics is an important
problem that has attracted many studies. However, most existing models are
adapted from biological epidemic ones. Although biological epidemic models can
certainly be adapted to capture some computer virus spreading scenarios
(especially when the so-called homogeneity assumption holds), the problem of
computer virus spreading is not well understood because it has many important
perspectives that are not necessarily accommodated in the biological epidemic
models. In this paper we initiate the study of such a perspective, namely that
of adaptive defense against epidemic spreading in arbitrary networks. More
specifically, we investigate a non-homogeneous
Susceptible-Infectious-Susceptible (SIS) model where the model parameters may
vary with respect to time. In particular, we focus on two scenarios we call
semi-adaptive defense and fully-adaptive} defense, which accommodate implicit
and explicit dependency relationships between the model parameters,
respectively. In the semi-adaptive defense scenario, the model's input
parameters are given; the defense is semi-adaptive because the adjustment is
implicitly dependent upon the outcome of virus spreading. For this scenario, we
present a set of sufficient conditions (some are more general or succinct than
others) under which the virus spreading will die out; such sufficient
conditions are also known as epidemic thresholds in the literature. In the
fully-adaptive defense scenario, some input parameters are not known (i.e., the
aforementioned sufficient conditions are not applicable) but the defender can
observe the outcome of virus spreading. For this scenario, we present adaptive
control strategies under which the virus spreading will die out or will be
contained to a desired level.Comment: 20 pages, 8 figures. This paper was submitted in March 2009, revised
in August 2009, and accepted in December 2009. However, the paper was not
officially published until 2014 due to non-technical reason
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