Intelligent agents enabling negotiated control of pervasive environments

One of the important goals of the intelligent buildings especially in commercial applications is not only to minimize the energy consumption but also to enhance the occupant’s comfort. However, most of current development in the intelligent buildings focuses on an implementation of the automatic building control systems that can support energy efficiency approach. The consideration of occupants’ preferences is not adequate. To improve occupant’s wellbeing and energy efficiency in intelligent environments, we develop four types of agent combined together to form a multi-agent system to control the intelligent buildings. Users’ preferential conflicts are discussed. Furthermore, a negotiation mechanism for conflict resolution, has been proposed in order to reach an agreement, and has been represented in syntax directed translation schemes for future implementation and testing. Keywords: conflict resolution, intelligent buildings, multi-agent systems (MAS), negotiation strategy, syntax directed translation schemes (SDTS).

B-spline neural networks based PID controller for Hammerstein systems

A new PID tuning and controller approach is introduced for Hammerstein systems based on input/output data. A B-spline neural network is used to model the nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacobians matrix are calculated using the De Boor algorithms including both the functional and derivative recursions. A numerical example is utilized to demonstrate the efficacy of the proposed approaches.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Intelligent agent technology within information warfare

Research into Intelligent Agent (IA) technology and how it can assist computer systems in the autonomous completion of common office and home computing tasks is extremely widespread. The use oflA's is becoming more feasible as the functionality moves Into line with what users require for their everyday computing needs. However, this does not mean that IA technology cannot be exploited or developed for use in a malicious manner, such as within an Information Waifare (IW) scenario, where systems may be attacked autonomously by agent system implem-entations. This paper will discuss tne cilrrenlStcite Ofmalicious use of lA's as well as focusing on attack techniques, the difficulties brought about by such attacks as well as security methods, both proactive and reactive, that could be instated within compromised or sensitive systems.

Provenance in Agent-mediated Healthcare Systems

Agent-oriented cooperation techniques and standardized electronic healthcare record exchange protocols can be used to combine information regarding different facets of a therapy received by a patient from different healthcare providers at different locations. Provenance is an innovative approach to trace events in complex distributed processes, dependencies between such events, and associated decisions by human actors. We focus on three aspects of provenance in agent-mediated healthcare systems: first, we define the provenance concept and show how it can be applied to agent-mediated healthcare applications; second, we investigate and provide a method for independent and autonomous healthcare agents to document the processes they are involved in without directly interacting with each other; and third, we show that this method solves the privacy issues of provenance in agent-mediated healthcare systems.

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.

Positivism: one ring to rule them all? A tale of information systems research methodology

The tale of research methodology in information systems is told through the fantasy of Tolkien’s Lord of the Rings. The tale is intended to be at once a piece of light hearted fun in its placement of the struggles of research methodology as an epic story but, in the tradition of the court jester, attempts to provide a new perspective on Information Systems (IS) research methodology and our struggles with positivism in particular. Our tale is one of developing a greater maturity and confidence in IS methodology and introduces postmodern methodologies to Information Systems. Our tale, our pastiche, is itself postmodern.

Feedback credibility issues in trust management systems

The following topics are dealt with: soft computing in intelligent multimedia; grid and pervasive computing security; interactive multimedia & intelligent services in mobile and ubiquitous computing; data management in ubiquitous computing; smart living space; software effectiveness and efficiency.

Analysis of multi-cluster computing systems with processor heterogeneity

This paper addresses the problem of performance modeling of heterogeneous multi-cluster computing systems. We present an analytical model that can be employed to explore the effectiveness of different design approaches so that one can have an intelligent choice during design and evaluation of a cost effective large-scale heterogeneous distributed computing system. The proposed model considers stochastic quantities as well as processor heterogeneity of the target system. The analysis is based on a parametric fat-tree network, the m-port n-tree, and a deterministic routing algorithm. The correctness of the proposed model is validated through comprehensive simulation of different types of clusters.

Detecting and tracing DDoS attacks by intelligent decision prototype

Over the last couple of months a large number of distributed denial of service (DDoS) attacks have occurred across the world, especially targeting those who provide Web services. IP traceback, a counter measure against DDoS, is the ability to trace IP packets back to the true source/s of the attack. In this paper, an IP traceback scheme using a machine learning technique called intelligent decision prototype (IDP), is proposed. IDP can be used on both probabilistic packet marking (PPM) and deterministic packet marking (DPM) traceback schemes to identify DDoS attacks. This will greatly reduce the packets that are marked and in effect make the system more efficient and effective at tracing the source of an attack compared with other methods. IDP can be applied to many security systems such as data mining, forensic analysis, intrusion detection systems (IDS) and DDoS defense systems.

A low-cost intelligent gas sensing device for military applications

The field of electronic noses and gas sensing has been developing rapidly since the introduction of the silicon based sensors. There are numerous systems that can detect and indicate the level of a specific gas. We introduce here a system that is low power, small and cheap enough to be used in mobile robotic platforms while still being accurate and reliable enough for confident use. The design is based around a small circuit board mounted in a plastic case with holes to allow the sensors to protrude through the top and allow the natural flow of gas evenly across them. The main control board consists of a microcontroller PCB with surface mount components for low cost and power consumption. The firmware of the device is based on an algorithm that uses an Artificial Neural Network (ANN) which receives input from an array of gas sensors. The various sensors feeding the ANN allow the microcontroller to determine the gas type and quantity. The Testing of the device involves the training of the ANN with a number of different target gases to determine the weightings for the ANN. Accuracy and reliability of the ANN is validated through testing in a specific gas filled environment.

Modeling intelligent agents for web-based information gathering

The recent emergence of intelligent agent technology and advances in information gathering have been the important steps forward in efficiently managing and using the vast amount of information now available on the Web to make 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 end users. Good decisions cannot be made without sufficient, timely, and correct information. Traditionally it is said that knowledge is power, however, nowadays sufficient, timely, and correct information is power. So gathering relevant information to meet user information needs is the crucial step for making good decisions. The ideal goal of information gathering is to obtain only the information that users need (no more and no less). However, the volume of information available, diversity formats of information, uncertainties of information, and distributed locations of information (e.g. World Wide Web) hinder the process of gathering the right information to meet the user needs. Specifically, two fundamental issues in regard to efficiency of information gathering are mismatch and overload. The mismatch means some information that meets user needs has not been gathered (or missed out), whereas, the overload means some gathered information is not what users need. Traditional information retrieval has been developed well in the past twenty years. The introduction of the Web has changed people's perceptions of information retrieval. Usually, the task of information retrieval is considered to have the function of leading the user to those documents that are relevant to his/her information needs. The similar function in information retrieval is to filter out the irrelevant documents (or called information filtering). Research into traditional information retrieval has provided many retrieval models and techniques to represent documents and queries. Nowadays, information is becoming highly distributed, and increasingly difficult to gather. On the other hand, people have found a lot of uncertainties that are contained in the user information needs. These motivate the need for research in agent-based information gathering. Agent-based information systems arise at this moment. In these kinds of systems, intelligent agents will get commitments from their users and act on the users behalf to gather the required information. They can easily retrieve the relevant information from highly distributed uncertain environments because of their merits of intelligent, autonomy and distribution. The current research for agent-based information gathering systems is divided into single agent gathering systems, and multi-agent gathering systems. In both research areas, there are still open problems to be solved so that agent-based information gathering systems can retrieve the uncertain information more effectively from the highly distributed environments. The aim of this thesis is to research the theoretical framework for intelligent agents to gather information from the Web. This research integrates the areas of information retrieval and intelligent agents. The specific research areas in this thesis are the development of an information filtering model for single agent systems, and the development of a dynamic belief model for information fusion for multi-agent systems. The research results are also supported by the construction of real information gathering agents (e.g., Job Agent) for the Internet to help users to gather useful information stored in Web sites. In such a framework, information gathering agents have abilities to describe (or learn) the user information needs, and act like users to retrieve, filter, and/or fuse the information. A rough set based information filtering model is developed to address the problem of overload. The new approach allows users to describe their information needs on user concept spaces rather than on document spaces, and it views a user information need as a rough set over the document space. The rough set decision theory is used to classify new documents into three regions: positive region, boundary region, and negative region. Two experiments are presented to verify this model, and it shows that the rough set based model provides an efficient approach to the overload problem. In this research, a dynamic belief model for information fusion in multi-agent environments is also developed. This model has a polynomial time complexity, and it has been proven that the fusion results are belief (mass) functions. By using this model, a collection fusion algorithm for information gathering agents is presented. The difficult problem for this research is the case where collections may be used by more than one agent. This algorithm, however, uses the technique of cooperation between agents, and provides a solution for this difficult problem in distributed information retrieval systems. This thesis presents the solutions to the theoretical problems in agent-based information gathering systems, including information filtering models, agent belief modeling, and collection fusions. It also presents solutions to some of the technical problems in agent-based information systems, such as document classification, the architecture for agent-based information gathering systems, and the decision in multiple agent environments. Such kinds of information gathering agents will gather relevant information from highly distributed uncertain environments.