QG/GA: a stochastic search for Progol

Most search techniques within ILP require the evaluation of a large number of inconsistent clauses. However, acceptable clauses typically need to be consistent, and are only found at the “fringe” of the search space. A search approach is presented, based on a novel algorithm called QG (Quick Generalization). QG carries out a random-restart stochastic bottom-up search which efficiently generates a consistent clause on the fringe of the refinement graph search without needing to explore the graph in detail. We use a Genetic Algorithm (GA) to evolve and re-combine clauses generated by QG. In this QG/GA setting, QG is used to seed a population of clauses processed by the GA. Experiments with QG/GA indicate that this approach can be more efficient than standard refinement-graph searches, while generating similar or better solutions. Editors: Ramon Otero, Simon Colton.     

User simulation in a stochastic dialog system

We present a new methodology of user simulation applied to the evaluation and refinement of stochastic dialog systems. Common weaknesses of these systems are the scarceness of the training corpus and the cost of an evaluation made by real users. We have considered the user simulation technique as an alternative way of testing and improving our dialog system. We have developed a new dialog manager that plays the role of the user. This user dialog manager incorporates several knowledge sources, combining statistical and heuristic information in order to define its dialog strategy. Once the user simulator is integrated into the dialog system, it is possible to enhance the dialog models by an automatic strategy learning. We have performed an extensive evaluation, achieving a slight but clear improvement of the dialog system.     

Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems

An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein–Wiener ARMAX models. The basic idea is to replace the unmeasurable noise terms in the information vector of the pseudo-linear regression identification model with the corresponding noise estimates which are computed by the obtained parameter estimates. The obtained parameter estimates of the identification model include the product terms of the parameters of the original systems. Two methods of separating the parameter estimates of the original parameters from the product terms are discussed: the average method and the singular value decomposition method. To improve the identification accuracy, an extended stochastic gradient algorithm with a forgetting factor is presented. The simulation results indicate that the parameter estimation errors become small by introducing the forgetting factor.     

Cost-Based Filtering Techniques for Stochastic Inventory Control Under Service Level Constraints

This paper¹ considers a single product and a single stocking location production/inventory control problem given a non-stationary stochastic demand. Under a widely-used control policy for this type of inventory system, the objective is to find the optimal number of replenishments, their timings and their respective order-up-to-levels that meet customer demands to a required service level. We extend a known CP approach for this problem using three cost-based filtering methods. Our approach can solve to optimality instances of realistic size much more efficiently than previous approaches, often with no search effort at all. This work was supported by Science Foundation Ireland under Grant No. 03/CE3/I405 as part of the Centre for Telecommunications Value-Chain-Driven Research (CTVR) and Grant No. 00/PI.1/C075. ¹This paper is an extended version of [19].     

Stochastic learning and optimization—A sensitivity-based approach

We introduce a sensitivity-based view to the area of learning and optimization of stochastic dynamic systems. We show that this sensitivity-based view provides a unified framework for many different disciplines in this area, including perturbation analysis, Markov decision processes, reinforcement learning, identification and adaptive control, and singular stochastic control; and that this unified framework applies to both the discrete event dynamic systems and continuous-time continuous-state systems. Many results in these disciplines can be simply derived and intuitively explained by using two performance sensitivity formulas. In addition, we show that this sensitivity-based view leads to new results and opens up new directions for future research. For example, the n th bias optimality of Markov processes has been established and the event-based optimization may be developed; this approach has computational and other advantages over the state-based approaches.     

Solving the Beck and Wieland model with optimal experimentation in DualPC

Currently, there is a renewed interest in the use of optimal experimentation (adaptive control) in economics. Example are found in [Amman and Kendrick, 1999], [Amman and Kendrick, 2003], [Cosimano, in?press], [Cosimano and Gapen, 2005b], [Cosimano and Gapen, 2005a], [Cosimano and Gapen, 2006], [Tesfaselassie et?al., 2007], [Tucci, 1997], [Wieland, 2000a] and [Wieland, 2000b]. In this paper we present the Beck & Wieland model [Beck, G., & Wieland, V. (2002). Learning and control in a changing economic environment. Journal of Economic Dynamics and Control, 26, 1359-1378] and the methodology to solve this model with time-varying parameters using the various control methods described in [Kendrick, 1981] and [Kendrick, 2002]. Furthermore, we also provide numerical results using the DualPC software [Amman, H. M., & Kendrick, D. A. (1999). The DualI/DualPC software for optimal control models: User’s guide. Working paper, Austin, TX 78712, USA: Center for Applied Research in Economics, University of Texas] and show first evidence that optimal experimentation or Dual Control may produce better results than Expected Optimal Feedback.     

On hybrid control of a class of stochastic non-linear Markovian switching systems

In this paper, we address the problem of hybrid control for a class of stochastic non-linear Markovian switching systems. First, a hybrid controller is introduced for the systems. Then under some appropriate assumptions, the stabilization condition for the systems under pure impulsive control is given. Further under impulsive control, the output feedback stabilization problem of the systems is discussed and linear output feedback controllers are designed. Finally a numerical example is provided to illustrate the effectiveness of the proposed methods.     

基于随机模型的艾滋病预防与控制策略研究

建立了艾滋病传播过程中感染者人数占总人口比例所满足的随机模型,分析了环境因素对艾滋病传播过程的影响,利用数值解方法对模型进行分析,结合我国数十年间艾滋病病毒感染者实际数据进行仿真,验证模型的有效性;并对未来我国艾滋病病毒感染者人数占总人口比例进行预测,通过对其中的传染率控制指标β进行分析,得出这一指标的波动对艾滋病流行趋势的影响.通过分析其均值与方差,提出对β的调整策略以达到一定的控制目标,从而为艾滋病的预防和控制提供一定程度上的决策支持.