An agent-based hybrid intelligent system for financial investment planning

Many complex problems including financial investment planning 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 due to complicated interactions and technique incompatibilities. This paper describes a hybrid intelligent system for financial investment planning that was built from agent points of view. This system currently consists of 13 different agents. The experimental results show that all agents in the system can work cooperatively to provide reasonable investment advice. The system is very flexible and robust. The success of the system indicates that agent technologies can significantly facilitate the construction of hybrid intelligent systems.

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.

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.

A loop-shaping approach to intelligent control

In this paper, we propose a loop-shaping approach to in telligent control with dynamically constructed neurocon troller. In the proposed control scheme, the process uncer tainly is reduced in the controller rather than in the process, without explicit identification of the process under control. The inherent noise/distrurbances in the process are utilized to satisfy persistency of excitation condition. The use of a reference model in form of a filter allow the frequency response of the closed-loop to be adapted in line with the changes in frequency response of the filter. The approach is evaluated on the example of control of polymerization reactor with promising results.

Intelligent headrest

In this paper we discuss the design and development of a novel intelligent headrest system. Developed to reduce neck injuries resulting from up to 63% of rear end accidents, this system uses inductive sensing technology to establish the position of a driver or passenger's skull in a vehicle. Once detected, the system autonomously places the vehicle's headrest in a position that best support an occupant's head in the case of an accident. Sensor construction, mechatronic design and controller selection and real world tests of the system under various conditions are covered.

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.

Intelligent financial news digest system

We present an agent-based system Intelligent Financial News Digest System (IFNDS) for analyzing online financial news articles and associated material. The system can abstract, synthesize, digest, and classify the contents, and assesses whether the report is favorable to any company discussed in the reports. It integrates artificial intelligence technologies including traditional information retrieval and extraction techniques for the news analysis. It makes use of keyword statistics and backpropagation training data to identify companies named in reportage whether it is, evaluatively speaking, positive, negative or neutral. The system would be of use to media such as clipping services, media management, advertising, public relations, public interest, and e-commerce professionals and government non-governmental bodies interested in monitoring the media profiles of corporations, products, and issues.

Making decisions for intelligent agents in procedure descriptive framework

There are many existing and potential industrial and commercial applications for intelligent agents in the literature. The very challenging issue for these applications is to "do what intelligent agents should do". The BDI (beliefs, desires, intentions) architecture is presented to solve this challenge, and it has attracted much attention. The main difficulty for this architecture is the formalization problem. In this paper, we discuss the "procedure descriptive framework", which is a method for formalizing the BDI architecture. We present decision models for intelligent agents in this framework. This research provides a technique for online agents to form beliefs and make decisions according to the current uncertain environment

An intelligent forex monitoring system

The need for intelligent monitoring systems has become a necessity to keep track of the complex forex market. The vast currency market is a foreign concept to the average individual. However, once it is broken down into simple terms, the average individual can begin to understand the foreign exchange market and use it as a financial instrument for future investing. We attempt to compare the performance of a Takagi-Sugeno, type neuro-fuzzy system and a feedforward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates. We considered the exchange values of Australian dollar with respect to US dollar, Singapore dollar, New Zealand dollar, Japanese yen and United Kingdom pounds. The connectionist models were trained using 70% of the data and remaining was used for testing and validation purposes. It is observed that the proposed connectionist models were able to predict the average forex rates one month ahead accurately. Experiment results also reveal that the neuro-fuzzy technique performed better than the neural network

An agent-based intelligent system for information gathering from world wide web environment

To use the vast amount of information efficiently and effectively from Web sites is very important for making informed decisions. There are, however, still many problems that need to be overcome in the information gathering research arena to enable the delivery of relevant information required by users. In this paper, an information gathering system is develop by means of multiple agents to solve those problems. We employed some ideas of Gaia's methodology and an open agent architecture to analyze and design the system. The system consists of a query preprocessing agent, information retrieval agent, information filtering agent, and information management agent. The filtering agent is trained with categorized documents and can provide users with the necessary information. The experimental results show that all agents in the system can work cooperatively to retrieve relevant information from the World Wide Web environment.

Decision model for BDI-based intelligent agents

The very challenging issue for intelligent agents is “do what they should do”. The BDI architecture is presented to solve this challenge. The main difficult for this architecture is the formalizing problem. In this paper, we discuss the procedure descriptive framework, which presents a method for formalizing BDI architecture. We a present decision model for intelligent agents in this procedure descriptive framework. This research shows how an agent to generate beliefs and make decisions by using its current beliefs.

A proposed hybrid evaluation methodology for intelligent transport system deployment

The rapid deployment of intelligent transport systems in practice requires serious evaluation of new construction projects. Traditional transportation evaluation methods are insufficient to capture the new features of intelligent transport systems, particularly the applications of recently developed information technologies. This research aims to develop a new conceptual framework to evaluate the social, economic and environmental impacts of intelligent transport systems deployment, with explicit consideration of impacts of information technologies on variant travel behaviours. The research would provide a systematic evaluation methodology to enable decision makers and publics better evaluating and understanding the social, economic and environmental benefits of implementation of intelligent transport systems.