Risk-sensitive control with HARA utility

A control methodology based on the hyperbolic absolute risk averse (HARA) utility function is presented as an alternative to the exponential-of-an-integral approach to finding robust controllers. The work is inspired by the intuition that HARA controllers, while being robust, may give better performance than exponential controllers in normal situations. The HARA problem is shown to be equivalent to a certain differential game, and the asymptotic properties of the HARA problem and this differential game are studied. As an example, a linear-quadratic HARA problem is studied, where the problem of finding a robust HARA controller is proved to be equivalent to solving a standard linear-quadratic problem for a system with a higher noise intensity. This reveals an interesting relationship between robustness and uncertainty     

Selection of a fixed-income portfolio

A nonlinear programming problem in the space of two scalar parameters is applied to approximate the problem of optimization of a fixed-income portfolio by the quantile quality criterion. Since such securities have limited maturity time, portfolio is formed through reinvestment of the avails obtained upon maturity of securities. A quantile criterion is applied to determine the investment risk due to the uncertainty in future reinvestment. The geometry of the set of admissible values of parameters is studied and an explicit algebraic expression is derived for the optimum of the aim function in the nonlinear programming problem.     

Uncertainty management with imprecise knowledge with application to design

Uncertainty management is critical to the effective use of knowledge-based systems in a wide variety of domains. Design is typical of these domains in that the implementation of a design in an artifact, the future environment for the artifact, and the component characteristics of the artifact are all uncertain. Existing probabilistic schemes to address the inherent uncertainty in areas like design assume precise knowledge of the probabilities of relevant events. This paper defines a probabilistic method for uncertainty management with imprecise inputs. The approach combines Bayesian inference networks and information theoretic inference procedures. The resulting scheme manages both imprecision and uncertainty in the problem domain. An application of the approach to materiel design is described.     

Risk-sensitive portfolio optimization with completely and partially observed factors

In this note, optimal portfolio maximizing the long run risk-sensitized growth rate of the capital process in the case when the dynamics of the asset prices depend on some economical factors, which are completely or partially observed, using a discounted cost approach is shown.     

Risk-sensitive probability for Markov chains

The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to a generalized notion of probability distribution for Markov chains. The evolution and the asymptotic behavior of this generalized or “risk-sensitive” probability distribution is studied in this paper and a conjecture is proposed regarding the asymptotic periodicity of risk-sensitive probability and proved in the two-dimensional case. The relation between a set of simultaneous non-linear and the set of periodic attractors is analyzed.     

Risk-sensitive control for a class of nonlinear systems with multiplicative noise

In this paper, we consider the problem of optimal control for a class of nonlinear stochastic systems with multiplicative noise. The nonlinearity consists of quadratic terms in the state and control variables. The optimality criteria are of a risk-sensitive and generalised risk-sensitive type. The optimal control is found in an explicit closed-form by the completion of squares and the change of measure methods. As applications, we outline two special cases of our results. We show that a subset of the class of models which we consider leads to a generalised quadratic–affine term structure model (QATSM) for interest rates. We also demonstrate how our results lead to generalisation of exponential utility as a criterion in optimal investment.     

Risk-sensitive filtering for jump Markov linear systems

In this paper, a risk-sensitive multiple-model filtering algorithm is derived using the reference probability methods. First, the approximation of the interacting multiple-model (IMM) algorithm is identified in the reference probability domain. Then, the same type of approximation is used to derive the finite-dimensional risk-sensitive filtering algorithm. The derived algorithm reduces to the IMM filter when the risk-sensitive parameter goes to zero and reduces to the risk-sensitive filter for linear Gauss-Markov systems when the number of models is unity. The algorithm performs better in a simulated uncertain parameter scenario than the IMM filter.     

Risk-sensitive estimation and a differential game

A large deviation result is employed to solve the state estimation problem of a continuous time Gauss-Markov system with an exponential cost. The exponential cost is the expected value of an exponential function of the state estimation-error. A scalar &thetas; appearing in the cost, termed as the risk factor, determines the penalty on the higher order moments of the error. In contrast to the minimum variance estimate, penalty on large deviations of the estimation error is possible     

Risk-averse two-stage stochastic programming with an application to disaster management

Traditional two-stage stochastic programming is risk-neutral; that is, it considers the expectation as the preference criterion while comparing the random variables (e.g., total cost) to identify the best decisions. However, in the presence of variability risk measures should be incorporated into decision making problems in order to model its effects. In this study, we consider a risk-averse two-stage stochastic programming model, where we specify the conditional-value-at-risk (CVaR) as the risk measure. We construct two decomposition algorithms based on the generic Benders-decomposition approach to solve such problems. Both single-cut and multicut versions of the proposed decomposition algorithms are presented. We adapt the concepts of the value of perfect information (VPI) and the value of the stochastic solution (VSS) for the proposed risk-averse two-stage stochastic programming framework and define two stochastic measures on the VPI and VSS. We apply the proposed model to disaster management, which is one of the research fields that can significantly benefit from risk-averse two-stage stochastic programming models. In particular, we consider the problem of determining the response facility locations and the inventory levels of the relief supplies at each facility in the presence of uncertainty in demand and the damage level of the disaster network. We present numerical results to discuss how incorporating a risk measure affects the optimal solutions and demonstrate the computational effectiveness of the proposed methods.     

Risk-sensitive control for a class of homing problems

A result of Lefebvre [Lefebvre, M. (2001). A different class of homing problems. Systems & Control Letters 42, 347-352] is here extended to a two-dimensional homing problem with a risk-sensitive cost criterion. It is shown that the optimal control is given explicitly, and moreover, the optimal value function has a simple probabilistic representation associated with a backward stochastic differential equation with a random terminal time.     

A convergent actor-critic-based FRL algorithm with application to power management of wireless transmitters

This paper provides the first convergence proof for fuzzy reinforcement learning (FRL) as well as experimental results supporting our analysis. We extend the work of Konda and Tsitsiklis, who presented a convergent actor-critic (AC) algorithm for a general parameterized actor. In our work we prove that a fuzzy rulebase actor satisfies the necessary conditions that guarantee the convergence of its parameters to a local optimum. Our fuzzy rulebase uses Takagi-Sugeno-Kang rules, Gaussian membership functions, and product inference. As an application domain, we chose a difficult task of power control in wireless transmitters, characterized by delayed rewards and a high degree of stochasticity. To the best of our knowledge, no reinforcement learning algorithms have been previously applied to this task. Our simulation results show that the ACFRL algorithm consistently converges in this domain to a locally optimal policy.     

一个风险敏感最优控制问题的随机最大值原理及在投资选择中的应用

In this paper,we mainly study a kind of risk-sensitive optimal control problem motivated by a kind of portfolio choice problem in certain financial market.Using the classical convex variational technique,we obtain the maximum principle for this kind of problem.The form of the maximum principle is similar to its risk-neutral counterpart.But the adjoint equation and the variational inequality heavily depend on the risk-sensitive parameterγ.This is one of the main difference from the risk-neutral case.We use this result to solve a kind of optimal portfolio choice problem.The optimal portfolio strategy obtained by the Bellman dynamic programming principle is a special case of our result when the investor only invests the home bond and the stock.Computational results and figures explicitly illustrate the relationships between the maximum expected utility and the parameters of the model.     

知识管理在化工企业中的应用

知识已成为企业重要的生产投入要素,知识管理能将企业战略、信息系统和企业核心能力知识有机结合起来,而成为新的研究热点。当前国际管理信息发展的三个趋势是：从信息管理走向知识管理,从信息资源开发走向知识资源开发,由C／S(Client／Server)结构走向B／S(Browser／Server)结构。企业实施知识管理是当前现代管理的发展方向。该文分析了化工企业实施知识管理的必要性,介绍了知识管理的基本概念,提出了知识管理系统的分层框架,以及企业实施知识管理的原则和步骤。     

流程挖掘在银行服务管理中的应用

随着银行服务信息化的不断发展,银行面临如何从大量的服务数据中提取有价值的信息用以提升服务效率的问题。在银行服务管理系统的实际应用中,由于其业务具有并发性事件多、日志数量大等特点,选择并行Apriori算法进行分析。与传统的Apriori算法相比,针对银行业务中并发性业务较多的特点,设计使用了并行Apriori算法,解决了单服务器运行效率随日志数量明显下降的弊端。银行服务管理系统每日会产生大量流程的日志数据,记录每一位参与员工的工作状态,通过调用并行Aporiori算法,挖掘服务流程日志中的关联规则,找出能够高效协作的员工组合。实验结果表明,将并行Apriori算法应用于服务流程日志的关联规则挖掘,使系统可以根据规则将协作关系紧密的员工分配在一起共同处理服务请求,提高了服务效率,取得了合理的应用效果,提高了银行服务管理系统中服务分配的智能。     

Risk-sensitive control and dynamic games for partially observeddiscrete-time nonlinear systems

Solves a finite-horizon partially observed risk-sensitive stochastic optimal control problem for discrete-time nonlinear systems and obtains small noise and small risk limits. The small noise limit is interpreted as a deterministic partially observed dynamic game, and new insights into the optimal solution of such game problems are obtained. Both the risk-sensitive stochastic control problem and the deterministic dynamic game problem are solved using information states, dynamic programming, and associated separated policies. A certainty equivalence principle is also discussed. The authors' results have implications for the nonlinear robust stabilization problem. The small risk limit is a standard partially observed risk-neutral stochastic optimal control problem     

A hierarchical tree layout algorithm with an application to corporate management in a change process

This work presents a hierarchical tree layout algorithm based on iterative rearrangement of subtrees. Using a greedy heuristic, all subtrees of a common parent are rearranged into a forest such that gaps between them are minimized. This heuristic is used to build a rearranged tree from bottom-up, starting with forests of the single leafs, and ending with the complete tree. Different cost measures for arrangement operations are discussed, which are based on the shape of a subtree. This shape can be characterized by the subtree’s leftmost and rightmost vertices, which determine how gapless this subtree can be combined with another one. The layout algorithm is used to display an organisational hierarchy. Such a hierarchical layout aids leadership when organisational structures are complex. In particular, it can be used to monitor the performance of organisational units undergoing change, e.g. restructuring. This improves the effectiveness of leadership instruments.     

Framework for application management with dynamic aspects J-EARS case study

The cost of computer system maintenance rises together with the increasing complexity of such systems. The use of an autonomic system architecture saves money by delegating some forms of maintenance to the systems themselves. The aim of this paper is to describe the results of creating a tool which introduces elements of adaptivity to Java applications using dynamic aspects. The impact of introducing aspects on the performance of various Application Servers is also discussed. Finally, benefits and problems arising from the use of the tool are presented, basing on sample use cases.     

Toward Web-based application management systems

As Web technology spreads, the number, variety, and sophistication of Web based information services is literally exploding. While some effort has been put into managing a single, centrally controlled Web site, current Web technologies offer little help for managing Web based applications in-the-large. This is partly due to the distributed, heterogeneous, and open nature of such applications. The paper proposes a generic framework for managing Web based applications which addresses both semantic and managerial issues. Semantic issues are addressed through the inclusion of a domain model component in the framework which describes the kinds of information that are available. Management issues are treated through a framework which includes formally defined notions for an information model, information base consistency, transactions, and concurrency control. Thus, the proposed management system provides a semantically robust environment for Web based information services while allowing for Web source independence     

Tools for distributed application management

The issues of managing distributed applications are discussed, and a set of tools, the meta system, that solves some longstanding problems is presented. The Meta model of a distributed application is described. To make the discussion concrete, it is shown how NuMon, a seismological analysis system for monitoring compliance with nuclear test-ban treaties is managed within the Meta framework. The three steps entailed in using Meta are described. First the programmer instruments the application and its environment with sensors and actuators. The programmer then describes the application structure using the object-oriented data modeling facilities of the authors' high-level control language, Lomita. Finally, the programmer writes a control program referencing the data model. Meta's performance and real-time behavior are examined