A Stochastic Search on the Line-Based Solution to Discretized Estimation

Recently, Oommen and Rueda [11] presented a strategy by which the parameters of a binomial/multinomial distribution can be estimated when the underlying distribution is nonstationary. The method has been referred to as the Stochastic Learning Weak Estimator (SLWE), and is based on the principles of continuous stochastic Learning Automata (LA). In this paper, we consider a new family of stochastic discretized weak estimators pertinent to tracking time-varying binomial distributions. As opposed to the SLWE, our proposed estimator is discretized , i.e., the estimate can assume only a finite number of values. It is well known in the field of LA that discretized schemes achieve faster convergence speed than their corresponding continuous counterparts. By virtue of discretization, our estimator realizes extremely fast adjustments of the running estimates by jumps, and it is thus able to robustly, and very quickly, track changes in the parameters of the distribution after a switch has occurred in the environment. The design principle of our strategy is based on a solution, pioneered by Oommen [7], for the Stochastic Search on the Line (SSL) problem. The SSL solution proposed in [7], assumes the existence of an Oracle which informs the LA whether to go “right” or “left”. In our application domain, in order to achieve efficient estimation, we have to first infer (or rather simulate ) such an Oracle. In order to overcome this difficulty, we rather intelligently construct an “Artificial Oracle” that suggests whether we are to increase the current estimate or to decrease it. The paper briefly reports conclusive experimental results that demonstrate the ability of the proposed estimator to cope with non-stationary environments with a high adaptation rate, and with an accuracy that depends on its resolution. The results which we present are, to the best of our knowledge, the first reported results that resolve the problem of discretized weak estimation using a SSL-based solution

Learning automaton based on-line discovery and tracking of spatio-temporal event patterns

Discovering and tracking of spatio-temporal patterns in noisy sequences of events is a difficult task that has become increasingly pertinent due to recent advances in ubiquitous computing, such as community-based social networking applications. The core activities for applications of this class include the sharing and notification of events, and the importance and usefulness of these functionalites increases as event-sharing expands into larger areas of one's life. Ironical

The Influence of Bioactive Oxylipins from Marine Diatoms on Invertebrate Reproduction and Development

Diatoms are one of the main primary producers in aquatic ecosystems and occupy a vital link in the transfer of photosynthetically-fixed carbon through aquatic food webs. Diatoms produce an array of biologically-active metabolites, many of which have been attributed as a form of chemical defence and may offer potential as candidate marine drugs. Of considerable interest are molecules belonging to the oxylipin family which are broadly disruptive to reproductive and developmental processes. The range of reproductive impacts includes; oocyte maturation; sperm motility; fertilization; embryogenesis and larval competence. Much of the observed bioactivity may be ascribed to disruption of intracellular calcium signalling, induction of cytoskeletal instability and promotion of apoptotic pathways. From an ecological perspective, the primary interest in diatom-oxylipins is in relation to the potential impact on energy flow in planktonic systems whereby the reproductive success of copepods (the main grazers of diatoms) is compromised. Much data exists providing evidence for and against diatom reproductive effects; however detailed knowledge of the physiological and molecular processes involved remains poor. This paper provides a review of the current state of knowledge of the mechanistic impacts of diatom-oxylipins on marine invertebrate reproduction and development

Generalized Bayesian pursuit: A novel scheme for multi-armed Bernoulli bandit problems

In the last decades, a myriad of approaches to the multi-armed bandit problem have appeared in several different fields. The current top performing algorithms from the field of Learning Automata reside in the Pursuit family, while UCB-Tuned and the ε -greedy class of algorithms can be seen as state-of-the-art regret minimizing algorithms. Recently, however, the Bayesian Learning Automaton (BLA) outperformed all of these, and other schemes, in a wide range of experiments. Although seemingly incompatible, in this paper we integrate the foundational learning principles motivating the design of the BLA, with the principles of the so-called Generalized Pursuit algorithm (GPST), leading to the Generalized Bayesian Pursuit algorithm (GBPST). As in the BLA, the estimates are truly Bayesian in nature, however, instead of basing exploration upon direct sampling from the estimates, GBPST explores by means of the arm selection probability vector of GPST. Further, as in the GPST, in the interest of higher rates of learning, a set of arms that are currently perceived as being optimal is pursued to minimize the probability of pursuing a wrong arm. It turns out that GBPST is superior to GPST and that it even performs better than the BLA by controlling the learning speed of GBPST. We thus believe that GBPST constitutes a new avenue of research, in which the performance benefits of the GPST and the BLA are mutually augmented, opening up for improved performance in a number of applications, currently being tested

Pression foncière, monétarisation et individualisation des systèmes de production en zone cotonnière au Togo

Pression foncière accrue et monétarisation des échanges transforment les systèmes de production : fixation de l'agriculture, baisse des rendements et de la productivité du travail, migrations accentuées. Le développement des cultures de rapport se fait par une augmentation de la surface cultivée par actif et favorise une simplification des systèmes de culture. Identification de stratégies paysannes diverse

A novel strategy for solving the stochastic point location problem using a hierarchical searching scheme

Stochastic point location (SPL) deals with the problem of a learning mechanism (LM) determining the optimal point on the line when the only input it receives are stochastic signals about the direction in which it should move. One can differentiate the SPL from the traditional class of optimization problems by the fact that the former considers the case where the directional information, for example, as inferred from an Oracle (which possibly computes the derivatives), suffices to achieve the optimization-without actually explicitly computing any derivatives. The SPL can be described in terms of a LM (algorithm) attempting to locate a point on a line. The LM interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point. Given a current estimate of the optimal solution, all the reported solutions to this problem effectively move along the line to yield updated estimates which are in the neighborhood of the current solution.1 This paper proposes a dramatically distinct strategy, namely, that of partitioning the line in a hierarchical

On utilizing stochastic non-linear fractional bin packing to resolve distributed web crawling

This paper deals with the extremely pertinent problem of web crawling, which is far from trivial considering the magnitude and all-pervasive nature of the World-Wide Web. While numerous AI tools can be used to deal with this task, in this paper we map the problem onto the combinatoriallyhard stochastic non-linear fractional knapsack problem, which, in turn, is then solved using Learning Automata (LA). Such LA-based solutions have been recently shown to outperform previous state-of-the-art approaches to resource allocation in Web monitoring. However, the ever growing deployment of distributed systems raises the need for solutions that cope with a distributed setting. In this paper, we present a novel scheme for solving the non-linear fractional bin packing problem. Furthermore, we demonstrate that our scheme has applications to Web crawling, i.e., distributed resource allocation, and in particular, to distributed Web monitoring. Comprehensive experimental results demonstrate the superiority of our scheme when compared to other classical approaches