33,274 research outputs found
On the inference and management of macro-actions in forward-chaining planning
In this paper we discuss techniques for online generation of macro-actions as part of the planning process and demonstrate their use in a forward chaining search planning framework. The macroactions learnt are specifically created at places in the search space where the heuristic is not informative. We present results to show that using macro-actions generated during planning can improve planning performance
Rational Deployment of CSP Heuristics
Heuristics are crucial tools in decreasing search effort in varied fields of
AI. In order to be effective, a heuristic must be efficient to compute, as well
as provide useful information to the search algorithm. However, some well-known
heuristics which do well in reducing backtracking are so heavy that the gain of
deploying them in a search algorithm might be outweighed by their overhead.
We propose a rational metareasoning approach to decide when to deploy
heuristics, using CSP backtracking search as a case study. In particular, a
value of information approach is taken to adaptive deployment of solution-count
estimation heuristics for value ordering. Empirical results show that indeed
the proposed mechanism successfully balances the tradeoff between decreasing
backtracking and heuristic computational overhead, resulting in a significant
overall search time reduction.Comment: 7 pages, 2 figures, to appear in IJCAI-2011, http://www.ijcai.org
Sensing Throughput Optimization in Fading Cognitive Multiple Access Channels With Energy Harvesting Secondary Transmitters
The paper investigates the problem of maximizing expected sum throughput in a
fading multiple access cognitive radio network when secondary user (SU)
transmitters have energy harvesting capability, and perform cooperative
spectrum sensing. We formulate the problem as maximization of sum-capacity of
the cognitive multiple access network over a finite time horizon subject to a
time averaged interference constraint at the primary user (PU) and almost sure
energy causality constraints at the SUs. The problem is a mixed integer
non-linear program with respect to two decision variables namely spectrum
access decision and spectrum sensing decision, and the continuous variables
sensing time and transmission power. In general, this problem is known to be NP
hard. For optimization over these two decision variables, we use an exhaustive
search policy when the length of the time horizon is small, and a heuristic
policy for longer horizons. For given values of the decision variables, the
problem simplifies into a joint optimization on SU \textit{transmission power}
and \textit{sensing time}, which is non-convex in nature. We solve the
resulting optimization problem as an alternating convex optimization problem
for both non-causal and causal channel state information and harvested energy
information patterns at the SU base station (SBS) or fusion center (FC). We
present an analytic solution for the non-causal scenario with infinite battery
capacity for a general finite horizon problem.We formulate the problem with
causal information and finite battery capacity as a stochastic control problem
and solve it using the technique of dynamic programming. Numerical results are
presented to illustrate the performance of the various algorithms
Optimized complex power quality classifier using one vs. rest support vector machine
Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a ?One Vs Rest? architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.Fil: de Yong, David Marcelo. Universidad Nacional de RĂo Cuarto. Facultad de IngenierĂa. Departamento de Electricidad y ElectrĂłnica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba; ArgentinaFil: Bhowmik, Sudipto. Nexant Inc; Estados UnidosFil: Magnago, Fernando. Universidad Nacional de RĂo Cuarto. Facultad de IngenierĂa. Departamento de Electricidad y ElectrĂłnica; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - CĂłrdoba; Argentin
Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation
Combinatorial interaction testing is an important software testing technique
that has seen lots of recent interest. It can reduce the number of test cases
needed by considering interactions between combinations of input parameters.
Empirical evidence shows that it effectively detects faults, in particular, for
highly configurable software systems. In real-world software testing, the input
variables may vary in how strongly they interact, variable strength
combinatorial interaction testing (VS-CIT) can exploit this for higher
effectiveness. The generation of variable strength test suites is a
non-deterministic polynomial-time (NP) hard computational problem
\cite{BestounKamalFuzzy2017}. Research has shown that stochastic
population-based algorithms such as particle swarm optimization (PSO) can be
efficient compared to alternatives for VS-CIT problems. Nevertheless, they
require detailed control for the exploitation and exploration trade-off to
avoid premature convergence (i.e. being trapped in local optima) as well as to
enhance the solution diversity. Here, we present a new variant of PSO based on
Mamdani fuzzy inference system
\cite{Camastra2015,TSAKIRIDIS2017257,KHOSRAVANIAN2016280}, to permit adaptive
selection of its global and local search operations. We detail the design of
this combined algorithm and evaluate it through experiments on multiple
synthetic and benchmark problems. We conclude that fuzzy adaptive selection of
global and local search operations is, at least, feasible as it performs only
second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the
best mean test suite size, the fuzzy adaptation even outperforms DPSO
occasionally. We discuss the reasons behind this performance and outline
relevant areas of future work.Comment: 21 page
Prioritized Random MAC Optimization via Graph-based Analysis
Motivated by the analogy between successive interference cancellation and
iterative belief-propagation on erasure channels, irregular repetition slotted
ALOHA (IRSA) strategies have received a lot of attention in the design of
medium access control protocols. The IRSA schemes have been mostly analyzed for
theoretical scenarios for homogenous sources, where they are shown to
substantially improve the system performance compared to classical slotted
ALOHA protocols. In this work, we consider generic systems where sources in
different importance classes compete for a common channel. We propose a new
prioritized IRSA algorithm and derive the probability to correctly resolve
collisions for data from each source class. We then make use of our theoretical
analysis to formulate a new optimization problem for selecting the transmission
strategies of heterogenous sources. We optimize both the replication
probability per class and the source rate per class, in such a way that the
overall system utility is maximized. We then propose a heuristic-based
algorithm for the selection of the transmission strategy, which is built on
intrinsic characteristics of the iterative decoding methods adopted for
recovering from collisions. Experimental results validate the accuracy of the
theoretical study and show the gain of well-chosen prioritized transmission
strategies for transmission of data from heterogenous classes over shared
wireless channels
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