An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings.

Expert System for Decision-Making Problem in Economics

An expert system (ES) is a class of computer programs developed by researchers in artificial intelligence. In essence, they are programs made up of a set of rules that analyze information about a specific class of problems, as well as provide analysis of the problems, and, depending upon their design, recommend a course of user action in order to implement corrections. ES are computerized tools designed to enhance the quality and availability of knowledge required by decision makers in a wide range of industries. Decision-making is important for the financial institutions involved due to the high level of risk associated with wrong decisions. The process of making decision is complex and unstructured. The existing models for decision-making do not capture the learned knowledge well enough. In this study, we analyze the beneficial aspects of using ES for decision- making process.

Information Supply of Geo-information Systems for the Forecasting Problem of the Avalanche Danger

This article is dedicated to the vital problem of the creation of GIS-systems for the monitoring, prognostication and control of technogenic natural catastrophes. The decrease of risks, the protection of economic objects, averting the human victims, caused by the dynamism of avalanche centers, depends on the effectiveness of the prognostication procedures of avalanche danger used. In the article the structure of a prognostication subsystem information input is developed and the technology for the complex forecast of avalanche-prone situations is proposed.

Genetic Algorithm Approach for Solving the Task Assignment Problem

This research was partially supported by the Serbian Ministry of Science and Ecology under project 144007. The authors are grateful to Ivana Ljubić for help in testing and to Vladimir Filipović for useful suggestions and comments.

On a Five-Diagonal Jacobi Matrices and Orthogonal Polynomials on Rays in the Complex Plane

∗ Partially supported by Grant MM-428/94 of MESC.

Generalized Problem of Sratlikeness for Products of P-Valent Starlike Functions

∗ Partially supported by grant No. 433/94 NSF of the Ministry of Education and Science of the Republic of Bulgaria 1991 Mathematics Subject Classification:30C45

Randomized Parallelization – a New Method for Solving Hard Combinatorial Problems

A new method for solving some hard combinatorial optimization problems is suggested, admitting a certain reformulation. Considering such a problem, several different similar problems are prepared which have the same set of solutions. They are solved on computer in parallel until one of them will be solved, and that solution is accepted. Notwithstanding the evident overhead, the whole run-time could be significantly reduced due to dispersion of velocities of combinatorial search in regarded cases. The efficiency of this approach is investigated on the concrete problem of finding short solutions of non-deterministic system of linear logical equations.

About the Multi Criteria Ranging Problem in the Fuzzy Environment

Decision making and technical decision analysis demand computer-aided techniques and therefore more and more support by formal techniques. In recent years fuzzy decision analysis and related techniques gained importance as an efficient method for planning and optimization applications in fields like production planning, financial and economical modeling and forecasting or classification. It is also known, that the hierarchical modeling of the situation is one of the most popular modeling method. It is shown, how to use the fuzzy hierarchical model in complex with other methods of Multiple Criteria Decision Making. We propose a novel approach to overcome the inherent limitations of Hierarchical Methods by exploiting multiple criteria decision making.

Cauchy Problem for Differential Equation with Caputo Derivative

The paper is devoted to the study of the Cauchy problem for a nonlinear differential equation of complex order with the Caputo fractional derivative. The equivalence of this problem and a nonlinear Volterra integral equation in the space of continuously differentiable functions is established. On the basis of this result, the existence and uniqueness of the solution of the considered Cauchy problem is proved. The approximate-iterative method by Dzjadyk is used to obtain the approximate solution of this problem. Two numerical examples are given.

A Metaheuristic Approach to Solving the Generalized Vertex Cover Problem

AMS Subj. Classification: 90C27, 05C85, 90C59

Solving the Task Assignment Problem with a Variable Neighborhood Search

In this paper a variable neighborhood search (VNS) approach for the task assignment problem (TAP) is considered. An appropriate neighborhood scheme along with a shaking operator and local search procedure are constructed specifically for this problem. The computational results are presented for the instances from the literature, and compared to optimal solutions obtained by the CPLEX solver and heuristic solutions generated by the genetic algorithm. It can be seen that the proposed VNS approach reaches all optimal solutions in a quite short amount of computational time.

An Electromagnetism Metaheuristic for the Uncapacitated Multiple Allocation Hub Location Problem

In this article, the results achieved by applying an electromagnetism (EM) inspired metaheuristic to the uncapacitated multiple allocation hub location problem (UMAHLP) are discussed. An appropriate objective function which natively conform with the problem, 1-swap local search and scaling technique conduce to good overall performance.Computational tests demonstrate the reliability of this method, since the EM-inspired metaheuristic reaches all optimal/best known solutions for UMAHLP, except one, in a reasonable time.

On the Quotient Function Employed in the Blind Source Separation Problem

MSC 2010: 42C40, 94A12

Complex Hyperbolic Surfaces of Abelian Type

2000 Mathematics Subject Classification: 11G15, 11G18, 14H52, 14J25, 32L07.

A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem

This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8.