An agent-based hybrid intelligent system for foreign exchange trading

The foreign exchange (FX) market has many features including (1) Each trader’s payoff depends not only on his own behavior, but also on other traders’ decisions; (2) The number of traders is too large to make them all know the other dealers’ methods of decision making; and (3) The FX market has many levels. The FX market is complex because of these features. A diversity of techniques are required to deal with such complex problems. That is hybrid solutions are crucial for the FX market. On the other hand, research into the FX market has revealed that it demonstrates some characteristics of multi-agent systems such as autonomy, interaction, and emergence. To this end, an agent-based hybrid intelligent system was developed for FX trading, which is based on our proposed agent-based hybrid framework. This paper is to discuss the analysis, design, and implementation such a system. Some experimental results and comparisons with related works are also provided. The interest of this paper does not reside in improving the predictive capabilities of different FX models, but rather in how to integrate different models into one system under the unifying agent framework. The success of this system indicates that agent perspectives are very appropriate to model complex problems such as the FX trading.

Database access on the internet using mobile agent technology

Traditionally. accessing databases on the Internet is based on client/server paradigm (including web-based paradigm). However. there are some drawbacks when using the techniques. Mobile agent technology provides an excellent solution that can easily eliminate those disadvantages. By conducting a series of experiments and analyzing the experimental results, this paper presents in which circumstance mobile agents can outperform traditional client/server paradigm, and how they can overcome the shortcomings in client/server paradigm in database access on the Internet.

Building agent-based hybrid intelligent systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.

Applying a reconfigurable multi-agent scheduler to product distribution

More intelligent scheduling methods are required for manufacturing scheduling due to the move to more agile systems. Multi-agent methods are one such approach. This paper describes the application of a reconfigurable multi-agent scheduler to the problem of allocating orders to warehouses in a distribution supply chain. This multi-agent system was originally developed for allocation of orders to machines in a highly reconfigurable manufacturing system and this work was aimed at investigating the ease of applying this same scheduler to other problems. It was found that this new application was readily achieved because of the modular structure of the scheduler. This paper shows how the application to the new problem was achieved.

Supporting adaptive learning in hypertext environment : an agent based approach

One problem for hypertext-based learning application is to control learning paths for different learning activities. This paper first introduces related concepts of hypertext learning state space, then proposes an agent based approach used to provide kinds of adaptation for learning activities. Examples are given while explaining ways to realizing adaptive instructions. Possible future directions are also discussed at the end of this paper.

Robust parallel filtering for mobile agent tracking

In this paper we develop a robust method of target/mobile agent tracking involving two independent estimators with separate measurement systems. The outputs of the two estimators are combined using simple trigonometry (post-estimation data fusion) and provide a robust and reliable tracking path. We demonstrate that through the use of recent advances in robust set-value state estimation, our robust parallel filter approach performs well even when the individual filters do not. Brief comparisons with common data fusion methods are conducted in order to demonstrate the advantages of our parallel (post-estimation fusion) approach

Artificial formation forces for stable aggregation of multi-agent system

This paper introduces, a robust and stable algorithm based on artificial formation forces, for multi-agent system (MAS) aggregation in 2D space. The MAS model with artificial forces; consists of inter-member collision avoidance element, formation generation element and a velocity based damping element; is analysed for stability and convergence. Computer simulations are used to illustrate stability and convergence, and to demonstrate effectiveness of the algorithm.

Observer-based control of multi-agent systems under decentralized information structure

This paper addresses the problem of decentralized implementation of a global state feedback controller for multi-agent systems. The system is assumed to be under the constraint of a complete decentralized information structure. The decentralization of the control task is achieved through the construction of low-order decentralized functional observers with the purpose of generating the required corresponding control signal for each local control station. A design procedure is developed for obtaining an approximate solution to the design of the observers. Stability analysis is provided for the global system using the proposed observer-based approach. A numerical example is given to illustrate the design procedure and cases when the observers' order increases from the lowest value.

Building agent-based hybrid intelligent systems : a case study

Many complex problems (e.g., financial investment planning, foreign exchange trading, data mining from large/multiple databases) require hybrid intelligent systems that integrate many intelligent techniques (e.g., fuzzy logic, neural networks, and genetic algorithms). However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. Thus, this paper discusses the development of an agent-based hybrid intelligent system for financial investment planning, in which a great number of heterogeneous computing techniques/packages are easily integrated into a unifying agent framework. This shows that agent technology can indeed facilitate the development of hybrid intelligent systems.

SEPS1 protects RAW264.7 cells from pharmacological ER stress agent-induced apoptosis

Selenoprotein S (SEPS1) is a novel endoplasmic reticulum (ER) resident protein and it is known to play an important role in production of inflammatory cytokines. Here, we show evidence that SEPS1 is stimulated by pharmacological ER stress agents in RAW264.7 macrophages as well as other cell types. Overexpression studies reveal a protective action of SEPS1 in macrophages against ER stress-induced cytotoxicity and apoptosis, resulting in promoting cell survival during ER stress. The protective action of SEPS1 is largely dependent on ER stress-mediated cell death signal with less effect on non-ER stress component cell death signals. Conversely, suppression of SEPS1 in macrophages results in sensitization of cells to ER stress-induced cell death. These findings suggest that SEPS1 could be a new ER stress-dependent survival factor that protects macrophage against ER stress-induced cellular dysfunction.

Smart cluster bombs - control of multi-agent systems for military applications

This paper introduces an aggregation algorithm for airborne swarming guided weapon systems, which can aggregate munitions into a given shape while reaching the surface. The algorithm uses an artificial force based controller to navigate the members of the swarm into the desired geographical position and evenly distribute them inside the shape. Inter-member repulsion forces are used to avoid collisions among members, which is crucial for a weapon deployment system. Moreover, a lower bound for the release height was obtained which guarantee convergence of the complete weapon system into the target area. The proposed swarming guided weapon system was tested using computer simulations.

Secure mobile agent-based e-negotiation for on-line trading

Electronic negotiation (e-negotiation) is a major activity in e-Commerce applications. Agent-based e-negotiation has recently received increasing attention. However, agent-based electronic negotiation suffers from a number of security attacks. In this paper, we present a mobile agent-based e-commerce framework. We also propose a security protocol that protects the information exchanged between the mobile agents during e- negotiations. We reason the correctness of the proposed security protocol in the presence of various security threats. The reasoning shows that the protocol maintains privacy, non- repudiation, authenticity, anonymity, and strong integrity of exchanged information.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.